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Hadoop MapReduce

#hadoop, #tutorial, #beginners, #overview, #big #data #overview, #big #bata #solutions, #introduction #to #hadoop, #enviornment #setup, #hdfs #overview, #hdfs #operations, #command #reference, #mapreduce, #streaming, #multi #node #cluster.


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Hadoop – MapReduce

MapReduce is a framework using which we can write applications to process huge amounts of data, in parallel, on large clusters of commodity hardware in a reliable manner.

What is MapReduce?

MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce. Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Secondly, reduce task, which takes the output from a map as an input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce task is always performed after the map job.

The major advantage of MapReduce is that it is easy to scale data processing over multiple computing nodes. Under the MapReduce model, the data processing primitives are called mappers and reducers. Decomposing a data processing application into mappers and reducers is sometimes nontrivial. But, once we write an application in the MapReduce form, scaling the application to run over hundreds, thousands, or even tens of thousands of machines in a cluster is merely a configuration change. This simple scalability is what has attracted many programmers to use the MapReduce model.

The Algorithm

Generally MapReduce paradigm is based on sending the computer to where the data resides!

MapReduce program executes in three stages, namely map stage, shuffle stage, and reduce stage.

Map stage. The map or mapper’s job is to process the input data. Generally the input data is in the form of file or directory and is stored in the Hadoop file system (HDFS). The input file is passed to the mapper function line by line. The mapper processes the data and creates several small chunks of data.

Reduce stage. This stage is the combination of the Shuffle stage and the Reduce stage. The Reducer’s job is to process the data that comes from the mapper. After processing, it produces a new set of output, which will be stored in the HDFS.

During a MapReduce job, Hadoop sends the Map and Reduce tasks to the appropriate servers in the cluster.

The framework manages all the details of data-passing such as issuing tasks, verifying task completion, and copying data around the cluster between the nodes.

Most of the computing takes place on nodes with data on local disks that reduces the network traffic.

After completion of the given tasks, the cluster collects and reduces the data to form an appropriate result, and sends it back to the Hadoop server.

Inputs and Outputs (Java Perspective)

The MapReduce framework operates on key, value pairs, that is, the framework views the input to the job as a set of key, value pairs and produces a set of key, value pairs as the output of the job, conceivably of different types.

The key and the value classes should be in serialized manner by the framework and hence, need to implement the Writable interface. Additionally, the key classes have to implement the Writable-Comparable interface to facilitate sorting by the framework. Input and Output types of a MapReduce job: (Input) k1, v1 – map – k2, v2 – reduce – k3, v3 (Output).

If the above data is given as input, we have to write applications to process it and produce results such as finding the year of maximum usage, year of minimum usage, and so on. This is a walkover for the programmers with finite number of records. They will simply write the logic to produce the required output, and pass the data to the application written.

But, think of the data representing the electrical consumption of all the largescale industries of a particular state, since its formation.

When we write applications to process such bulk data,

  • They will take a lot of time to execute.
  • There will be a heavy network traffic when we move data from source to network server and so on.

To solve these problems, we have the MapReduce framework.

Input Data

The above data is saved as sample.txt and given as input. The input file looks as shown below.

Example Program

Given below is the program to the sample data using MapReduce framework.

Save the above program as ProcessUnits.java. The compilation and execution of the program is explained below.

Compilation and Execution of Process Units Program

Let us assume we are in the home directory of a Hadoop user (e.g. /home/hadoop).

Follow the steps given below to compile and execute the above program.

Step 1

The following command is to create a directory to store the compiled java classes.

Step 2

Download Hadoop-core-1.2.1.jar, which is used to compile and execute the MapReduce program. Visit the following link http://mvnrepository.com/artifact/org.apache.hadoop/hadoop-core/1.2.1 to download the jar. Let us assume the downloaded folder is /home/hadoop/.

Step 3

The following commands are used for compiling the ProcessUnits.java program and creating a jar for the program.

Step 4

The following command is used to create an input directory in HDFS.

Step 5

The following command is used to copy the input file named sample.txt in the input directory of HDFS.

Step 6

The following command is used to verify the files in the input directory.

Step 7

The following command is used to run the Eleunit_max application by taking the input files from the input directory.

Wait for a while until the file is executed. After execution, as shown below, the output will contain the number of input splits, the number of Map tasks, the number of reducer tasks, etc.

Step 8

The following command is used to verify the resultant files in the output folder.

Step 9

The following command is used to see the output in Part-00000 file. This file is generated by HDFS.

Below is the output generated by the MapReduce program.

Step 10

The following command is used to copy the output folder from HDFS to the local file system for analyzing.

Important Commands

All Hadoop commands are invoked by the $HADOOP_HOME/bin/hadoop command. Running the Hadoop script without any arguments prints the description for all commands.

Usage. hadoop [–config confdir] COMMAND

The following table lists the options available and their description.


Donate cars ny

#bbbs,big #brother,big #sister,charity,mentor,mentors #for #children,big #brothers #big #sisters,big #brothers,big #sisters,ct #charity,connecticut #charity


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Donate

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Giving to Big Brothers Big Sisters of Southwestern Connecticut

Funding for the programs of Big Brothers Big Sisters comes from foundations, corporations, United Way and special events, such as Bowl for Kids’ Sake and our annual Golf Tournament. In addition, we rely on donations from individuals who endorse our mentoring mission.

Support from individuals like you is vital to provide mentors for children in your community. And we know they need them. It isn’t easy being a kid these days. In these complex and confusing times, children need a caring adult to be a mentor and a friend — someone they can talk to, have fun with, and learn from.

Big Brothers Big Sisters of Southwestern Connecticut is a 501(c) 3, non-profit agency recognized by the IRS and the State of CT. All charitable gifts made to Big Brothers Big Sisters of Southwestern Connecticut are tax-deductible to the fullest extent allowed by law.

Your support in any amount is greatly appreciated (and needed). Each Big-Little match comes at an approximate annual cost to the agency of $1,000. In order to continue to serve the kids already enrolled in our programs, while trying to match those on our waiting list, we need your help.

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Not only will you be helping Big Brothers Big Sisters by making a tax deductible donation, but your recycling efforts will be helping the environment. Win – win!

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Whether your car starts or not, donating it can certainly start something in your community. Big Brothers Big Sisters Cars for Kids’ Sake helps us raise funds through the donation of your unwanted vehicle. Donated vehicles qualify as charitable gifts and may be eligible for a tax deduction.

We welcome all types of vehicles, including cars, trucks, SUVs, motor homes, boats, airplanes, farm equipment, and construction equipment.

Shop Online Through iGive

Support Big Brothers Big Sisters of Southwestern Connecticut just by shopping online for brand-name stuff at over 600 well-known online stores. Get free deals and coupons just by being an iGive.com member. Up to 26% of EACH purchase gets donated! If you join iGive.com and make a purchase within 45 days from one of the participating stores, iGive.com will donate an extra $5 FREE to Big Brothers Big Sisters of Southwestern Connecticut!

How It Works

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Ten big data case studies in a nutshell

#big #data #public #companies


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Ten big data case studies in a nutshell

You haven’t seen big data in action until you’ve seen Gartner analyst Doug Laney present 55 examples of big data case studies in 55 minutes. It’s kind of like The Complete Works of Shakespeare. Laney joked at Gartner Symposium. though less entertaining and hopefully more informative. (Well, maybe, for this tech crowd.) The presentation was, without question, a master class on the three Vs definition of big data: Data characterized by increasing variety. velocity and volume. It’s a description, by the way, that Laney — who also coined the term infonomics — floated way back in 2001 .

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The 55 examples are not intended to intimidate, but instruct. Laney told the audience not to feel overwhelmed, but to home in on the big data case studies that might improve business performance at their own companies: Yes, I know you’re in industry x. but there are tremendous ideas that come from other industries that you need to consider adapting and adopting for your own industry, he said.

Here are 10 of them:

1. Macy’s Inc.and real-time pricing. The retailer adjusts pricing in near-real time for 73 million (!) items, based on demand and inventory, using technology from SAS Institute .

2. Tipp24 AG, a platform for placing bets on European lotteries, and prediction. The company uses KXEN software to analyze billions of transactions and hundreds of customer attributes, and to develop predictive models that target customers and personalize marketing messages on the fly. That led to a 90% decrease in the time it took to build predictive models. SAP is in the process of acquiring KXEN. That’s probably a great move by SAP to fill a predictive analytics gap they’ve long had, Laney said.

3. Wal-Mart Stores Inc.and search. The mega-retailer’s latest search engine for Walmart.com includes semantic data. Polaris. a platform that was designed in-house, relies on text analysis, machine learning and even synonym mining to produce relevant search results. Wal-Mart says adding semantic search has improved online shoppers completing a purchase by 10% to 15%. In Wal-Mart terms, that is billions of dollars, Laney said.

4.Fast foodand video. This company (Laney wasn’t giving up who) is training cameras on drive-through lanes to determine what to display on its digital menu board. When the lines are longer, the menu features products that can be served up quickly; when the lines are shorter, the menu features higher-margin items that take longer to prepare.

5. Morton’s The Steakhouseand brand recognition. When a customer jokingly tweeted the Chicago-based steakhouse chain and requested that dinner be sent to the Newark airport, where he would be getting in late after a long day of work, Morton’s became a player in a social media stunt heard ’round the Interwebs. The steakhouse saw the tweet, discovered he was a frequent customer (and frequent tweeter), pulled data on what he typically ordered, figured out which flight he was on, and then sent a tuxedo-clad delivery person to serve him his dinner. Sure, the whole thing was a publicity stunt (that went viral), but that’s not the point. The question businesses should be asking themselves: Is your company even capable of something like this? Laney said.

6.PredPol Inc.and repurposing. The Los Angeles and Santa Cruz police departments. a team of educators and a company called PredPol have taken an algorithm used to predict earthquakes, tweaked it and started feeding it crime data. The software can predict where crimes are likely to occur down to 500 square feet. In LA, there’s been a 33% reduction in burglaries and 21% reduction in violent crimes in areas where the software is being used.

Previously on The Data Mill

MetLife fires up Synapse and JSON to recruit rock-star developers

The state of the digital enterprise at Gartner Symposium

7. Tesco PLCand performance efficiency. The supermarket chain collected 70 million refrigerator-related data points coming off its units and fed them into a dedicated data warehouse. Those data points were analyzed to keep better tabs on performance, gauge when the machines might need to be serviced and do more proactive maintenance to cut down on energy costs.

8.American Express Co.and business intelligence. Hindsight reporting and trailing indicators can only take a business so far, AmEx realized. Traditional BI [business intelligence] hindsight-oriented reporting and trailing indicators aren’t moving the needle on the business, Laney said. So AmEx started looking for indicators that could really predict loyalty and developed sophisticated predictive models to analyze historical transactions and 115 variables to forecast potential churn. The company believes it can now identify 24% of Australian accounts that will close within the next four months.

9. Express Scripts Holding Co.and product generation. Express Scripts, which processes pharmaceutical claims, realized that those who most need to take their medications were also those most likely to forget to take their medications. So they created a new product: Beeping medicine caps and automated phone calls reminding patients it’s time to take the next dose.

10. InfinityProperty Casualty Corp. anddark data . Laney defines dark data as underutilized information assets that have been collected for single purpose and then archived. But given the right circumstances, that data can be mined for other reasons. Infinity, for example, realized it had years of adjusters’ reports that could be analyzed and correlated to instances of fraud. It built an algorithm out of that project and used the data to reap $12 million in subrogation recoveries.

This was last published in October 2013


How to Pay Off a Big Student Loan #mortgage #loan


#fast student loans
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This Millennial Paid Off $23,375 in Student Loans in Just 10 Months

“If you have a game plan, you can accomplish your goals,” says 22-year-old Jordan Arnold.

Like many millennials, Jordan Arnold graduated from college five figures deep in student debt. Unlike most of his peers, he paid off all of his loans less than a year after graduation.

Bluffton, Ind.

When he started paying it down: May 2013

When he became debt-free: March 2014

How I started building debt

I always knew I was going to go to college, though I figured I d go to community college for a year or two because it s cheap. But my parents started talking to me about this private Christian school, Indiana Wesleyan in Marion, Ind. I took a visit, and I really liked it. It s only like 3,000 students on campus, so it s a tight-knit community.

Tuition and room and board was about $31,000 a year. And the first year I hadn t applied for federal student aid, since I didn t commit to the college until about 10 days before classes started. I got some scholarships and a grant from my church, though. So, ultimately, I owed approximately $9,000 that first year.

Getting to $23,000

I could only borrow up to $5,500 in subsidized loans from the government each year, so I worked to cover the rest so that I didn t have to take out private loans. I also graduated in three years, which helped.

Still, altogether, I had to take out $15,150 in subsidized federal loans and $2,000 in unsubsidized federal loans. I borrowed another $6,000 from my parents.

My uh-oh moment

In the fall semester of my senior year, I remember being kind of nervous. I knew I had to start paying my debt within six months. It s stressful, when you don t have any money. And I heard all these stories about college students who get out of school, they have all this debt, and they can t find jobs.

Getting my debts paid off was important to me. I didn t want to get the point where I d have to be paying student loans for another 10 years. Right now, I m single. I don t have any dependents that rely on my income. But I didn t want to have these loans over my head when I m trying to feed a family and put a roof over their heads. It s not just about me, it s about my future family.

My first step out of the hole

Luckily, I got a job right out of college at an insurance agency (I had majored in finance). I was on salary, and it was pretty good: $36,000 plus bonuses.

I didn t have to pay my student loans for another four months, but over the summer I decided to go ahead and start making payments before interest began accruing.

I actually moved back in with my parents which is hard when you have been out on your own. But I didn t really have a reason to move out. And I was blessed that they actually preferred me to live there because I could help out around the farm they own, baling hay or feeding the horses. Living at my parents place for free was a lot better than having to pay $400 or $500 a month for rent.

Kicking it into gear

About four months into my new job, I picked up a second job, delivering for Pizza Hut, to help pay off my debt. I would start work at the insurance agency at 8:30 a.m. change in the bathroom at 4:50 p.m. get to Pizza Hut by 5, deliver pizzas until about 9:30, get home around 10, then shower, eat, and go to bed.

My monthly take-home pay from the insurance company was about $2,200, and I made about $1,000 at Pizza Hut. After gas, car insurance, tithing to my church, entertainment and food, I could put about $2,000 towards my debt every month.

At that rate, I was projected to pay off my debt in May 2014. But I got a $3,000 refund on my taxes, and paid off the rest of my debt with that.

How I celebrated being debt-free

I made my last payment the first of March, then I went to Florida with some friends two weeks later. It was pretty rewarding after a 10-month battle. I had probably worked 65 to 70 hours a week for seven or eight months. It was exhausting, but it was worth it.

What I d tell someone else in my place

If you have a game plan, you can accomplish your goals. I have an account on Mint.com, that s where I kept my budget. That s a big part of it just seeing your progress and knowing you re getting closer.

Also, have an emergency fund. While I was paying off that debt, I had a small car accident. I was delivering a pizza, and I hit something in someone s driveway. It cost me about $760 to fix the car. But I had a $1,000 emergency fund, which was kind of a buffer that I kept because life happens.

Finally, don t be afraid to move home if you have to. That was a big part of how I got out of debt.

My plan for the future

I quit my Pizza Hut job in April after paying off my debt, and now work at a bank analyzing commercial and agricultural loans, which is more in line with what I wanted to do.

I actually haven t moved out of my parents house yet. Instead I m saving up for a down payment on a house. I m putting away 50% of my take-home income for that, and I should have a down payment by mid-summer. I also started investing. I started a Roth IRA, and I plan to max it out this year.

Staying true to myself

Some people have made the argument, Maybe you shouldn t have paid off the debt so fast because the interest rate is cheaper than what it will be for you to borrow for a home.

That makes sense in my head, but in my heart, I didn t want this hanging over me. I want to be responsible with my money and build a strong foundation.

Check out Money 101 for more resources:

  • I am unable to pay my debts. What can I do?
  • How do I get rid of my credit card debt?
  • How can I improve my credit score?
  • How do I set a budget I can stick to?


Mom-And-Pop Loan Sharks Being Driven Out By Big Credit-Card Companies – The Onion – America s Finest News Source #same #day #loans


#loan shark
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Mom-And-Pop Loan Sharks Being Driven Out By Big Credit-Card Companies

PHILADELPHIA Frankie “The Gorilla” Pistone leans wistfully on his bat. Then, without warning, he picks it up, swinging it furiously toward his deadbeat client’s leg. Just before the Louisville Slugger makes contact with the man’s kneecap, he pulls back, as only a real pro can, leaving the $250-in-the-hole man gasping in fear and relief. “Just get it to me by tomorrow, because next time, I ain’t gonna let up,” Pistone says.

Loan shark Frankie Pistone, whose way of life is endangered by the likes of American Express.

As the thankful man scurries off, Pistone pulls the cigarette out of his mouth and drops it to the ground. “I’m going to miss this,” he says.

Frank Pistone is part of the dying breed known as the American Loan Shark. Not so long ago, the loan shark flourished, offering short-term, high-interest loans to desperate people with nowhere else to turn. Today, however, Pistone and countless others like him are being squeezed out by the major credit-card companies, which can offer money to the down-and-out at lower rates of interest and without the threat of bodily harm.

“It’s a damn shame,” said Joseph Stasi, 61, a South Philadelphia loan shark whose business is down 90 percent from its mid-’70s heyday. “These days, there’s just no place for the small businessman. My kind, we just can’t compete with the Visas and MasterCards of the world.”

“The old customers don’t come ’round here no more,” said Felix Costa, 59, speaking from the Elizabeth, NJ, pool hall that has served as his place of business since 1972. “Time was, a guy who needed a quick $400 for a new refrigerator or some car repairs would come straight to me. Now, he just puts it on his Discover card.”

Though their client lists are dwindling, the loan sharks still have their champions.

“Call me old-fashioned, but I prefer the loan sharks to the credit-card companies,” said Gene Hobson of Detroit. “When I borrow money from Three Knuckles Benny, I know there’s going to be a personal touch, whether it’s a dead animal on my doorstep or one of my kids coming home with a missing toe. The credit cards just don’t give you that sort of individualized attention. And, if you’re late with them, it’s a form letter and maybe maybe an irate call from the accounts-receivable department.”

“With our overhead, we need to charge a 50 percent weekly interest rate just to break even,” said a Chicago loan shark who identified himself only as “Johnny Toothpick.” “We’ve got rent, pay-offs, and switchblade maintenance, not to mention travel expenses. How can we compete with rates as low as 18 to 26 percent a year?”

Continued Toothpick: “These [credit-card companies] are monsters. They care nothing about the damage they’re doing to the American landscape by driving us out. Loan sharking was about more than giving people money and roughing them up when they didn’t come through. It was about ruffling a kid’s hair on the street, helping out a local fella who needed a break, and occasionally letting somebody off easy with just a couple of punches to the gut instead of a glass-filled sock to the face. It’s a unique part of our shared national experience that, once extinct, will never come back.”

With nearly 200,000 new credit-card solicitations going out every week, the loan sharks have little hope of regaining the ground they’ve lost.

“We were going by word of mouth, and we did pretty good around the neighborhood,” Pistone said. “But these credit cards? With direct mail and the Internet, they reach a customer base we can only dream about. In this business climate, how can a small, independent goon possibly compete?”



How to Pay Off a Big Student Loan #calculate #auto #loan


#fast student loans
#

This Millennial Paid Off $23,375 in Student Loans in Just 10 Months

“If you have a game plan, you can accomplish your goals,” says 22-year-old Jordan Arnold.

Like many millennials, Jordan Arnold graduated from college five figures deep in student debt. Unlike most of his peers, he paid off all of his loans less than a year after graduation.

Bluffton, Ind.

When he started paying it down: May 2013

When he became debt-free: March 2014

How I started building debt

I always knew I was going to go to college, though I figured I d go to community college for a year or two because it s cheap. But my parents started talking to me about this private Christian school, Indiana Wesleyan in Marion, Ind. I took a visit, and I really liked it. It s only like 3,000 students on campus, so it s a tight-knit community.

Tuition and room and board was about $31,000 a year. And the first year I hadn t applied for federal student aid, since I didn t commit to the college until about 10 days before classes started. I got some scholarships and a grant from my church, though. So, ultimately, I owed approximately $9,000 that first year.

Getting to $23,000

I could only borrow up to $5,500 in subsidized loans from the government each year, so I worked to cover the rest so that I didn t have to take out private loans. I also graduated in three years, which helped.

Still, altogether, I had to take out $15,150 in subsidized federal loans and $2,000 in unsubsidized federal loans. I borrowed another $6,000 from my parents.

My uh-oh moment

In the fall semester of my senior year, I remember being kind of nervous. I knew I had to start paying my debt within six months. It s stressful, when you don t have any money. And I heard all these stories about college students who get out of school, they have all this debt, and they can t find jobs.

Getting my debts paid off was important to me. I didn t want to get the point where I d have to be paying student loans for another 10 years. Right now, I m single. I don t have any dependents that rely on my income. But I didn t want to have these loans over my head when I m trying to feed a family and put a roof over their heads. It s not just about me, it s about my future family.

My first step out of the hole

Luckily, I got a job right out of college at an insurance agency (I had majored in finance). I was on salary, and it was pretty good: $36,000 plus bonuses.

I didn t have to pay my student loans for another four months, but over the summer I decided to go ahead and start making payments before interest began accruing.

I actually moved back in with my parents which is hard when you have been out on your own. But I didn t really have a reason to move out. And I was blessed that they actually preferred me to live there because I could help out around the farm they own, baling hay or feeding the horses. Living at my parents place for free was a lot better than having to pay $400 or $500 a month for rent.

Kicking it into gear

About four months into my new job, I picked up a second job, delivering for Pizza Hut, to help pay off my debt. I would start work at the insurance agency at 8:30 a.m. change in the bathroom at 4:50 p.m. get to Pizza Hut by 5, deliver pizzas until about 9:30, get home around 10, then shower, eat, and go to bed.

My monthly take-home pay from the insurance company was about $2,200, and I made about $1,000 at Pizza Hut. After gas, car insurance, tithing to my church, entertainment and food, I could put about $2,000 towards my debt every month.

At that rate, I was projected to pay off my debt in May 2014. But I got a $3,000 refund on my taxes, and paid off the rest of my debt with that.

How I celebrated being debt-free

I made my last payment the first of March, then I went to Florida with some friends two weeks later. It was pretty rewarding after a 10-month battle. I had probably worked 65 to 70 hours a week for seven or eight months. It was exhausting, but it was worth it.

What I d tell someone else in my place

If you have a game plan, you can accomplish your goals. I have an account on Mint.com, that s where I kept my budget. That s a big part of it just seeing your progress and knowing you re getting closer.

Also, have an emergency fund. While I was paying off that debt, I had a small car accident. I was delivering a pizza, and I hit something in someone s driveway. It cost me about $760 to fix the car. But I had a $1,000 emergency fund, which was kind of a buffer that I kept because life happens.

Finally, don t be afraid to move home if you have to. That was a big part of how I got out of debt.

My plan for the future

I quit my Pizza Hut job in April after paying off my debt, and now work at a bank analyzing commercial and agricultural loans, which is more in line with what I wanted to do.

I actually haven t moved out of my parents house yet. Instead I m saving up for a down payment on a house. I m putting away 50% of my take-home income for that, and I should have a down payment by mid-summer. I also started investing. I started a Roth IRA, and I plan to max it out this year.

Staying true to myself

Some people have made the argument, Maybe you shouldn t have paid off the debt so fast because the interest rate is cheaper than what it will be for you to borrow for a home.

That makes sense in my head, but in my heart, I didn t want this hanging over me. I want to be responsible with my money and build a strong foundation.

Check out Money 101 for more resources:

  • I am unable to pay my debts. What can I do?
  • How do I get rid of my credit card debt?
  • How can I improve my credit score?
  • How do I set a budget I can stick to?


Trump Rolls Back Two Obama-Era Education Regulations #essa, #every #student #succeeds #act, #federal #regulations, #government #overreach, #president #donald #trump, #teacher #training,big #government, #education, #obama,education


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Trump Rolls Back Two Obama-Era Education Regulations

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President Donald Trump signed legislation Monday that rolls back two Obama-era education regulations — one regarding teacher training programs and another regarding requirements for states in meeting directives of the federal Every Student Succeeds Act (ESSA).

Trump signed H.J.Res. 58, which overturns the U.S. Education Department’s (USED) rule that relates to how teacher training programs are assessed. Additionally, the president signed H.J.Res. 57, which nullifies USED’s rule relating to state accountability requirements under ESSA.

The teacher training program requirement, part of the Higher Education Act, mandated states to rate training programs for teachers each year based, in part, on student outcome measures. The Washington Post describes the Obama rule as “broadly unpopular”:

Teachers unions said the regulations wrongly tied ratings of teacher-training programs to the performance of teachers’ students on standardized tests; colleges and states argued that the rules were onerous and expensive, and many Republicans argued that Obama’s Education Department had overstepped the bounds of executive authority.

The ESSA rule concerned states’ accountability in identifying failing schools and reporting their plans for improving them to the federal government..

In his remarks about these two House joint resolutions, Trump said they “eliminate harmful burdens on state and local taxes on school systems that could have cost states hundreds of millions of dollars.”

“So it’s the states and local-tax school systems, and that was very important,” the president added. “Parents, teachers, communities, and state leaders know the needs of their students better than anyone in Washington by far. So we re removing these additional layers of bureaucracy to encourage more freedom and innovation in our schools.”

The Republican-led Congress and Trump are overturning some Obama-era rules and regulations via the Congressional Review Act.

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Spikes Cavell – Data Analysts #analytics, #data, #big #data, #spend #analysis, #procurement, #spend, #dashboards


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We harness data and analytics to build smart, data-driven applications that solve real business problems.

Our solutions help: drive revenue growth, reduce costs, improve efficiency, monitor and measure performance or manage risk.

Our solutions help: drive local job creation, deliver more with smaller budgets, measure policy impact or manage risk.

It s all about the data

WHAT WE DO

Examples of our work

Improving visibility to save a nation $1.8bn

Problem: Poor visibility over a European country’s £10bn + annual spend on goods services leading to higher costs and inefficiency in procurement.
Solution: Capture, aggregation, cleansing, classification and enrichment of spend data for over 100 separate entities. Development of an analytics platform deployed to more than 750 users and used to reduce the costs of purchased goods services at national, regional and local levels.

Predicting contract cancellation to reduce ‘churn’

Problem: 4G mobile internet provider in South East Asia suffering revenue loss due to inability to anticipate cancellation.
Solution: Analysis of subscriber records to identify patterns in advance of contract cancellation. Predictive model developed that provides marketing and customer teams with accurate indication of likelihood of cancellation to facilitate retention.

Leveraging data to indicate fraud and abuse

Problem: More effective use of data to reduce losses to fraud and financial abuse by UK Government in the giving of grants.
Solution: Review and analysis of Government data sources and development of 124 potential fraud and abuse indicators and a scoring system design to generate ranked and prioritised ‘watch lists’ to support prioritisation of investigative effort.

Standardizing across borders to drive out cost

Problem: Failure of incumbent to accurately classify a global media company’s $14bn of annual direct and indirect spend.
Solution: Capture, cleansing, classification and enrichment of spend data for 11 business units across multiple geographies. Deployment of an analytics platform to group sourcing used to reduce the costs of commonly purchased goods services.

Location: North America

Monitoring spend with minority owned businesses

Problem: Lack of metrics to monitor spend with minority owned business and track delivery of policy at a large US school district.
Solution: Transformation of spend data combined with aggregation of external minority business data to enable calculation of spend with minorities delivered as a report and updated quarterly to enable tracking of ‘direction of travel’.

Location: North America

Directing spending to create jobs and opportunity

Problem: Leverage a European country’s public spending on goods services to support the growth of the local economy.
Solution: Transformation of spend and aggregation of multiple data sources to facilitate identification and ranking of high potential categories. Delivered as an analytical application with tools to conduct outreach to encourage bid participation.

Benchmarking property assets to manage outliers

Problem: Comparative metrics to manage property costs by global property management company too time consuming to compile.
Solution: Aggregation of spend and property data to create benchmarks against which outlying properties and suppliers could be identified to target cost reduction efforts. Delivered as a dashboard that included metrics to support the sales effort.

Empowering a nation’s ‘armchair auditors’

Problem: Re-engage electorate and rebuild trust and confidence in politicians and the political system at a major US city.
Solution: Repurposing of transformed spend to render it ‘citizen friendly’. Delivered as and easy-to-use web application accessible directly from the City’s own website to allow citizens, journalists and arm-chair analysts direct access to the City’s spend data.

Location: North America

Benchmarking peers to identify better performance

Problem: Absence of comparative metrics for a group of US universities to support identification and prioritisation of opportunities to better manage spend.
Solution: Transformation and standardization of spend by category combined with student and faculty data to generate meaningful category specific benchmarks. Delivered as an intelligent dashboard to enable comparison with peer institutions.

Location: North America

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The importance of big data analytics in business #why #big #data


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TechRadar pro

The importance of big data analytics in business

The term and use of big data is nothing new. In fact, more and more companies, both large and small, are beginning to utilize big data and associated analysis approaches as a way to gain information to better support their company and serve their customers.

Let’s put today’s data in perspective. One study estimated that by 2024, the world’s enterprise servers will annually process the digital equivalent of a stack of books extending more than 4.37 light-years to Alpha Centauri, our closest neighboring star system in the Milky Way Galaxy. That’s a lot of data to gather or analyze let alone understand!

According to Gartner analyst Svetlana Sicular, “Big data is a way to preserve context that is missing in the refined structured data stores this means a balance between intentionally “dirty” data and data cleaned from unnecessary digital exhaust, sampling or no sampling. A capability to combine multiple data sources creates new expectations for consistent quality; for example, to accurately account for differences in granularity, velocity of changes, lifespan, perishability and dependencies of participating datasets. Convergence of social, mobile, cloud and big data technologies presents new requirements getting the right information to the consumer quickly, ensuring reliability of external data you don’t have control over, validating the relationships among data elements, looking for data synergies and gaps, creating provenance of the data you provide to others, spotting skewed and biased data.”

With the use of big data becoming more and more important to businesses, it is even more vital for them to find a way to analyze the ever (faster) growing disparate data coursing through their environments and give it meaning.

Getting the Right Information for Your Business

Focusing on the right information by asking what’s important to the business is a key point in obtaining better data context. In a presentation held at TeamQuest ITSO Summit this past June titled “The Data Driven Business of Winning” Managing Director of CMS Motor Sports Ltd. Mark Gallagher, shared how Formula One teams successfully analyze data to ensure the safety of drivers and win races.

Gallagher explained how a team of data engineers, analyzing reams of information in real time, can help make strategic decisions for the business during the race. “In 2014 Formula One, any one of these data engineers can call a halt to the race if they see a fundamental problem developing with the system like a catastrophic failure around the corner.”

It comes down to the data engineers looking for anomalies. “99% of the information we get, everything is fine,” Gallagher said. “We’re looking for the data that tells us there’s a problem or that tells us there’s an opportunity.” In a nutshell, it’s about finding the anomalies that matter, in the context of the business problem being managed.

A Formula One driver’s steering wheel is basically a laptop, providing him with the data needed to make the best decision available. Drivers can scroll through a 10-point menu while driving and adjust parameters that affect the performance of the vehicle. This happens because the driver is able to get to the right data when needed to get a desired outcome.

Lots of data is collected by IT, which shares data that’s important to the customer (business), and together they use that data to gain an advantage and be successful in the marketplace.

Proving the Value in IT to Business

How can you prove the value of IT to business? The ability to measure costs is key but having the ability to measure the business results that come from the use of IT services (private cloud environments, for example) will drive better business conversations with IT management.

Focus on business goals and understand how the use of IT services contribute to business results and provide the best basis for planning future services. The majority of CIOs believe the IT department can increase the value it delivers to the organization by improving cost measurement.

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Microsoft to offer three new ways to store big data on Azure #big #data #story


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Microsoft to offer three new ways to store big data on Azure

Microsoft will soon offer three additional ways for enterprises to store data on Azure, making the cloud computing platform more supportive of big data analysis.

Azure will have a data warehouse service, a “data lake” service storing large amounts of data, and an option for running “elastic” databases that can store sets of data that vary greatly in size, explained Scott Guthrie, Microsoft executive vice president of the cloud and enterprise group, who unveiled these new services at the company’s Build 2015 developer conference, held this week in San Francisco.

The Azure SQL Data Warehouse, available later this year, will give organizations a way to store petabytes of data so it can be easily ingested by data analysis software, such as the company’s Power BI tool for data visualization, the Azure Data Factory for data orchestration, or the Azure Machine Learning service.

Unlike traditional in-house data warehouse systems, this cloud service can quickly be adjusted to fit the amount of data that actually needs to be stored, Guthrie said. Users can also specify the exact amount of processing power they’ll need to analyze the data. The service builds on the massively parallel processing architecture that Microsoft developed for its SQL Server database.

The Azure Data Lake has been designed for those organizations that need to store very large amounts of data, so it can be processed by Hadoop and other “big data” analysis platforms. This service could be most useful for Internet of Things-based systems that may amass large amounts of sensor data.

“It allows you to store literally an infinite amount of data, and it allows you to keep data in its original form,” Guthrie said. The Data Lake uses Hadoop Distributed File System (HDFS), so it can be deployed by Hadoop or other big data analysis systems.

A preview of the Azure Data Lake will be available later this year.

In addition to these two new products, the company has also updated its Azure SQL Database service so customers can pool their Azure cloud databases to reduce storage costs and prepare for bursts of database activity.

“It allows you to manage lots of databases at lower cost,” Guthrie said. “You can maintain completely isolated databases, but allows you to aggregate all of the resources necessary to run those databases.”

The new service would be particularly useful for running public-facing software services, where the amount of database storage needed can greatly fluctuate. Today, most Software-as-a-Service (SaaS) offerings must overprovision their databases to accommodate the potential peak demand, which can be financially wasteful. The elastic option allows an organization to pool the available storage space for all of its databases in such a way that if one database rapidly grows, it can pull unused space from other databases.

The new elastic pooling feature is now available in preview mode.

Microsoft Azure’s new Data Lake architecture.



Data Science Africa 2017 #machine #learning #and #big #data


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Data Science Africa 2017

The last few years have witnessed an explosion in the quantity and variety of data available in Africa, produced either as a by-product of digital services, from sensors or measuring devices, satellites and from many other sources. A number of practical fields have been transformed by the ability to collect large volumes of data: for example, bioinformatics with the development of high throughput sequencing technology capable of measuring gene expression in cells, or agriculture with the widespread availability of high quality remote sensing data. For other data sources – such as mobile phone usage records from telecoms operators, which can be used to measure population movement and economic activity – we are just beginning to understand the practical possibilities.

Data science seeks to exploit advances in machine learning and statistics to make sense of the growing amounts of data available from various sources. In Africa, a number of problems in areas such as healthcare, agriculture, disaster response and wildlife conservation would benefit greatly if domain experts were exposed to data science techniques. These skills would allow practitioners to extract useful information from these abundant sources of raw data

Summer School on Machine Learning and Data Science

Dates: 17 July – 19 July 2017

Venue: Nelson Mandela African Institute of Science and Technology, Tanzania

In the tradition of previous Africa Data Science workshops, a summer school on machine learning and data science will be held prior to the main workshop. This summer school will target graduate students, researchers and professionals working with huge amounts of data or unique datasets.

The summer school will focus on introductory and advanced lectures in data science and machine learning as well as moderate to advanced practical and tutorial sessions where participants will get their hands wet wrangling and munging datasets and applying cutting edge machine learning techniques to derive inference from the data. Lectures will be given by distinguished world renown researchers and practitioners including researchers from Sheffield University, Amazon, Swansea University Medical School, Facebook, Pulse Lab Kampala, the AI and Data Science (AIR) lab-Makerere University, ARM and Dedan Kimathi University of Technology (DeKUT).

The school will also involve end-to-end tutorial sessions from professionals walking the participants through a real data analytics problem from data acquisition to data presentation. To benefit from this course participants are encouraged to have some background in programming particularly programming with Python.

School programme outline:

Draft Lecture Schedule

Stuff to install..

To ensure we hit the ground running, it is essential you install the prerequiste software and test it out and make sure it is working on your computer. The venue for the summer school will have some computers on which the software will have been installed but you are advised to come with your own laptop with the software installed.

Anaconda

Luckily all the software required has already been prepackaged in a bundle called Anaconda. You can download the various versions of the software for your laptop OS and architecture from the Anaconda website. Please download the Python 3.6 version. Instructions on how to install are next to the download links on the Anaconda website.

Stuff to do..

To ensure that the software is working fine on your machine and to get you up and running, download the following jupyter notebook (right click and ‘save as’) and do the exercises in there. To access it you’ll need to run a jupyter notebook (instructions ).

Troubleshooting and comments..

Use the comment section below to (a) ask questions that are not already answered (b) help your peers by providing answers to their questions, if you can.

Please enable JavaScript to view the comments powered by Disqus.

Summer School Day 1

The first day of the data science school will introduce the jupyter notebook and overview the use of python for analyzing data. We will introduce the machine learning technique of classification and perform lab practicals exploring these techniques.

Time

Call for Registration

The workshop will be organized around paper presentations and interactive panel discussions. We invite participants interested in presenting work at the workshop to submit a short abstract describing the application of data science methods to problems relevant to Africa. These may include, for example, the following areas:

  • Data Science for the Sustainable Development Goals
  • Healthcare
  • Agriculture
  • Wildlife conservation
  • Disaster response
  • Geospatial modelling
  • Telecommunications data modelling
  • Economic monitoring

During the panel discussions, we will unite a wide range of stakeholders, including data scientists, representatives from government, development practitioners and the private sector; this will provide a unique setting in which innovative solution driven ideas can thrive.

Participants will also develop a framework for attracting young African talent, mentors and researchers from academia, the public sector and the private sector in Africa to engage in activities geared towards harnessing big data and real-time analytics for the public good.

Workshop programme outline:



Business Intelligence And The Smart EMR #explorys,health #catalyst,mckesson,ibm,microsoft,sap,oracle,smart #emr,community #hospitals,healthcare #big #data,hospital #business #intelligence,hospital #electronic #health #record,hospital #electronic #medical #record,hospital #emr,hospital #healthcare #it,hospital #it #systems


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A new study by KLAS suggests that while providers are giving thought to business intelligence needs, they still haven t honed in on favored vendors that they see as holding a leading position in healthcare. That may be, I d suggest, because the industry is still waiting on EMRs that can offer the BI functionality they really need.

To look at the issue of BI in healthcare, KLAS interviewed execs at more than 70 hospitals and delivery systems with 200 or more beds.

When asked which BI vendors will stand out in the healthcare industry, 41 percent of respondents replied that they weren t sure, according to a story in Health Data Management .

Of the other 59 percent who chose a vendor, IBM, SAP, Microsoft and Oracle came up as leaders in enterprise BI applications but none of the above got more than 12 percent of the vote, HDM notes.

Vendors that did get a nod as standing out in healthcare-specific BI included Explorys, Health Catalyst, McKesson and Humedica (Optum). IBM and Microsoft were also singled out for healthcare use, but respondents noted that their products came with high price tags.

Meanwhile, QlikTech and Tableau Software were noted for their usability and data visualization tools though lacking in full BI toolsets, according to HDM.

While these stats are somewhat interesting on their own, they sidestep a very important issue: when will EMRs evolve from transaction-based to intelligence-based systems. After all, an intelligence-based EMR can do more to improve healthcare in context than freestanding BI systems.

As my colleague John Lynn notes, EMRs will ultimately need to leverage big data and support smart processes, becoming what he likes to call the Smart EMR. These systems will integrate business intelligence natively rather than requiring a whole separate infrastructure to gather insights from the tsunami of patient data being generated today.

The reality, unfortunately, is that we re a fairly long way away from having such Smart EMRs in place. Readers, how long to you think it will take before such a next-gen EMR hits the market? And who do you think will be the first to market with such a system?

Hospital EMR EHR Resources

Recent Comments

  • Operational CIO vs Strategic CIO (3 )
    • Drex DeFord. https://www.linkedin.com/pulse /darwin-often-misquoted-drex-d eford
    • John Lynn. Tom, Exactly. However, many CIOs relegate themselves to just making sure the servers are on, the desktops.
  • VA (Veteran s Administration) Chooses Cerner EHR (5 )
    • Alec Johnson. Tim you provide an interesting solution to a seriously complex issue. I do respectfully disagree with.
    • John Lynn. So, if EHR s change, government gets smart, or patients start caring. In Vegas we call all of those.
    • Tim Shear. Only if the EHR platforms agree to adopt a standard data platform to take away the advantages and hold on.

Categories

EMR and EHR in the hospital, A complex mess of potential benefit!

2005 – 2017 Hospital EMR and EHR



Truck Accident Statistics #truck #accident #statistics,semi #truck #accident #statistics,commercial #truck #statistics,big #rig #truck #accident #statistics,18 #wheeler #accident #statistics,tractor #trailer #accident #statistics,truck #accident #statistics,statistics #for #truck #accidents,fire #truck #accident #statistics


Truck Accident Statistics

The use of the Internet or this form for communication with the firm or any individual member of the firm does not establish an attorney-client relationship. Confidential or time-sensitive information should not be sent through this form.

Due to their massive sizes and heavy weights, trucks can cause serious damage and death, should they be involved in an accident. To inform the public about traffic safety and to bring the dangers of truck collisions to light, various agencies throughout the U.S.—including the U.S. Department of Transportation (USDOT), the National Center for Statistics and Analysis (NCSA) and the National Highway Traffic Safety Administration (NHTSA)—have compiled the following statistics regarding the incidence of different types of truck accidents in the U.S.

Semi Heavy Truck Accident Statistics


  • Of the 15.5 million trucks in the U.S. nearly 13 percent are semis , big rigs. 18 wheelers and tractor trailers
  • About 98 percent of all semi accidents result in at least one fatality

  • Fatal tractor trailer accidents cost Americans more than $20 billion each year, $13.1 billion of which is the cost associated with loss of quality of life
  • For every 100 million miles driven on U.S. road ways, there are 2.3 deaths and 60.5 injuries caused by big rigs

Commercial truck accident statistics


  • The average cost of a commercial truck accident is about $59,150
  • Nearly 90 percent of commercial truck accidents are caused or worsened by some sort of human error—either on behalf of a truck driver, other drivers, other vehicle passengers, cyclists or pedestrians
  • About 75 percent of commercial truck accidents are caused by drivers of other smaller passenger vehicles, rather than the truck driver
  • Driver fatigue is responsible for roughly 30 percent of all commercial truck accidents

Truck Accident Injury Statistics


  • About 130,000 individuals are injured each year in truck collisions

  • About 22 percent of all truck accidents result in injuries
  • In most truck accidents (about 70 percent), there are no injuries or deaths—only property damage

Fire Truck Accident Statistics


  • On average, there are nearly 2,500 fire truck accidents each year in the U.S.
  • Of the 21 fatalities reported from these accidents, 6 were occupants of a fire truck
  • Nearly 1,100 people were injured or killed in fire truck accidents last year
  • In 1,882 fire truck collisions, only property damage occurred

Get Help For Your Injuries

If you or a loved one has been injured in any type of truck accident, or if your loved one has died in a truck crash, contact us to speak with a highly qualified personal injury lawyer. Because of the complexities in truck crash cases, it is important to hire an attorney who has experience handling truck accidents claims, specifically. People often confuse auto accident attorneys for truck accident attorneys. It is important to note, however, that choosing the right attorney, with the right experience, can make all the difference in the outcome of your case.

For more information about truck accident statistics or to speak with a qualified attorney about your potential claim, contact truck accident lawyers today.



Big Data Analytics Training in Hyderabad #big #data #analytics #training #in #hyderabad, #big #data #analytics #courses #in #hyderabad, #big #data #training #in #hyderabad,big #data #analytics #training #institute #in #hyderabad,best #big #data #analytics #training #in #hyderabad


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Big Data Analytics Training in Hyderabad

Imagine Your Business with Advanced Big Data Analytics

Big Data Analytics is incredibly becoming new raw materials of business enhancement and has come up with the main goal to turn data into information and information into insights. This is the right time to build a career in right technology for your data center where it takes from data overload to data insights to have everlasting and booming career in IT world. Simply sign-up with Best Big Data Analytics Training Institute in Hyderabad to master In-Depth subject knowledge skill set to act as Industry-Ready aspirants.

Analytics Path is the best Powerhouse for Big Data Analytics Training in Hyderabad which is located in Hi-Tech City Madhapur, Hyderabad that helps to achieve the dream goals of aspirants.

Why Big Data Analytics Training in Hyderabad is Best Career Move?

The rise of analytics is incredible wherever you go, you can hear the word of Analytics which is the most buzz word these days. You can easily change the way of Business enhancement by Big Data Analytics Training in Hyderabad where it has brought great impact over the Internet to transform every aspect of life. The power of analytics is high and it is impossible to go back.

Big Data Analytics Training in Hyderabad will make aspirants to acquire tremendous knowledge to change the multi-million dollars of the company. Aspirants will acquire fully fledged knowledge in the various applications of a sequence of algorithms to generate insights from processed datasets. Annual hike for Analytics Professionals is very high and is the competitive resource for many of the companies to make an effective better decision.

Intended Audience

BI, ETL, Data Warehousing and Data Base Professionals, Software Developers and Architects, Graduates interested in making a career in Big Data, Hadoop Professionals can prefer Big Data Analytics Course in Hyderabad.

Big Data Analytics Training and Certification Program Take Away

Upon completion of High Interactive Training classes, aspirants can acquire huge subject knowledge skills

  • Data can be fetched from multiple users and structured
  • Grasps skills in various types of Machine Learning, Distance Metrics, and Gradient Descent
  • Enhances knowledge in Support Vector Machine, KNN, CART, Neural Network and Regression
  • Enriches knowledge in Clustering, Segmentation, PCA and Association Rule Mining
  • Big Data Technologies like Hadoop, MapReduce, Pig, Hive,Mongo DB, Cassandra, AWS, Spark and many other
  • Skills in managing of Data Project with regards to time, cost, effort, valuation and risk analysis of data project

Future Prospects after Analytics Path Big Data Analytics Course in Hyderabad

In Big Data Analytics Training in Hyderabad, aspirants will acquire skills from basic level to advanced analytics knowledge with various practical methodologies and real-time scenarios. Individuals will acquire knowledge in both executive management and decision making with hands-on training experience in a reliable and effective way. Industry expertise makes easy to understand in every module by using various machine learning tools and techniques.

Big Data Analytics Course in Hyderabad leverages skills in every module that includes Pharma, Telecom, Manufacturing, Retail, Health Care and Public Sector Administration to face Industry world challenges in an effective way.

The State and Rise of Analytics Today

Certified Big Data Analytics Professionals can easily grasp wonderful job opportunity in the top notch companies.

McKinsey Global Institutes of Big Data Analytics – The next frontier for innovation, competition, and productivity estimates that by 2018, “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know- how to use the analysis of big data to make effective decisions”

There is a prediction that there are 2.5 lakh jobs in the coming year in India. The pays scale for the Big Data Analytics Professionals has gone up in an incredible way.

If you wanted to have exciting career future, just join Analytics Path- The Best Big Data Analytics Training Institute in Hyderabad.

Contact Us



How to Implement a Big Data System #big #data #implementation


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How to Implement a Big Data System

Understanding a big data infrastructure by looking at a typical use case.

Published January 2012

I often get asked about big data, and more often than not we seem to be talking at different levels of abstraction and understanding. Words such as real time and advanced analytics show up, and we are instantly talking about products, which is typically not a good idea.

So let’s try to step back and look at what big data means from a use-case perspective, and then we can map the use case into a usable, high-level infrastructure picture. As we walk through all this, you will I hope start to see a pattern and start to understand how words such as real time and analytics fit in.

The Use Case in Business Terms

Rather then inventing something from scratch I’ve looked at the keynote use case describing Smartmall (you can see a nice animation and explanation of smart mall in this video ).

The idea behind Smartmall is often referred to as multichannel customer interaction. meaning how can I interact with customers that are in my brick-and-mortar store via their smartphones ? Rather than requiring customers to whip out their smartphone to browse prices on the internet, we would like to drive their behavior proactively.

The goals of Smartmall are straightforward:

  • Increase store traffic within the mall.
  • Increase revenue per visit and per transaction.
  • Reduce the non-buy percentage.

What Do You Need?

In terms of technologies you would be looking at the following:

  • Smart devices with location information tied to an individual
  • Data collection and decision points for real-time interactions and analytics
  • Storage and processing facilities for batch-oriented analytics

In terms of data sets, you would want to have at least the following:

  • Customer profiles tied to an individual and linked to the individual’s identifying device (phone, loyalty card, and so on)
  • A very fine-grained customer segmentation tied to detailed buying behavior and tied to elements such as coupon usage, preferred products, and other product recommendations

High-Level Components

A picture speaks a thousand words, so Figure 2 shows both the real-time decision-making infrastructure and the batch data processing and model generation (analytics) infrastructure.

Figure 2. Example Infrastructure

The first and, arguably, most important step and the most important piece of data is the identification of a customer. Step 1, in this case, is the fact that a user with a smartphone walks into a mall. By identifying this, we trigger the lookups in step 2a and step 2b in a user-profile database.

We will discuss this a little more later but, in general, this is a database leveraging an indexed structure to do fast and efficient lookups. Once we find the actual customer, we feed the profile of this customer into our real-time expert system (step 3).

The models in the expert system (custom-built or COTS software) evaluate the offers and the profile and determine what action to take (for example, send a coupon). All this happens in real time, keeping in mind that Websites do this in milliseconds and our smart mall would probably be OK doing it in a second or so.

To build accurate models and this where many of the typical big data buzz words come in we add a batch-oriented massive-processing farm into the picture. The lower half of Figure 3 shows how we leverage a set of components that includes Apache Hadoop and the Apache Hadoop Distributed File System (HDFS) to create a model of buying behavior. Traditionally, we would leverage a database (or data warehouse [DW]) for this. We still do, but we now leverage an infrastructure before the database/data warehouse to go after more data and to continuously re-evaluate all the data.

Figure 3. Creating a Model of Buying Behavior

A word on the data sources. One key element is point-of-sale (POS) data (in the relational database), which you want to link to customer information (either from your Web store, from cell phones, or from loyalty cards). The NoSQL database with customer profiles in Figure 2 and Figure 3 show the Web store element. It is very important to make sure this multichannel data is integrated (and deduplicated, but that is a different topic) with your Web browsing, purchasing, searching, and social media data.

Once the data linking and data integration is done, you can figure out the behavior of an individual. In essence, big data allows microsegmentation at the person level in effect, for every one of your millions of customers!

The final goal of all this is to build a highly accurate model that is placed within the real-time decision engine. The goal of the model is directly linked to the business goals mentioned earlier. In other words, how can you send a customer a coupon while the customer is in the mall that gets the customer to go to your store and spend money?

Detailed Data Flows and Product Ideas

Now, how do you implement this with real products and how does your data flow within this ecosystem? The answer is shown in the following sections.

Step 1: Collect Data

To look up data, collect it, and make decisions on it, you need to implement a system that is distributed. Because the devices essentially keep sending data, you need to be able to load the data (collect or acquire it) without much delay. That is done in the collection points shown in Figure 4. That is also the place to evaluate the data for real-time decisions. We will come back to the collection points later.

Figure 4. Collection Points

The data from the collection points flows into the Hadoop cluster, which, in our case, is a big data appliance. You would also feed other data into this appliance. The social feeds shown in Figure 4 would come from a data aggregator (typically a company) that sorts out relevant hash tags, for example. Then you use Flume or Scribe to load the data into Hadoop.

Step 2: Collate and Move the Data

The next step is to add data (social feeds, user profiles, and any other data required to make the results relevant to analysis) and to start collating, interpreting, and understanding the data.

Figure 5. Collating and Interpreting the Data

For instance, add user profiles to the social feeds and add the location data to build a comprehensive understanding of an individual user and the patterns associated with this user. Typically, this is done using Apache Hadoop MapReduce. The user profiles are batch-loaded from the Oracle NoSQL Database via a Hadoop InputFormat interface and, thus, added to the MapReduce data sets.

To combine all this with the POS data, customer relationship management (CRM) data, and all sorts of other transactional data, you would use Oracle Big Data Connectors to efficiently move the reduced data into the Oracle Database. Then you have a comprehensive view of the data that you can go after, either by using Oracle Exalytics or business intelligence (BI) tools or and this is the interesting piece via things such as data mining.

Figure 6. Moving the Reduced Data

Step 3: Analyze the Data

That last phase here called analyze creates data mining models and statistical models that are used to produce the right coupons. These models are the real crown jewels, because they allow you to make decisions in real time based on very accurate models. The models go into the collection and decision points to act on real-time data, as shown in Figure 7.

Figure 7. Analyzing the Data

In Figure 7, you see the gray model being utilized in the Expert Engine. That model describes and predicts the behavior of an individual customer and, based on those predictions, determines what action to take.

Conclusion

The description above is an end-to-end look at big data and real-time decisions. Big data allows us to leverage tremendous amounts of data and processing resources to arrive at accurate models. It also allows us to determine all sorts of things that we were not expecting, which creates more-accurate models and also new ideas, new business, and so on.

You can implement the entire solution shown here using the Oracle Big Data Appliance on Oracle technology. Then you’ll just need to find a few people who understand the programming models to create those crown jewels.



Big Sky Western Bank #personal #finance


#sky loans
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Loan Application

Please complete an application for the loan or line and submit it to any Big Sky Western Bank office. Simply call 587-2922, contact us. or visit any Big Sky Western Bank SM office so you can say YES to yourself today!

Vehicle Loans

Big Sky Western Bank offers lending programs to purchase or refinance a new or used vehicle including cars, trucks, pickups, sport utility vehicles, recreational vehicles, and more.

Personal Loans

When you need extra cash for whatever reason, we can set up a personal loan for you. Collateral for these types of loans can be Big Sky Western Bank certificates of deposit, real estate, and vehicles. (Or can be done unsecured – On Approved Credit -).

Home Equity Lines of Credit and Loans

A Big Sky Western Bank SM Home Equity Loan or Line of Credit is an agreement where the borrower(s) uses the equity in their home as collateral for the loan / line. Home Equity loans and lines are the most popular methods for consumers to finance home improvements, invest in more property, manage their personal cashflow, purchase vehicles or consolidate debt, and usually at a lower interest rate. Home equity loans are for a specific amount and are amortized / paid over a defined period of time. A Home Equity Line of Credit is a revolving line of credit accessed by Online Banking or through checks. You can make major purchases, pay off debt or use for any other purpose to manage your personal cashflow. Generally these loans and lines are the second mortgage on your residence, but can be a first mortgage, or possibly a mortgage on a second home.

A Big Sky Western Bank SM Home Equity Line of Credit could open up a new world of financial independence for you. It’s easy. By borrowing against the equity in your home, you create a line of credit that you control.

For example. If you establish a $15,000 home equity line of credit, you access these funds by simply writing a check. If you write checks totaling $2,500, you only pay interest on that amount. You still have $12,500 available to use. Pay down your line and use these funds over and over again. It’s that simple. like approving your own loan whenever you want.

Why should you obtain a Home Equity Loan or Line?

One of the biggest advantages of a home equity loan or line compared to a vehicle or consumer loan is the possible tax deductibility of the interest paid on these loans — consult a competent tax advisor.

Click here to access our easy to use Online calculators. to help you solve some common financial problems. If you find these calculators useful, be sure to bookmark this page or suggest it as a link to your favorite home page or search engine.



Big Data – Analytics #big #data #security #analytics


#

Big Data Analytics

Big Data Analytics

Analytics Business Intelligence

Complex business problems require advanced analytical platforms. We can help run any analytics on any data anywhere, streaming or at rest, to drive better decisions across your organization. Dell Services has been acquired by NTT DATA. Select these offers to be redirected to their website.

Data Integration

Eliminate fragmented analysis with a real-time view of all your data. Integrate over 160 data sources, regardless of the underlying data platform or its location (on- and off-premises).

Analytics Business Intelligence

Complex business problems require advanced analytical platforms. We can help run any analytics on any data anywhere, streaming or at rest, to drive better decisions across your organization. Dell Services has been acquired by NTT DATA. Select these offers to be redirected to their website.

Data Integration

Eliminate fragmented analysis with a real-time view of all your data. Integrate over 160 data sources, regardless of the underlying data platform or its location (on- and off-premises).

Analytics Business Intelligence

Complex business problems require advanced analytical platforms. We can help run any analytics on any data anywhere, streaming or at rest, to drive better decisions across your organization. Dell Services has been acquired by NTT DATA. Select these offers to be redirected to their website.

Data Integration

Eliminate fragmented analysis with a real-time view of all your data. Integrate over 160 data sources, regardless of the underlying data platform or its location (on- and off-premises).

Ultrabook, Celeron, Celeron Inside, Core Inside, Intel, Intel Logo, Intel Atom, Intel Atom Inside, Intel Core, Intel Inside, Intel Inside Logo, Intel vPro, Itanium, Itanium Inside, Pentium, Pentium Inside, vPro Inside, Xeon, Xeon Phi, and Xeon Inside are trademarks of Intel Corporation in the U.S. and/or other countries.

Offers subject to change, not combinable with all other offers. Taxes, shipping, handling and other fees apply. U.S. Dell Home new purchases only. Dell reserves the right to cancel orders arising from pricing or other errors.

*Promotional eGift Card: Arrives separately form purchase, typically in 10-20 days from ship date via email; expires in 90 days (except where prohibited by law). Terms and conditions apply. See www.Dell.com/giftcard/promoterms .

*Rewards are issued to your online Dell Advantage Loyalty Rewards Account (available via your Dell.com My Account) typically within 30 business days after your order’s ship date; Rewards expire in 90 days (except where prohibited by law). “Current rewards balance” amount may not reflect the most recent transactions occurring within the past 30 business days. Bonus rewards on select purchases identified at dell.com/businessrewards or by calling 800-456-3355. Total rewards earned may not exceed $2,000 within a 3 month period. Any balance remaining on your purchase after Rewards are applied may not be paid with DBC and instead a separate form of payment must be used. Outlet purchases do not qualify for rewards. Expedited Delivery not available on certain TVs, monitors, batteries and adapters, and is available in Continental (except Alaska) U.S. only. Other exceptions apply. Not valid for resellers and/or online auctions. See Dell.com/businessrewardsfaq.

Lower TCO: TCO calculated over 5 years and applies to specific workloads. Source: “Cloud Comparison: Microsoft Private Cloud on the Intel-Powered Dell Solution vs. a Leading Public Cloud Provider,” a Principled Technologies Report commissioned by Dell, August 2014. Actual results will vary.

Intel, the Intel logo, Xeon, and Xeon Inside are trademarks or registered trademarks of Intel Corporation in the U.S.and/or other countries.

^Dell Business Credit. Offered to business customers by WebBank who determines qualifications for and terms of credit. Taxes, shipping and other charges are extra and vary. Minimum monthly payments are the greater of $15 or 3% of the new balance shown on the monthly billing statement. Dell and the Dell logo are trademarks of Dell Inc.



How to Pay Off a Big Student Loan #no #credit #car #loans


#fast student loans
#

This Millennial Paid Off $23,375 in Student Loans in Just 10 Months

“If you have a game plan, you can accomplish your goals,” says 22-year-old Jordan Arnold.

Like many millennials, Jordan Arnold graduated from college five figures deep in student debt. Unlike most of his peers, he paid off all of his loans less than a year after graduation.

Bluffton, Ind.

When he started paying it down: May 2013

When he became debt-free: March 2014

How I started building debt

I always knew I was going to go to college, though I figured I d go to community college for a year or two because it s cheap. But my parents started talking to me about this private Christian school, Indiana Wesleyan in Marion, Ind. I took a visit, and I really liked it. It s only like 3,000 students on campus, so it s a tight-knit community.

Tuition and room and board was about $31,000 a year. And the first year I hadn t applied for federal student aid, since I didn t commit to the college until about 10 days before classes started. I got some scholarships and a grant from my church, though. So, ultimately, I owed approximately $9,000 that first year.

Getting to $23,000

I could only borrow up to $5,500 in subsidized loans from the government each year, so I worked to cover the rest so that I didn t have to take out private loans. I also graduated in three years, which helped.

Still, altogether, I had to take out $15,150 in subsidized federal loans and $2,000 in unsubsidized federal loans. I borrowed another $6,000 from my parents.

My uh-oh moment

In the fall semester of my senior year, I remember being kind of nervous. I knew I had to start paying my debt within six months. It s stressful, when you don t have any money. And I heard all these stories about college students who get out of school, they have all this debt, and they can t find jobs.

Getting my debts paid off was important to me. I didn t want to get the point where I d have to be paying student loans for another 10 years. Right now, I m single. I don t have any dependents that rely on my income. But I didn t want to have these loans over my head when I m trying to feed a family and put a roof over their heads. It s not just about me, it s about my future family.

My first step out of the hole

Luckily, I got a job right out of college at an insurance agency (I had majored in finance). I was on salary, and it was pretty good: $36,000 plus bonuses.

I didn t have to pay my student loans for another four months, but over the summer I decided to go ahead and start making payments before interest began accruing.

I actually moved back in with my parents which is hard when you have been out on your own. But I didn t really have a reason to move out. And I was blessed that they actually preferred me to live there because I could help out around the farm they own, baling hay or feeding the horses. Living at my parents place for free was a lot better than having to pay $400 or $500 a month for rent.

Kicking it into gear

About four months into my new job, I picked up a second job, delivering for Pizza Hut, to help pay off my debt. I would start work at the insurance agency at 8:30 a.m. change in the bathroom at 4:50 p.m. get to Pizza Hut by 5, deliver pizzas until about 9:30, get home around 10, then shower, eat, and go to bed.

My monthly take-home pay from the insurance company was about $2,200, and I made about $1,000 at Pizza Hut. After gas, car insurance, tithing to my church, entertainment and food, I could put about $2,000 towards my debt every month.

At that rate, I was projected to pay off my debt in May 2014. But I got a $3,000 refund on my taxes, and paid off the rest of my debt with that.

How I celebrated being debt-free

I made my last payment the first of March, then I went to Florida with some friends two weeks later. It was pretty rewarding after a 10-month battle. I had probably worked 65 to 70 hours a week for seven or eight months. It was exhausting, but it was worth it.

What I d tell someone else in my place

If you have a game plan, you can accomplish your goals. I have an account on Mint.com, that s where I kept my budget. That s a big part of it just seeing your progress and knowing you re getting closer.

Also, have an emergency fund. While I was paying off that debt, I had a small car accident. I was delivering a pizza, and I hit something in someone s driveway. It cost me about $760 to fix the car. But I had a $1,000 emergency fund, which was kind of a buffer that I kept because life happens.

Finally, don t be afraid to move home if you have to. That was a big part of how I got out of debt.

My plan for the future

I quit my Pizza Hut job in April after paying off my debt, and now work at a bank analyzing commercial and agricultural loans, which is more in line with what I wanted to do.

I actually haven t moved out of my parents house yet. Instead I m saving up for a down payment on a house. I m putting away 50% of my take-home income for that, and I should have a down payment by mid-summer. I also started investing. I started a Roth IRA, and I plan to max it out this year.

Staying true to myself

Some people have made the argument, Maybe you shouldn t have paid off the debt so fast because the interest rate is cheaper than what it will be for you to borrow for a home.

That makes sense in my head, but in my heart, I didn t want this hanging over me. I want to be responsible with my money and build a strong foundation.

Check out Money 101 for more resources:

  • I am unable to pay my debts. What can I do?
  • How do I get rid of my credit card debt?
  • How can I improve my credit score?
  • How do I set a budget I can stick to?


How to Pay Off a Big Student Loan #payday #loans #australia


#fast student loans
#

This Millennial Paid Off $23,375 in Student Loans in Just 10 Months

“If you have a game plan, you can accomplish your goals,” says 22-year-old Jordan Arnold.

Like many millennials, Jordan Arnold graduated from college five figures deep in student debt. Unlike most of his peers, he paid off all of his loans less than a year after graduation.

Bluffton, Ind.

When he started paying it down: May 2013

When he became debt-free: March 2014

How I started building debt

I always knew I was going to go to college, though I figured I d go to community college for a year or two because it s cheap. But my parents started talking to me about this private Christian school, Indiana Wesleyan in Marion, Ind. I took a visit, and I really liked it. It s only like 3,000 students on campus, so it s a tight-knit community.

Tuition and room and board was about $31,000 a year. And the first year I hadn t applied for federal student aid, since I didn t commit to the college until about 10 days before classes started. I got some scholarships and a grant from my church, though. So, ultimately, I owed approximately $9,000 that first year.

Getting to $23,000

I could only borrow up to $5,500 in subsidized loans from the government each year, so I worked to cover the rest so that I didn t have to take out private loans. I also graduated in three years, which helped.

Still, altogether, I had to take out $15,150 in subsidized federal loans and $2,000 in unsubsidized federal loans. I borrowed another $6,000 from my parents.

My uh-oh moment

In the fall semester of my senior year, I remember being kind of nervous. I knew I had to start paying my debt within six months. It s stressful, when you don t have any money. And I heard all these stories about college students who get out of school, they have all this debt, and they can t find jobs.

Getting my debts paid off was important to me. I didn t want to get the point where I d have to be paying student loans for another 10 years. Right now, I m single. I don t have any dependents that rely on my income. But I didn t want to have these loans over my head when I m trying to feed a family and put a roof over their heads. It s not just about me, it s about my future family.

My first step out of the hole

Luckily, I got a job right out of college at an insurance agency (I had majored in finance). I was on salary, and it was pretty good: $36,000 plus bonuses.

I didn t have to pay my student loans for another four months, but over the summer I decided to go ahead and start making payments before interest began accruing.

I actually moved back in with my parents which is hard when you have been out on your own. But I didn t really have a reason to move out. And I was blessed that they actually preferred me to live there because I could help out around the farm they own, baling hay or feeding the horses. Living at my parents place for free was a lot better than having to pay $400 or $500 a month for rent.

Kicking it into gear

About four months into my new job, I picked up a second job, delivering for Pizza Hut, to help pay off my debt. I would start work at the insurance agency at 8:30 a.m. change in the bathroom at 4:50 p.m. get to Pizza Hut by 5, deliver pizzas until about 9:30, get home around 10, then shower, eat, and go to bed.

My monthly take-home pay from the insurance company was about $2,200, and I made about $1,000 at Pizza Hut. After gas, car insurance, tithing to my church, entertainment and food, I could put about $2,000 towards my debt every month.

At that rate, I was projected to pay off my debt in May 2014. But I got a $3,000 refund on my taxes, and paid off the rest of my debt with that.

How I celebrated being debt-free

I made my last payment the first of March, then I went to Florida with some friends two weeks later. It was pretty rewarding after a 10-month battle. I had probably worked 65 to 70 hours a week for seven or eight months. It was exhausting, but it was worth it.

What I d tell someone else in my place

If you have a game plan, you can accomplish your goals. I have an account on Mint.com, that s where I kept my budget. That s a big part of it just seeing your progress and knowing you re getting closer.

Also, have an emergency fund. While I was paying off that debt, I had a small car accident. I was delivering a pizza, and I hit something in someone s driveway. It cost me about $760 to fix the car. But I had a $1,000 emergency fund, which was kind of a buffer that I kept because life happens.

Finally, don t be afraid to move home if you have to. That was a big part of how I got out of debt.

My plan for the future

I quit my Pizza Hut job in April after paying off my debt, and now work at a bank analyzing commercial and agricultural loans, which is more in line with what I wanted to do.

I actually haven t moved out of my parents house yet. Instead I m saving up for a down payment on a house. I m putting away 50% of my take-home income for that, and I should have a down payment by mid-summer. I also started investing. I started a Roth IRA, and I plan to max it out this year.

Staying true to myself

Some people have made the argument, Maybe you shouldn t have paid off the debt so fast because the interest rate is cheaper than what it will be for you to borrow for a home.

That makes sense in my head, but in my heart, I didn t want this hanging over me. I want to be responsible with my money and build a strong foundation.

Check out Money 101 for more resources:

  • I am unable to pay my debts. What can I do?
  • How do I get rid of my credit card debt?
  • How can I improve my credit score?
  • How do I set a budget I can stick to?


Mom-And-Pop Loan Sharks Being Driven Out By Big Credit-Card Companies – The Onion – America s Finest News Source #car #calculator #payment


#loan shark
#

Mom-And-Pop Loan Sharks Being Driven Out By Big Credit-Card Companies

PHILADELPHIA Frankie “The Gorilla” Pistone leans wistfully on his bat. Then, without warning, he picks it up, swinging it furiously toward his deadbeat client’s leg. Just before the Louisville Slugger makes contact with the man’s kneecap, he pulls back, as only a real pro can, leaving the $250-in-the-hole man gasping in fear and relief. “Just get it to me by tomorrow, because next time, I ain’t gonna let up,” Pistone says.

Loan shark Frankie Pistone, whose way of life is endangered by the likes of American Express.

As the thankful man scurries off, Pistone pulls the cigarette out of his mouth and drops it to the ground. “I’m going to miss this,” he says.

Frank Pistone is part of the dying breed known as the American Loan Shark. Not so long ago, the loan shark flourished, offering short-term, high-interest loans to desperate people with nowhere else to turn. Today, however, Pistone and countless others like him are being squeezed out by the major credit-card companies, which can offer money to the down-and-out at lower rates of interest and without the threat of bodily harm.

“It’s a damn shame,” said Joseph Stasi, 61, a South Philadelphia loan shark whose business is down 90 percent from its mid-’70s heyday. “These days, there’s just no place for the small businessman. My kind, we just can’t compete with the Visas and MasterCards of the world.”

“The old customers don’t come ’round here no more,” said Felix Costa, 59, speaking from the Elizabeth, NJ, pool hall that has served as his place of business since 1972. “Time was, a guy who needed a quick $400 for a new refrigerator or some car repairs would come straight to me. Now, he just puts it on his Discover card.”

Though their client lists are dwindling, the loan sharks still have their champions.

“Call me old-fashioned, but I prefer the loan sharks to the credit-card companies,” said Gene Hobson of Detroit. “When I borrow money from Three Knuckles Benny, I know there’s going to be a personal touch, whether it’s a dead animal on my doorstep or one of my kids coming home with a missing toe. The credit cards just don’t give you that sort of individualized attention. And, if you’re late with them, it’s a form letter and maybe maybe an irate call from the accounts-receivable department.”

“With our overhead, we need to charge a 50 percent weekly interest rate just to break even,” said a Chicago loan shark who identified himself only as “Johnny Toothpick.” “We’ve got rent, pay-offs, and switchblade maintenance, not to mention travel expenses. How can we compete with rates as low as 18 to 26 percent a year?”

Continued Toothpick: “These [credit-card companies] are monsters. They care nothing about the damage they’re doing to the American landscape by driving us out. Loan sharking was about more than giving people money and roughing them up when they didn’t come through. It was about ruffling a kid’s hair on the street, helping out a local fella who needed a break, and occasionally letting somebody off easy with just a couple of punches to the gut instead of a glass-filled sock to the face. It’s a unique part of our shared national experience that, once extinct, will never come back.”

With nearly 200,000 new credit-card solicitations going out every week, the loan sharks have little hope of regaining the ground they’ve lost.

“We were going by word of mouth, and we did pretty good around the neighborhood,” Pistone said. “But these credit cards? With direct mail and the Internet, they reach a customer base we can only dream about. In this business climate, how can a small, independent goon possibly compete?”



Federal Student Aid Office Calls Failure A Success – And Hands Out Big Bonuses #cheap #loans


#student financial aid
#

House Republicans blasted the “performance-based” office for lousy work.

Bill Clark via Getty Images

In 1998. the Republican-led Congress and President Bill Clinton’s Democratic administration decided to give the U.S. Department of Education’s financial aid office more freedom to run the student loan program in exchange for it committing to measurable goals.

It may have been a huge mistake.

That’s the takeaway from a congressional hearing Wednesday that featured blistering criticism from government watchdogs. House Republicans and higher education experts directed at the Office of Federal Student Aid and its chief, James Runcie, for what they described as sloppy oversight of loan contractors and for-profit colleges, inconsistent and poor communication to schools, and an agency culture that chafes at criticism and oversight and seemingly rewards failure.

Late payments on student loans have risen in recent years despite generous repayment options, lower joblessness, higher wages and an improving U.S. economy. Federal regulators have found evidence that some of the Federal Student Aid office’s loan contractors have misled borrowers. State attorneys general and the federal Consumer Financial Protection Bureau have sued FSA-overseen schools for allegedly swindling students, conduct that FSA missed or ignored for years.

“It is clear that FSA can not administer this program,” said Rep. Virginia Foxx (R-N.C.). She told Runcie: “You are harming the people you are supposed to be helping, and that has to stop.”

Meanwhile, FSA senior officials continue to give themselves rich salaries and bonuses as the agency in its own reckoning continues to exceed its performance goals. The top bonus last year was $75,000, a 96 percent increase from three years earlier.

More than 100 of FSA’s roughly 1,300 employees in the 2014 fiscal year had salaries above $150,000, according to an online database maintained by the Asbury Park Press. The typical FSA employee is paid more than $100,000 a year, about 33 percent more than the typical federal employee, according to separate data from the U.S. Office of Personnel Management.

“I am concerned about the culture being fostered at FSA,” said Rep. Mark Meadows (R-N.C.). Justin Draeger. president of the National Association of Student Financial Aid Administrators, said the agency needs “cultural changes.” Outgoing Education Secretary Arne Duncan appointed Runcie to lead FSA.

FSA was the federal government’s first “performance-based organization ,” a concept championed by former Vice President Al Gore as a way to bring private sector expertise and management practices into government. The office was given wide latitude in contracting and personnel practices so long as it set quantifiable goals and committed to improving on them every year.

Congress designated the office as a performance-based organization to improve its customer service, reduce costs, and increase accountability. The move was a rebuke to the Education Department’s management of the student loan program.

The problem, experts say, is that FSA largely sets its own goals and defines success without considering the views of others.

Take how it judges progress toward reducing distress among student debtors. Last year. in its annual report to Congress, FSA said 8.1 percent of borrowers were at least 90 days late on their student loans. This year. it was 9.8 percent. But FSA changed how it calculated the delinquency metric, and revised up the 2014 figure to 9.9 percent, allowing it to claim success this year. FSA said the new metric was a “better measure.”

“The problem with self-assessment is even when the department fails they deem it a success,” Draeger said .

Former senior Education Department officials have complained about an inability to get basic information out of FSA. Current officials at various federal agencies have said that they have had difficulty getting data on the performance of federal student loans. FSA doesn’t make public the number of borrowers who annually default on federal student loans.

“I’m extremely concerned about FSA’s ability to serve students, borrowers and taxpayers well,” Foxx said. In remarks directed at Runcie, Foxx added: “You have been given the high honor, in my opinion, of being a performance-based organization and you have not lived up to that.”

Americans with federal student loans who have complained about various issues to the FSA office tasked with advocating for them would probably concur. Debtors gave the Federal Student Aid Ombudsman, an office Congress created to assist borrowers. abysmal ratings in a customer satisfaction survey in the last fiscal year, according to the agency’s annual report .

Borrowers gave the ombudsman group a score of 41 out of 100 on the American Customer Satisfaction Index, a widely used gauge that measures customers’ satisfaction.

FSA encourages its loan contractors to maintain scores in the low 80s. The national average across all economic sectors is 76.

The ombudsman group explained away its atrocious customer satisfaction score in its annual report to Congress by arguing that the survey data suggests that borrowers are rating the ombudsman not on the quality of service, but on the outcome of their case.

Fiscal Year 2015 Annual Report for Federal Student Aid

The ombudsman tried to present a better picture of its horrendous customer satisfaction score by removing its zero ratings from borrowers. That only raised its score to 63. All of FSA s loan contractors received higher ratings from borrowers this past year.

Before Congress on Wednesday, Runcie defended his organization by citing data showing that the agency has reduced waste, cut costs, and reduced the time needed to complete the Free Application for Federal Student Aid, or FAFSA.

Congressional Democrats praised the agency for ensuring that students have their loans and grants in time for the start of classes. They also noted that FSA has had to contend with a significant transformation of the federal student loan program from a bank-based system to one in which the government makes all new loans. And more students are taking out more loans and grants from the department, perhaps straining resources.

Ben Miller, a former Education Department official who now is senior director for postsecondary education at the Center for American Progress, told Congress on Wednesday that FSA had done good work on loan delivery and easing the FAFSA-filing process for students. But he criticized the agency for poor oversight of for-profit colleges and loan contractors whose past performance requires “significant scrutiny.”

Oversight in higher education overall appears to be minimal. Meadows, the North Carolina Republican, acknowledged that Congress itself has failed to hold FSA accountable. The head of FSA has testified before Congress just three times since 2010, he said. “We created this thing and then walked away,” Meadows said .



How to Pay Off a Big Student Loan #refinance #student #loans


#fast student loans
#

This Millennial Paid Off $23,375 in Student Loans in Just 10 Months

“If you have a game plan, you can accomplish your goals,” says 22-year-old Jordan Arnold.

Like many millennials, Jordan Arnold graduated from college five figures deep in student debt. Unlike most of his peers, he paid off all of his loans less than a year after graduation.

Bluffton, Ind.

When he started paying it down: May 2013

When he became debt-free: March 2014

How I started building debt

I always knew I was going to go to college, though I figured I d go to community college for a year or two because it s cheap. But my parents started talking to me about this private Christian school, Indiana Wesleyan in Marion, Ind. I took a visit, and I really liked it. It s only like 3,000 students on campus, so it s a tight-knit community.

Tuition and room and board was about $31,000 a year. And the first year I hadn t applied for federal student aid, since I didn t commit to the college until about 10 days before classes started. I got some scholarships and a grant from my church, though. So, ultimately, I owed approximately $9,000 that first year.

Getting to $23,000

I could only borrow up to $5,500 in subsidized loans from the government each year, so I worked to cover the rest so that I didn t have to take out private loans. I also graduated in three years, which helped.

Still, altogether, I had to take out $15,150 in subsidized federal loans and $2,000 in unsubsidized federal loans. I borrowed another $6,000 from my parents.

My uh-oh moment

In the fall semester of my senior year, I remember being kind of nervous. I knew I had to start paying my debt within six months. It s stressful, when you don t have any money. And I heard all these stories about college students who get out of school, they have all this debt, and they can t find jobs.

Getting my debts paid off was important to me. I didn t want to get the point where I d have to be paying student loans for another 10 years. Right now, I m single. I don t have any dependents that rely on my income. But I didn t want to have these loans over my head when I m trying to feed a family and put a roof over their heads. It s not just about me, it s about my future family.

My first step out of the hole

Luckily, I got a job right out of college at an insurance agency (I had majored in finance). I was on salary, and it was pretty good: $36,000 plus bonuses.

I didn t have to pay my student loans for another four months, but over the summer I decided to go ahead and start making payments before interest began accruing.

I actually moved back in with my parents which is hard when you have been out on your own. But I didn t really have a reason to move out. And I was blessed that they actually preferred me to live there because I could help out around the farm they own, baling hay or feeding the horses. Living at my parents place for free was a lot better than having to pay $400 or $500 a month for rent.

Kicking it into gear

About four months into my new job, I picked up a second job, delivering for Pizza Hut, to help pay off my debt. I would start work at the insurance agency at 8:30 a.m. change in the bathroom at 4:50 p.m. get to Pizza Hut by 5, deliver pizzas until about 9:30, get home around 10, then shower, eat, and go to bed.

My monthly take-home pay from the insurance company was about $2,200, and I made about $1,000 at Pizza Hut. After gas, car insurance, tithing to my church, entertainment and food, I could put about $2,000 towards my debt every month.

At that rate, I was projected to pay off my debt in May 2014. But I got a $3,000 refund on my taxes, and paid off the rest of my debt with that.

How I celebrated being debt-free

I made my last payment the first of March, then I went to Florida with some friends two weeks later. It was pretty rewarding after a 10-month battle. I had probably worked 65 to 70 hours a week for seven or eight months. It was exhausting, but it was worth it.

What I d tell someone else in my place

If you have a game plan, you can accomplish your goals. I have an account on Mint.com, that s where I kept my budget. That s a big part of it just seeing your progress and knowing you re getting closer.

Also, have an emergency fund. While I was paying off that debt, I had a small car accident. I was delivering a pizza, and I hit something in someone s driveway. It cost me about $760 to fix the car. But I had a $1,000 emergency fund, which was kind of a buffer that I kept because life happens.

Finally, don t be afraid to move home if you have to. That was a big part of how I got out of debt.

My plan for the future

I quit my Pizza Hut job in April after paying off my debt, and now work at a bank analyzing commercial and agricultural loans, which is more in line with what I wanted to do.

I actually haven t moved out of my parents house yet. Instead I m saving up for a down payment on a house. I m putting away 50% of my take-home income for that, and I should have a down payment by mid-summer. I also started investing. I started a Roth IRA, and I plan to max it out this year.

Staying true to myself

Some people have made the argument, Maybe you shouldn t have paid off the debt so fast because the interest rate is cheaper than what it will be for you to borrow for a home.

That makes sense in my head, but in my heart, I didn t want this hanging over me. I want to be responsible with my money and build a strong foundation.

Check out Money 101 for more resources:

  • I am unable to pay my debts. What can I do?
  • How do I get rid of my credit card debt?
  • How can I improve my credit score?
  • How do I set a budget I can stick to?


Big Sky Western Bank


#sky loans
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Loan Application

Please complete an application for the loan or line and submit it to any Big Sky Western Bank office. Simply call 587-2922, contact us. or visit any Big Sky Western Bank SM office so you can say YES to yourself today!

Vehicle Loans

Big Sky Western Bank offers lending programs to purchase or refinance a new or used vehicle including cars, trucks, pickups, sport utility vehicles, recreational vehicles, and more.

Personal Loans

When you need extra cash for whatever reason, we can set up a personal loan for you. Collateral for these types of loans can be Big Sky Western Bank certificates of deposit, real estate, and vehicles. (Or can be done unsecured – On Approved Credit -).

Home Equity Lines of Credit and Loans

A Big Sky Western Bank SM Home Equity Loan or Line of Credit is an agreement where the borrower(s) uses the equity in their home as collateral for the loan / line. Home Equity loans and lines are the most popular methods for consumers to finance home improvements, invest in more property, manage their personal cashflow, purchase vehicles or consolidate debt, and usually at a lower interest rate. Home equity loans are for a specific amount and are amortized / paid over a defined period of time. A Home Equity Line of Credit is a revolving line of credit accessed by Online Banking or through checks. You can make major purchases, pay off debt or use for any other purpose to manage your personal cashflow. Generally these loans and lines are the second mortgage on your residence, but can be a first mortgage, or possibly a mortgage on a second home.

A Big Sky Western Bank SM Home Equity Line of Credit could open up a new world of financial independence for you. It’s easy. By borrowing against the equity in your home, you create a line of credit that you control.

For example. If you establish a $15,000 home equity line of credit, you access these funds by simply writing a check. If you write checks totaling $2,500, you only pay interest on that amount. You still have $12,500 available to use. Pay down your line and use these funds over and over again. It’s that simple. like approving your own loan whenever you want.

Why should you obtain a Home Equity Loan or Line?

One of the biggest advantages of a home equity loan or line compared to a vehicle or consumer loan is the possible tax deductibility of the interest paid on these loans — consult a competent tax advisor.

Click here to access our easy to use Online calculators. to help you solve some common financial problems. If you find these calculators useful, be sure to bookmark this page or suggest it as a link to your favorite home page or search engine.



Federal Student Aid Office Calls Failure A Success – And Hands Out Big Bonuses


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House Republicans blasted the “performance-based” office for lousy work.

Bill Clark via Getty Images

In 1998. the Republican-led Congress and President Bill Clinton’s Democratic administration decided to give the U.S. Department of Education’s financial aid office more freedom to run the student loan program in exchange for it committing to measurable goals.

It may have been a huge mistake.

That’s the takeaway from a congressional hearing Wednesday that featured blistering criticism from government watchdogs. House Republicans and higher education experts directed at the Office of Federal Student Aid and its chief, James Runcie, for what they described as sloppy oversight of loan contractors and for-profit colleges, inconsistent and poor communication to schools, and an agency culture that chafes at criticism and oversight and seemingly rewards failure.

Late payments on student loans have risen in recent years despite generous repayment options, lower joblessness, higher wages and an improving U.S. economy. Federal regulators have found evidence that some of the Federal Student Aid office’s loan contractors have misled borrowers. State attorneys general and the federal Consumer Financial Protection Bureau have sued FSA-overseen schools for allegedly swindling students, conduct that FSA missed or ignored for years.

“It is clear that FSA can not administer this program,” said Rep. Virginia Foxx (R-N.C.). She told Runcie: “You are harming the people you are supposed to be helping, and that has to stop.”

Meanwhile, FSA senior officials continue to give themselves rich salaries and bonuses as the agency in its own reckoning continues to exceed its performance goals. The top bonus last year was $75,000, a 96 percent increase from three years earlier.

More than 100 of FSA’s roughly 1,300 employees in the 2014 fiscal year had salaries above $150,000, according to an online database maintained by the Asbury Park Press. The typical FSA employee is paid more than $100,000 a year, about 33 percent more than the typical federal employee, according to separate data from the U.S. Office of Personnel Management.

“I am concerned about the culture being fostered at FSA,” said Rep. Mark Meadows (R-N.C.). Justin Draeger. president of the National Association of Student Financial Aid Administrators, said the agency needs “cultural changes.” Outgoing Education Secretary Arne Duncan appointed Runcie to lead FSA.

FSA was the federal government’s first “performance-based organization ,” a concept championed by former Vice President Al Gore as a way to bring private sector expertise and management practices into government. The office was given wide latitude in contracting and personnel practices so long as it set quantifiable goals and committed to improving on them every year.

Congress designated the office as a performance-based organization to improve its customer service, reduce costs, and increase accountability. The move was a rebuke to the Education Department’s management of the student loan program.

The problem, experts say, is that FSA largely sets its own goals and defines success without considering the views of others.

Take how it judges progress toward reducing distress among student debtors. Last year. in its annual report to Congress, FSA said 8.1 percent of borrowers were at least 90 days late on their student loans. This year. it was 9.8 percent. But FSA changed how it calculated the delinquency metric, and revised up the 2014 figure to 9.9 percent, allowing it to claim success this year. FSA said the new metric was a “better measure.”

“The problem with self-assessment is even when the department fails they deem it a success,” Draeger said .

Former senior Education Department officials have complained about an inability to get basic information out of FSA. Current officials at various federal agencies have said that they have had difficulty getting data on the performance of federal student loans. FSA doesn’t make public the number of borrowers who annually default on federal student loans.

“I’m extremely concerned about FSA’s ability to serve students, borrowers and taxpayers well,” Foxx said. In remarks directed at Runcie, Foxx added: “You have been given the high honor, in my opinion, of being a performance-based organization and you have not lived up to that.”

Americans with federal student loans who have complained about various issues to the FSA office tasked with advocating for them would probably concur. Debtors gave the Federal Student Aid Ombudsman, an office Congress created to assist borrowers. abysmal ratings in a customer satisfaction survey in the last fiscal year, according to the agency’s annual report .

Borrowers gave the ombudsman group a score of 41 out of 100 on the American Customer Satisfaction Index, a widely used gauge that measures customers’ satisfaction.

FSA encourages its loan contractors to maintain scores in the low 80s. The national average across all economic sectors is 76.

The ombudsman group explained away its atrocious customer satisfaction score in its annual report to Congress by arguing that the survey data suggests that borrowers are rating the ombudsman not on the quality of service, but on the outcome of their case.

Fiscal Year 2015 Annual Report for Federal Student Aid

The ombudsman tried to present a better picture of its horrendous customer satisfaction score by removing its zero ratings from borrowers. That only raised its score to 63. All of FSA s loan contractors received higher ratings from borrowers this past year.

Before Congress on Wednesday, Runcie defended his organization by citing data showing that the agency has reduced waste, cut costs, and reduced the time needed to complete the Free Application for Federal Student Aid, or FAFSA.

Congressional Democrats praised the agency for ensuring that students have their loans and grants in time for the start of classes. They also noted that FSA has had to contend with a significant transformation of the federal student loan program from a bank-based system to one in which the government makes all new loans. And more students are taking out more loans and grants from the department, perhaps straining resources.

Ben Miller, a former Education Department official who now is senior director for postsecondary education at the Center for American Progress, told Congress on Wednesday that FSA had done good work on loan delivery and easing the FAFSA-filing process for students. But he criticized the agency for poor oversight of for-profit colleges and loan contractors whose past performance requires “significant scrutiny.”

Oversight in higher education overall appears to be minimal. Meadows, the North Carolina Republican, acknowledged that Congress itself has failed to hold FSA accountable. The head of FSA has testified before Congress just three times since 2010, he said. “We created this thing and then walked away,” Meadows said .



Mom-And-Pop Loan Sharks Being Driven Out By Big Credit-Card Companies – The Onion – America s Finest News Source


#loan shark
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Mom-And-Pop Loan Sharks Being Driven Out By Big Credit-Card Companies

PHILADELPHIA Frankie “The Gorilla” Pistone leans wistfully on his bat. Then, without warning, he picks it up, swinging it furiously toward his deadbeat client’s leg. Just before the Louisville Slugger makes contact with the man’s kneecap, he pulls back, as only a real pro can, leaving the $250-in-the-hole man gasping in fear and relief. “Just get it to me by tomorrow, because next time, I ain’t gonna let up,” Pistone says.

Loan shark Frankie Pistone, whose way of life is endangered by the likes of American Express.

As the thankful man scurries off, Pistone pulls the cigarette out of his mouth and drops it to the ground. “I’m going to miss this,” he says.

Frank Pistone is part of the dying breed known as the American Loan Shark. Not so long ago, the loan shark flourished, offering short-term, high-interest loans to desperate people with nowhere else to turn. Today, however, Pistone and countless others like him are being squeezed out by the major credit-card companies, which can offer money to the down-and-out at lower rates of interest and without the threat of bodily harm.

“It’s a damn shame,” said Joseph Stasi, 61, a South Philadelphia loan shark whose business is down 90 percent from its mid-’70s heyday. “These days, there’s just no place for the small businessman. My kind, we just can’t compete with the Visas and MasterCards of the world.”

“The old customers don’t come ’round here no more,” said Felix Costa, 59, speaking from the Elizabeth, NJ, pool hall that has served as his place of business since 1972. “Time was, a guy who needed a quick $400 for a new refrigerator or some car repairs would come straight to me. Now, he just puts it on his Discover card.”

Though their client lists are dwindling, the loan sharks still have their champions.

“Call me old-fashioned, but I prefer the loan sharks to the credit-card companies,” said Gene Hobson of Detroit. “When I borrow money from Three Knuckles Benny, I know there’s going to be a personal touch, whether it’s a dead animal on my doorstep or one of my kids coming home with a missing toe. The credit cards just don’t give you that sort of individualized attention. And, if you’re late with them, it’s a form letter and maybe maybe an irate call from the accounts-receivable department.”

“With our overhead, we need to charge a 50 percent weekly interest rate just to break even,” said a Chicago loan shark who identified himself only as “Johnny Toothpick.” “We’ve got rent, pay-offs, and switchblade maintenance, not to mention travel expenses. How can we compete with rates as low as 18 to 26 percent a year?”

Continued Toothpick: “These [credit-card companies] are monsters. They care nothing about the damage they’re doing to the American landscape by driving us out. Loan sharking was about more than giving people money and roughing them up when they didn’t come through. It was about ruffling a kid’s hair on the street, helping out a local fella who needed a break, and occasionally letting somebody off easy with just a couple of punches to the gut instead of a glass-filled sock to the face. It’s a unique part of our shared national experience that, once extinct, will never come back.”

With nearly 200,000 new credit-card solicitations going out every week, the loan sharks have little hope of regaining the ground they’ve lost.

“We were going by word of mouth, and we did pretty good around the neighborhood,” Pistone said. “But these credit cards? With direct mail and the Internet, they reach a customer base we can only dream about. In this business climate, how can a small, independent goon possibly compete?”