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


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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

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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.