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What is Predictive Modeling? Predictive Analytics Today

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What is Predictive Modeling ?

What is Predictive Modeling ?

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Predictive modeling is the process of creating, testing and validating a model to best predict the probability of an outcome. A number of modeling methods from machine learning, artificial intelligence, and statistics are available in predictive analytics software solutions for this task. The model is chosen on the basis of testing, validation and evaluation using the detection theory to guess the probability of an outcome in a given set amount of input data. Models can use one or more classifiers in trying to determine the probability of a set of data belonging to another set. The different models available on the Modeling portfolio of predictive analytics software enables to derive new information about the data and to develop the predictive models. Each model has its own strengths and weakness and is best suited for particular types of problems.

Predictive Analytics Value Chain

A model is reusable and is created by training an algorithm using historical data and saving the model for reuse purpose to share the common business rules which can be applied to similar data, in order to analyze results without the historical data, by using the trained algorithm.

Most of the predictive modeling software solutions has the capability to export the model information into a local file in industry standard Predictive Modeling Markup Language, (PMML) format for sharing the model with other PMML compliant applications to perform analysis on similar data.

Business process on Predictive Modeling

1. Creating the model. Software solutions allows you to create a model to run one or more algorithms on the data set.

2. Testing the model. Test the model on the data set. In some scenarios, the testing is done on past data to see how best the model predicts.

3. Validating the model. Validate the model run results using visualization tools and business data understanding.

4. Evaluating the model. Evaluating the best fit model from the models used and choosing the model right fitted for the data.

Predictive modeling process

The process involve running one or more algorithms on the data set where prediction is going to be carried out. This is an iterative processing and often involves training the model, using multiple models on the same data set and finally arriving on the best fit model based on the business data understanding.

1.Predictive models :The models in Predictive models analyze the past performance for future predictions.

2.Descriptive models. The models in descriptive model category quantify the relationships in data in a way that is often used to classify data sets into groups.

3.Decision models. The decision models describe the relationship between all the elements of a decision in order to predict the results of decisions involving many variables.

Algorithms perform data mining and statistical analysis in order to determine trends and patterns in data. The predictive analytics software solutions has built in algorithms such as regressions, time series, outliers, decision trees, k-means and neural network for doing this. Most of the software also provide integration to open source R library.

1. Time Series Algorithms which perform time based predictions. Example Algorithms are Single Exponential Smoothing, Double Exponential Smoothing and Triple Exponential Smoothing.

2. Regression Algorithms which predicts continuous variables based on other variables in the dataset. Example algorithms are Linear Regression, Exponential Regression, Geometric Regression, Logarithmic Regression and Multiple Linear Regression.

3. Association Algorithms which Finds the frequent patterns in large transactional dataset to generate association rules. Example algorithms are Apriori

4. Clustering Algorithms which clustor observations into groups of similar Groups. Example algorithms are K-Means. Kohonen, and TwoStep.

5. Decision Trees Algorithms classify and predict one or more discrete variables based on other variables in the dataset. Example algorithms are C 4.5 and CNR Tree

6. Outlier Detection Algorithms detect the outlying values in the dataset. Example algorithms are Inter Quartile Range and Nearest Neighbour Outlier

7. Neural Network Algorithms does the forecasting, classification, and statistical pattern recognition. Example algorithms are NNet Neural Network and MONMLP Neural Network

8.Ensemble models are a form of Monte Carlo analysis where multiple numerical predictions are conducted using slightly different initial conditions.

9.Factor Analysis deals with variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. Example algorithms are Maximum likelihood algorithm.

10.Naive Bayes are probabilistic classifier based on applying Bayes theorem with strong (naive) independence assumptions.

11.Support vector machines are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.

12.Uplift modeling. models the incremental impact of a treatment on an individual s behavior.

13.Survival analysis are analysis of time to events.

Features in Predictive Modeling

1) Data Analysis and manipulation. Tools for data analysis, create new data sets, modify, club, categorize, merge and filter data sets.

2) Visualization. Visualization features includes interactive graphics, reports.

3) Statistics : Statistics tools to create and confirm the relationships between variables in the data. Statistics from different statistical software can be integrated to some of the solutions.

4) Hypothesis testing. Creation of models, evaluation and choosing of the right model.

Predictive Analytics Software

You may also like to review the predictive analytics free software list :
Predictive Analytics Freeware Software

You may also like to review the predictive analytics software API :
Predictive Analytics Software API

You may also like to review the top predictive analytics proprietary software list:
Top Predictive Analytics proprietary Software

More Information on Predictive Analysis Process

Predictive Analytics Process Flow

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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


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

Latest Articles



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The University reserves the right to make changes to programme tuition fees and other charges each year (i.e. post the first year of entry to the course).
It may also make changes to programmes and their curriculum and structure, entry requirements and methods of delivery, and to discontinue, merge or combine programmes, both before and after a student’s admission to the University, if such action is reasonably considered necessary by the University.

If the University discontinues the programme prior to the student commencing the first term of study, the student may either:
1. withdraw from the University without any liability for fees, including the application fee, or
2. transfer to another programme offered by the University, subject to suitable qualifications and meeting entry requirements, availability of places and the Dean’s approval.

Programme tuition fees for the year of entry into a programme can be viewed at:

When accepting an offer of a place on a programme with the University and School, students accept the University’s Terms and Conditions of Admission. They can also be viewed at:

Student profile

This programme is suitable for graduates from technical disciplines such as, engineering, computer science and mathematics with a talent for and interest in finding mathematical or computer solutions to business problems. It is also suitable for business graduates who have studied Management Information Systems (MIS) subjects. The student profile also includes people with practical experience, who often use the programme to redirect their career.

The mix of full and part-time students from different backgrounds, working together on projects, makes the interaction between students an important and worthwhile part of the learning experience.

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Democratizing Decision Making with Analytics

How important is analytics in helping organizations understand, visualize, and assess all the complex data coming out of their business processes, broader organization, and customers? Listen to Ray Wang from Constellation as he makes sense of all the data that organizations are now trying to work with.

The Value of Data and Analytics in Digital Transformation

Digital transformation has become a broad-level initiative at many companies. A key part of this investment is being focused on data and analytics. SAP recently posed several questions to Dan Vesset, group vice president of IDC’s Analytics and Information Management research, on behalf of SAP’s customers.

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Risk analysis, from the ground up! Oxxford can identify and segment over 22 million businesses and over 200 million residential locations with both financial and environmental risk indicators.


Only people grow business. Only excellence keeps it. Gaining a client s confidence over the long term is the result of performance, of service, and of offering a superior product. ultimately of working together. Everyone at Oxxford takes these beliefs seriously and our mission statement is in our everyday performance: To deliver consistently high quality in all the services we provide in an innovative and functional manner. To develop and maintain positive, long term relationships with our clients. –To render the highest standards in professionalism and integrity in our industry.

Recent News

April 2013 Update

Thursday, July 7, 2016

Oxxford UCIS and Dun and Bradstreet now offer select financial, insurance, and energy information through D Bs Data Exchange program. Contact your D B rep for further detailed information that will give your business one more advantage over competitors and enhance your cross-selling capabilities.

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Oxxford has upgraded their entire commercial and residential files with more specific energy consumption actionable data – at the site or for the entire commercial relationship. Service provider geography; energy usage factors; green scores and more are available for the entire U.S. Oxxford recently attained the deans list for vendors providing services to DTE Energy – providing secondary demographic, segmentation, and other services to DTE s customer relationship and analysis groups.

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Marketing Analytics

Marketing analytics comprises the processes and technologies that enable marketers to evaluate the success of their marketing initiatives by measuring performance (e.g. blogging versus social media versus channel communications) using important business metrics, such as ROI, marketing attribution and overall marketing effectiveness. In other words, it tells you how your marketing programs are really performing.

Marketing analytics gathers data from across all marketing channels and consolidates it into a common marketing view. From this common view, you can extract analytical results that can provide invaluable assistance in driving your marketing efforts forward.

Why marketing analytics is important

Over the years, as businesses expanded into new marketing categories, new technologies were adopted to support them. Because each new technology was typically deployed in isolation, the result was a hodgepodge of disconnected data environments.

Consequently, marketers often make decisions based on data from individual channels (website metrics, for example), not taking into account the entire marketing picture. Social media data alone is not enough. Web analytics data alone is not enough. And tools that look at just a snapshot in time for a single channel are woefully inadequate. Marketing analytics, by contrast, considers all marketing efforts across all channels over a span of time – which is essential for sound decision making and effective, efficient program execution.

What you can do with marketing analytics

With marketing analytics, you can answer questions like these:

  • How are our marketing initiatives performing today? How about in the long run? What can we do to improve them?
  • How do our marketing activities compare with our competitors’? Where are they spending their time and money? Are they using channels that we aren’t using?
  • What should we do next? Are our marketing resources properly allocated? Are we devoting time and money to the right channels? How should we prioritize our investments for next year?

Marketing Insights

Explore insights from marketing movers and shakers on a variety of timely topics.

To reap the greatest rewards from marketing analytics, follow these three steps:

  1. Use a balanced assortment of analytic techniques.
  2. Assess your analytic capabilities, and fill in the gaps.
  3. Act on what you learn.

Use a balanced assortment of analytic techniques

To get the most benefit from marketing analytics, you need an analytic assortment that is balanced – that is, one that combines techniques for:

  • Reporting on the past. By using marketing analytics to report on the past, you can answer such questions as: Which campaign elements generated the most revenue last quarter? How did email campaign A perform against direct mail campaign B? How many leads did we generate from blog post C versus social media campaign D?
  • Analyzing the present. Marketing analytics enables you to determine how your marketing initiatives are performing right now by answering questions like: How are our customers engaging with us? Which channels do our most profitable customers prefer? Who is talking about our brand on social media sites, and what are they saying?
  • Predicting and/or influencing the future. Marketing analytics can also deliver data-driven predictions that you can use to influence the future by answering such questions as: How can we turn short-term wins into loyalty and ongoing engagement? How will adding 10 more sales people in under-performing regions affect revenue? Which cities should we target next using our current portfolio?

Assess your analytic capabilities, and fill in the gaps

Marketing organizations have access to a lot of different analytic capabilities in support of various marketing goals, but if you’re like most, you probably don’t have all your bases covered. Assessing your current analytic capabilities is a good next step. After all, it’s important to know where you stand along the analytic spectrum, so you can identify where the gaps are and start developing a strategy for filling them in.

For example, a marketing organization may already be collecting data from online and POS transactions, but what about all the unstructured information from social media sources or call-center logs? Such sources are a gold mine of information, and the technology for converting unstructured data into actual insights that marketers can use exists today. As such, a marketing organization may choose to plan and budget for adding analytic capabilities that can fill that particular gap. Of course, if you’re not quite sure where to start, well, that’s easy. Start where your needs are greatest, and fill in the gaps over time as new needs arise.

Act on what you learn

There is absolutely no real value in all the information marketing analytics can give you – unless you act on it. In a constant process of testing and learning, marketing analytics enables you to improve your overall marketing program performance by, for example:

  • Identifying channel deficiencies.
  • Adjusting strategies and tactics as needed.
  • Optimizing processes.

Without the ability to test and evaluate the success of your marketing programs, you would have no idea what was working and what wasn’t, when or if things needed to change, or how. By the same token, if you use marketing analytics to evaluate success, but you do nothing with that insight, then what is the point?

Applied holistically, marketing analytics allows for better, more successful marketing by enabling you to close the loop as it relates to your marketing efforts and investments. For example, marketing analytics can lead to better lead nurturing and management, which leads to more revenue and greater profitability. By more effectively managing leads and being able to tie those leads to sales – which is known as closed-loop marketing analytics – you can see which specific marketing initiatives are contributing to your bottom line.

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Mobile applications are a new important communication channel and marketing tool. This must be analyzed to get the best performance and to understand if your investment in App development is positive or not.
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ShinyStat™ Video Analytics allows you to measure informations linked to the videos fruition and multimedia content published on you own web site or embedded on others ones thanks to the specific metric and tools for “viral marketing video success” such as users distribution, 2.0 actions and Video Geo audience.
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ShinyStat™ Web Analytics allows to get these informations in real time, to create automatic channels, to analyze detailed conversions and marketing campaign progress (CPC, display, video, mobile).

10 Most Popular Analytics Tools in Business #marketing #analytics #tools


Update 2017: This article on most popular analytics tools was originally written more than 5 years ago. It has been updated a couple of times since then. However, the industry has changed rapidly in this time and the article continues to generate a lot of interest.

I felt it was time now to do a complete revamp and share the latest and most updated list of analytics tools with the readers.You can read the original article here .

The analytics industry has seen a paradigm shift in how and which analytics tools are used. We have seen the demise of the SAS monopoly, which had for long been the case, giving way to increased adoption of open source tools.

I have worked on SAS for many years and it is a fantastic tool. However, the high pricing of the software was always a deterrent. Most small and medium companies could not afford the tool, and neither did it make sense for them to invest in it as their analysis needs were different. The companies that have and could use it, often ended up paying more for the tool than the analyst who worked on the tool and this was not an optimal scenario.

If we needed analytics to work for us the way we wanted it to, there was a terrible need for industry-wide adoption of cheaper tools, and better if they were free.

In line with the growing importance of open source tools in the field of analytics, I have decided to switch the order of listing the top tools. I will start with the most popular open source tools this time and then move on to the commercial or paid tools.

Open Source Analytics Tools

Here are the 5 most popular open source analytics tools:

R – R is now the most popular analytics tool in the industry. It has surpassed SAS in usage and is now the tool of choice even for companies that can easily afford SAS. Over the years, R has become a lot more robust. It handles large data sets much better than it used to, say even a decade earlier. It has also become a lot more versatile.

1800 new packages were introduced in R between April 2015 and April 2016. The total number of R packages is now over 8000. There are some concerns about the sheer number of packages but this has certainly added a lot to R’s capabilities. R also integrates very well with many Big Data platforms which has contributed to its success.

Python – Python has been a favorite of programmers for long. This is mainly because it’s an easy to learn language that is also quite fast. However, it developed into a powerful analytics tool with the development of analytical and statistical libraries like numpy, scipy etc. Today it offers a comprehensive coverage of statistical and mathematical functions.

Increasingly, we are seeing programmers and other tech folks moving into analytics. Most of these guys are already familiar with Python and therefore it has become a tool of choice for many data scientists.

Apache Spark – Spark is another open source processing engine that is built with a focus on analytics, especially on unstructured data or huge volumes of data. Spark has become tremendously popular in the last couple of years. This is because of various reasons – easy integration with the Hadoop ecosystem being one of them. Spark has its own machine learning library which makes it ideal for analytics as well.

Apache Storm – Storm is the Big Data tool of choice for moving data or when the data comes in as a continuous stream. Spark works on static data. Storm is ideal for real time analytics or stream processing.

PIG and HIVE – Pig and Hive are integral tools in the Hadoop ecosystem that reduce the complexity of writing MapReduce queries. Both these languages are like SQL (Hive more so than Pig). Most companies that work with Big Data and leverage the Hadoop platform use Pig and/or Hive.

Commercial Analytics Tools

Now, let’s take a look at the 5 most popular paid analytics tools:

SAS – SAS continues to be widely used in the industry. Some flexibility on pricing from the SAS Institute has helped its cause. SAS continues to be a robust, versatile and easy to learn tool. SAS has added tons of new modules. Some of the specialized modules that have been added in the recent past are SAS analytics for IOT, SAS Anti-money Laundering, and SAS Analytics Pro for Midsize Business.

Tableau – Tableau is an easy to learn tool that does an effective job of slicing and dicing your data and creating great visualizations and dashboards. Tableau can create better visualizations than Excel and can most definitely handle much more data than Excel can. If you want interactivity in your plots, then Tableau is surely the way to go.

Excel – Excel is of course the most widely used analytics tool in the world. I have seldom come across a data scientist who does not use Excel. Whether you are an expert in R or Tableau, you will still use Excel for the grunt work. Non-analytics professionals will usually not have access to tools like SAS or R on their machines. But everyone has Excel. Excel becomes vital when the analytics team interfaces with the business steam.

QlikView – Qlikview and Tableau are essentially vying for the top spot amongst the data visualization giants. Qlikview is supposed to be slightly faster than Tableau and gives experienced users a bit more flexibility. Tableau has a more intuitive GUI and is easier to learn.

Splunk – Splunk is more popular than some of the more known names like Cloudera and Hortonworks. It started as a ‘Google for log files’ which means its primary use was to process machine log files data. It has now become much more than that. Splunk has great visualization options and a web interface makes it easy to use.

Top Companies Using the Most Popular Analytics Tools

This is the new and updated list of the most popular tools in the analytics industry. If you are interested in learning these tools, here is what you need to do.

To learn SAS, R, and Python, head to our Data Scientist Specialization . Spark, Storm, and other Big Data technologies are covered in the Big Data Analyst Specialization . We have a course on Analytics with Excel as well that will help you take your step into analytics.

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Big Data Analytics Training in Hyderabad

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Intended Audience

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Master of Professional Studies in Data Analytics – Business Analytics Option

Online Business Analytics Degree

Learn how to make informed, data-driven business decisions with Penn State’s 30-credit online Master of Professional Studies in Data Analytics with an option in Business Analytics. As a student in this data analytics degree program, you can acquire the business intelligence training needed to work in positions such as business analyst, analytic system designer, or data scientist. Learn how to apply big data analytics, data mining techniques, and predictive analytics to meet your organization’s business objectives and leverage competitive advantage.

The Penn State Difference

Delivered in a strong partnership between four colleges and four academic departments from across the University, the business analytics degree program offers you the opportunity to benefit from the expertise and unique perspectives of faculty with diverse backgrounds. With their broad spectrum of experiences, our faculty can teach you to collect, classify, analyze, and model data at large and ultra-large scales and across domains using statistics, computer science, machine learning, and software engineering.

Business Analytics Curriculum: More than Predictive Analytics

The focus of the business analytics degree curriculum is to explore and analyze big data to support data-driven business decisions through the complete spectrum of analytics activities: descriptive (what happened), diagnostic (why it happened), predictive (what will happen), and prescriptive (what should happen). In addition to learning how to use optimization and forecasting techniques, you can take data management courses, and learn about data mining for business intelligence. You can gain a practical skill set to perform tasks in various areas of business, such as marketing, supply chains, operations, forensics, and risk.

You can also choose the data analytics base program that focuses on database design and big data storage technologies.

Start with a Business Analytics Certificate and Step Up to the Master’s Degree

The Graduate Certificate in Business Analytics consists of three courses from the MPS in Data Analytics – Business Analytics option. You can begin by pursuing the certificate, and then choose to continue and apply to the master’s degree program. If you apply and are accepted to the master’s degree program, you can apply the 9 credits from the certificate toward the degree.

Related Programs

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M.S. in Business Analytics Admissions

It is expected that most successful applicants will have either (i) an undergraduate degree in a quantitative discipline such as mathematics, economics, statistics, physics, engineering, or computer science, or (ii) five or more years of work experience in the public or private sector. Students with a non-quantitative undergraduate degree and less than five years of work experience will be considered on an exceptional basis if (1) their work experience is in the area of business analytics or marketing research or (2) they have developed a strong quantitative background through additional coursework or relevant work experience. The program will only admit students who have a solid foundation in quantitative methods, particularly knowledge of probability and statistics and the use of statistical applications such as R, SAS, STATA, and Matlab.

Additionally, each student must demonstrate evidence of programming proficiency from prior college coursework, professional experience, a continuing education program, Coursera, or EdX. If you are new to R we recommend you also complete a course on programming in R from Data Camp or Coursera .

The Rady School admissions team reviews applications as they are received and makes decisions on a rolling basis. The number of seats is limited so applying early increases your likelihood of gaining admission.

Application Checklist

Fall 2018 Deadlines
November 1, 2017*
December 1, 2017
February 1, 2018
April 1, 2018**
June 1, 2018

*Early decision deadline
**Final deadline for international applicants

A completed MS in Business Analytics application will include the following components:

1. Application Form

2. Transcripts

You may provide academic transcripts in two ways: upload a PDF or Word document of your transcripts directly into the online application, or you can have your academic records sent to us from the institutions at which you studied. If you upload your documents and are offered admission, official transcripts of all your previous academic work will be required to verify the information provided in the uploaded files.

For international applicants ONLY: All international transcripts require an official translation into English by a certified translator. International applicants must submit a certified copy of their diploma indicating the degree received.

3. Resume or CV

Submit your up-to-date resume or CV.

4. Quantitative Background

Provide details regarding your experience with computational programming (for example, C++) and with statistical, econometrics and mathematical applications and tools (for example, SAS, Stata, MatLab, R, S-Plus, Mathematica).

5. Recommendations

All applicants are required to submit two recommendations. It is highly encouraged that recommendations be submitted online. However, the recommendation form can be downloaded and signed, sealed and sent to the Rady School directly. It is most important that letters of recommendation be completed by individuals who are in a position to analyze your ability and academic promise.

6. Statement of Purpose

Your Statement of Purpose should be no longer than 500 words, double-spaced, 12-point font. The Rady Mission Statement: The Rady School of Management develops ethical and entrepreneurial leaders who make a positive impact in the world through innovation, collaboration and knowledge. Please choose two of the following words from our mission statement: entrepreneurial, innovation, collaboration, knowledge. Discuss how the two words you have chosen have played a role in your academic and/or professional career.

7. GMAT or GRE Scores

The MS in Business Analytics program accepts either GMAT or GRE scores. You may submit your application before taking the GMAT or GRE, but the final admissions decision will not be made until we have received your scores. Waiver considered for those with an M.D. or Ph.D. in a quantitative discipline, or Master’s degree in a quantitative discipline with at least 8 years of relevant work experience.

  • The code for the GMAT is 3NS-ZK-33.
  • The code for the GRE is 4836.

8. TOEFL, IELTS or PTE Score (for international applicants only)

  • TOEFL scores are to be sent from ETS to institutional code 4836-02. Minimum scores for TOEFL: Paper 550; Internet 80.
  • IELTS scores are to be sent directly from the British Council or test-taking institution. The minimum IELTS score is 7. Select University of California, San Diego as the institution and have them send scores directly to our Graduate Admissions office at: Rady School of Management, Master of Science in Business Analytics Program, UC San Diego, 9500 Gilman Drive #0553, La Jolla, CA 92093-0553
  • PTE test takers must select the University of California, San Diego as a score recipient, then submit a copy of your scores directly to our Graduate Admissions office at: Rady School of Management, UC San Diego, 9500 Gilman Drive #0553, La Jolla, CA 92093-0553. Include your registration ID number and exam date on your application. Official scores will be verified online. The minimum PTE score is 65.

9. Application Fee

The Summer 2017 application fee is $105 for US citizens or Permanent Residents; $125 for international applicants. The non-refundable fee is payable by credit card through the online application. You may also pay by check, following the instructions in the online application. If you choose to pay by check, please note that your application will not be processed until your check has been received.

After You Apply

The Rady School admissions team reviews applications as they are received and makes decisions on a rolling basis. The number of seats is limited so applying early increases your likelihood of gaining admission.

  • After you submit your application, you will receive confirmation and instructions for further action, if necessary.
  • Once you have submitted a complete application, it will be reviewed to determine if the admissions committee would like to schedule an interview.
  • Interviews are required for admission and are by invitation only. We prefer to conduct interviews in person to get to know you better and learn more about your background and goals. For out-of-state candidates, arrangements can be made for Skype interviews.

If you have questions about the application process, please contact the Rady Admissions Office .

What is Google Analytics? Definition from #google #app #analytics


Google Analytics

Google Analytics is a free Web analytics service that provides statistics and basic analytical tools for search engine optimization (SEO ) and marketing purposes. The service is available to anyone with a Google account. Google bought Urchin Software Corporation in April 2005 and used that company’s Urchin on Demand product as the basis for its current service.

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Google Analytics features include:

• Data visualization tools including a dashboard. scorecards and motion charts, which display changes in data over time.

• Segmentation for analysis of subsets, such as conversions.

• Email-based sharing and communication.

• Integration with other Google products, such as AdWords, Public Data Explorer and Website Optimizer.

Google Analytics is geared toward small and medium-sized retail websites. The service has limitations that make it less suited to more complex websites and larger enterprises. For example, the system collects data through a JavaScript page tag inserted in the code of pages the user wants to collect data on. The page tag functions as a Web bug to gather visitor information. However, because it’s reliant on cookies. the system can’t collect data for users who have disabled them. Google also uses sampling in its reports rather than analyzing all available data.

Furthermore, some security experts have raised concerns about privacy issues in Google Analytics. Through the Google Analytics Dashboard, users can collect information on people whose websites link to social networking sites such as Facebook and Twitter.

There are a number of commercial products for enterprises that require more advanced Web analytics, including those from Omniture, Webtrends, IBM ’s Cognos product line and Oracle WebCenter.

This was last updated in November 2011

Continue Reading About Google Analytics

AnonymousUser – 4 Jul 2013 10:20 AM

Google Analytics is the most broadly utilised website statistics service, actually in use on around 55% of the 10,000 most well liked websites.Google Analytics is a service suggested by Google that generates comprehensive statistics about a website’s traffic and traffic sources and assesses alterations and sales.

fernandolopes9 – 11 Feb 2014 5:45 AM

Really Google Analytics is a great SEO tool offered by major search engine to check out the useful statistics of websites. It is easy to use and can determine everything ranging from the conversion rates to bounce rates of your website. Many SEO companies like follow this tool while promoting websites online. It help companies to analyze which marketing activity is capturing the best traffic to your site and help to get positive results on search engines.

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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).

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