The University of Texas

Data Analytics Certificate Program

Program Details

Courses

The following courses, taken sequentially, compose the Data Analytics & Big Data Program.

In this course you will be working under Blackwell's Chief Technology Officer Danielle Sherman, as a member of the Blackwell Electronics eCommerce Team. Blackwell Electronics has been a successful consumer electronics retailer in the southeastern United States for over 40 years. Last year, the company launched an eCommerce website. Your job is to use data mining and machine-learning techniques to investigate the patterns in customer sales data and provide insight into customer buying trends and preferences. The inferences you draw from the patterns in the data will help the business make data-driven decisions about sales and marketing activities.

First you will install the RapidMiner Data Science Platform and use it to understand the relationship between customer demographics and purchasing behavior. Next, you will use Regression and Classification machine learning algorithms in RapidMiner to assist you with proposing business decisions based on your analysis. Finally, you will present to management, explaining your insights and suggestions for data mining process improvements.


In this course, you will continue to work with Danielle Sherman, the Chief Technology Officer at Blackwell Electronics. Blackwell Electronics is a successful consumer electronics retailer with both bricks & mortar stores in the southeastern United States and an eCommerce site. They have recently begun to leverage the data collected from online and in-store transactions to gain insight into their customers' purchasing behavior. Your job is to extend their application of data mining methods to develop predictive models and you'll be using R to accomplish this. In this course, you will use machine learning methods to predict which brand of computer products Blackwell customers prefer based on customer demographics collected from a marketing survey, and then you will go on to determine associations between products that be used to drive sales-oriented initiatives such as recommender systems like the ones used by Amazon and other eCommerce sites. Finally, you will present to management, explaining your insights and suggestions for data mining process improvements.


Increasingly, technology companies are applying data analytics techniques to the masses of data generated by devices such as smart phones, appliances, vehicles, electric meters, et cetera. The ability to deal with data of these types will prove to be a high-demand skill for data analysts as applications of commercial interest increasingly go beyond business intelligence. The skills you will learn are applicable to a wide variety of data analytics projects and will enable you to start working on problems that benefit from the application of machine learning and statistical analysis techniques to sensor (and other) data.

In this course, you'll be working for an "Internet of Things" technology start-up that wants to use Data Analytics to solve two difficult problems in the physical world:

  1. Smart energy usage: Modeling patterns of energy usage by time of day and day of the year in a typical residence whose electrical system is monitored by multiple sub-meters.
  2. Indoor locationing: Determining a person's physical position in a multi-building indoor space using wifi fingerprinting.

You'll use R to create visualizations, and then you will generate descriptive statistics and predictive models using both statistical classifiers and linear regression techniques. Finally, you'll present the results to the start-up's management, explaining strengths and weaknesses of the approaches you implemented and making suggestions for further improvement.


In this course, module, you will be working as a data analyst for Alert Analytics, a data analytics consulting firm. On your first project for the firm, Alert's founding partner and SVP Michael Ortiz has asked you to take over for a recently-transferred analyst who has been working on a big data project for Helio, a smart phone and tablet app developer. Helio is working with a government health agency to create a suite of smart phone medical apps for use by aid workers in developing countries. The government agency will be providing workers with technical support services, but they need to limit the support to a single model of smart phone and operating system. To select the most appropriate device, Helio has engaged Alert Analytics to conduct a broad-based web sentiment analysis to gain insight into the attitudes toward the devices. Your job is to conduct this analysis.

First, you will set up and become familiar with the Amazon Web Services (AWS) computing environment. Next, you will use the AWS Elastic Map Reduce (EMR) platform to run a series of Hadoop Streaming jobs that will collect large amounts of smart phone-related web pages from a massive repository of web data called Common Crawl. Once this data has been gathered, you will then compile it into a data matrix where you can then use a machine learning to develop a predictive model that will label the data with the websites' sentiment toward the devices. Finally, you will prepare a presentation and summary of your findings from the analysis for an executive audience and report on lessons learned during the process.


In this course, you are a Data Scientist for Credit One, a third-party credit rating authority that provides retail customer credit approval services to businesses.

Credit One has tasked you with examining current customer demographics to better understand what traits might relate to whether or not a customer is likely to default on their current credit obligations. Understanding this is vital to the success of Credit One because their business model depends on customers paying their debts.

Your job as a Data Scientist will be to identify which customer attributes relate significantly to customer default rates and to build a predictive model that Customer One can use to better classify potential customers as being 'at-risk', compared to previously implemented models. You will use ensemble machine learning classification methods in Python for this task.

You will then go on to complete a capstone project of your own choice, again using Python.


*Though the first two courses are set in a retail/consumer behavior context, students are really learning foundational data analytics techniques that can be applied to a wide range of problems.

Realistic Prerequisites and Expectations

The Data Analytics Certificate Program is designed for professionals who want to acquire new skills in the areas of data analytics and big data. You'll learn how to conduct analyses of data, interpret the results of your analyses to make predictions, and communicate data mining results to management and other non-technical audiences. Students can choose to devote either 15 or 30 hours per week; thus the duration of the program is 44 or 22 weeks respectively. Because the program is online, it is open to people around the world. When applying, you should have:

  • At least a year of work experience
  • Familiarity with Windows, Mac, or Linux operating system, specifically:
    • Creating and managing folders within folders
    • Creating and extracting files from zip archives
    • Elementary administrative tasks (e.g., installing software requiring admin privileges)
    • Basic familiarity with Microsoft Office or an equivalent productivity suite
  • Basic knowledge of statistics may accelerate your initial progress in the program, but all necessary statistical concepts will be introduced during each course.

Register Now

Technical skills are a hot commodity

Coding bootcamps are springing up around the world. Recently, bootcamps in "Data Science" are emerging as the latest thing. Why?

The short answer is, of course, jobs.

Nearly all businesses collect data about their operations and examine this data for insight into how to improve their operations. As the amount of data that businesses collect becomes increasingly large, insights from the data can no longer be effectively derived manually. There is a growing trend among companies to exploit data mining's potential to help them discover and act on the most important patterns contained within the data they collect. According to McKinsey,

"the United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data. "

PROBLEMS WITH DATA SCIENCE BOOTCAMPS:

  • They needlessly require substantial backgrounds in programming and statistics as prerequisites for admission
  • They needlessly take a programming approach to data analytics; lots of good jobs are available to people who can define problems appropriately and use a host of powerful tools to solve them
  • They are too short and, thus, too intense; 10-12 weeks is simply not enough time to learn a completely new set of skills
  • Their curricula are not designed by pedagogical experts; the curricula typically are composed of lectures (which are not an effective vehicle for learning) and a collection of ad hoc projects
  • Mentoring also tends to be ad hoc; a student's experience is all too often completely dependent on the quality of the mentor he or she is assigned
  • Programs tend to be very expensive, especially considered in terms of the amount students learn given what they pay

Our Program is Different

XTOL has created a unique Data Analytics Certificate Program, designed by current and former faculty of Carnegie Mellon, Northwestern, and Yale Universities. Our program is not aimed at computer scientists, engineers, and statisticians, but at a broader range of people who want to learn to use powerful statistical machine learning tools and big data infrastructure to extract actionable business insights from mountains of business and other data. The program's unique learn-by-doing approach will teach you practical job skills that you can put to use immediately.

IF YOU WANT TO DO IT, YOU CAN!

CONTACT US

STUDENT REGISTRATION


More Information

Visit the Univerity of Texas at Austin - Extended Campus website for more details, including upcoming information sessions.

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Register Today

Visit the Univerity of Texas at Austin - Extended Campus website to register for the Data Analytics Certificate Program.

Corporate Partnerships

We are happy to help train your people in Data Analytics. We'll reach out to you and arrange a discussion about your specific situation.