Are you driven to solve business and engineering problems? Do you see complexity as a challenge rather than a barrier? You may not have a technical background, be a programmer, or know much about statistics, but you are willing to work hard and to learn as you go. If this description fits you, the Data Analytics and Big Data Program may be for you.

This immersive online program will teach you how to make data-driven decisions. If you're a self-starter looking to dive in to a highly competitive workforce, this program will teach you the skills that will guarantee you a job

What Will You Do in This Program

Data Analytics: Understanding Customers and Predicting Profitability

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.

Data Analytics: Predicting Customer Preferences

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.

Deep Analytics and Visualization

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.

Big Data: Web Mining

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.

Data Science with Python

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.

Your Mentor

You work on authentic problems with an experienced mentor as their guide. Mentors don't lecture but rather help you learn and develop skills relevant to the work they are doing. Mentors provide in-depth feedback on your projects and make recommendations for improvement spurring additional growth in the process.

Attend a Free Webinar

Click below to register for a free webinar to learn more and ask questions about this program.

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Try our Demo

See our immersive, story-centered curricula in action. This demo is actual content from an early task in this course.
Note: due to the technical nature of this course, this course is built for and best viewed on a laptop/desktop (not mobile).

Launch demo

At least a year of work experience
- Although the course can be taught to anyone, our experience suggests that individuals that have some work experience are more likely to succeed.
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.

Flexible Schedule

You work online, attend regularly scheduled meetings and make appointments with your mentor just as you would do with a real-world supervisor.

  You can choose to attend:
- Full time (30 hours per week) for 20 weeks, or
- Part time (15 hours per week) for 40 weeks.


  The cost for this program is 

  Financing available.

  The program comes with our Employment Guarantee and is subject to our Refund Policy.


You will get an offer for a full-time job, a contracting job, or paid internship working at least 35 hours per week within six months of successfully completing a certificate program.

If you voluntarily elect to accept a job working fewer than 35 hours per week and stops seeking full-time employment as a result, you will forfeit the employment guarantee.

If you meet the above criteria and do not receive a job offer within six months of completing the program you will be issued a refund for 100% of tuition you have paid.

At the conclusion of the first course of the Data Analytics program (three to four full-time weeks into the program), a committee of mentors will decide, based on your progress and quality of work, if you are likely to succeed in a professional career. If the committee decides that you are not, you can elect to leave the program with a 90% refund or to continue the program without an employment guarantee.


Successfully complete the entire program, submitting work judged to be satisfactory by our mentors for all projects in the program.
Be 21 years or older.
Live in a top 20 metropolitan area of the USA or be willing to locate to one. Be eligible and willing to work in the USA.
Be proficient in English.
Develop and actively maintain a portfolio in Github.
Develop an acceptable professional resume and accompanying LinkedIn profile.
Be available for a minimum of 3 job interviews per week.
Apply for jobs with at least 8 prospective employers each week.
Submit any analytics challenges required for job applications promptly.
Accept any legitimate job offer meeting the conditions above.

Apply for this program