Contact the Schank Academy for more information.
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."
Nearly all businesses collect data about their operations and examine this data for insight into performance improvement. As the amount of business data becomes increasingly large, insights from the data can no longer be effectively derived manually. Skilled data analytics practitioners are needed to exploit the data's potential and drive the improvements that keep companies competitive. 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."
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.
Education in the deepest sense has always been about doing rather than about knowing.
- Roger Schank
The Data Analytics Academy is unique in that we teach the skills needed to perform the job from day one. We call our mentored courses "Deep Dives" because from the very first day, our students dive in to an authentic role in a real-world scenario with all the complex challenges of the data-analytics industry. Our programs center around a rich, engaging story where students are given a realistic workplace role, work to achieve real goals, overcome real challenges, and explore the opportunity make real mistakes and learn from them in a safe environment. Along the way, your mentor provides resources, tools, and constructive, critical input on your work when you need it.
Our program is technical, but also grounded in the needs of business. Students use sophisticated, powerful analytics tools while polishing necessary business skills like identifying the types of problems data analytics can solve and presenting effectively to stakeholders.
There are no lectures, there are no grades, and there are no tests. We believe real learning comes from having an authentic, memorable experience.
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:
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.
Students work on authentic problems with an experienced mentor as their guide. Mentors don't lecture but rather help students learn and develop skills as relevant to the work they are doing. Mentors provide in-depth feedback on student projects and make recommendations for improvement spurring additional student growth in the process.
You don't have to be a computer scientist, engineer or statistician to enroll in XTOL's Data Analytics/Big Data Certificate.
Our program was developed for a broader range of professionals looking to improve their skill set.
When applying, you should have:
You work online, attend regularly scheduled meetings and make appointments with your mentor just as you would do with a real-world supervisor.
Students can choose to attend:
The cost for this program is $7,500.
The program comes is subject to our Refund Policy.
See our immersive, story-centered curricula in action. This demo is actual content from an early task in this course.
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.
Contact the Schank Academy for more information.