A non-technical program - no background in coding required
The abundance of data creates opportunities for business leaders to make better decisions. The challenge is that interpreting data from multiple sources isn’t common knowledge for most business professionals. How do we know which algorithm to use? How do we know when to insert our human judgement into the decision mix? What are some of the most practical applications of artificial intelligence?
In the non-technical Applied Business Analytics program, you will learn a practical framework that includes data models, decisions, and value, building confidence in using data to improve decision-making.
Upon completion of Applied Business Analytics, you will know which analytics approach is the most appropriate for your situation, and more importantly, how to tackle big data and leverage it for better business outcomes.
Anyone who wants to understand the business applications for analytics can benefit from this program, whether for a functional area of practice or for general management. This program is designed for non-technical professionals, however those with technical backgrounds will find bonus code snippets to illustrate how to implement the concepts.
Representative roles include:
Through a series of case studies that lay out an analytics framework, this program helps prepare leaders to effectively contribute to problem solving and lead teams of data scientists.
How can Netflix and other video-on-demand providers predict customer preferences? Explore a basic movie recommendation engine and observe the details of clustering, the critical enabler that makes it all possible.
Learn how a linear regression algorithm can outperform talent scouts for player selection in a manner that outperforms the traditional scouting system as the Oakland A’s did in the early 2000s.
How do we leverage Framingham Heart Study data to improve public health? You will consider the ability of logistic regression to save lives by predicting the chance that an individual will develop coronary heart disease.
Leverage a historic Boston real estate data set and a set of simplified approaches and consider the development and launch of an app based on your end user’s stated accuracy and interpretability requirements.
Study how analytics are used to predict Supreme Court decisions. Analyze classification and regression tree (CART) algorithm and how they can outperform the elite community of experts.
What if the healthcare system could identify patients before a major health complication and intervene? Learn how predictive modeling can dramatically improve the identification of high-risk patients and save lives.
How can companies use analytics to understand their customers? The challenge: can we correctly classify tweets as being negative, positive, or neither as it relates to Apple? Learn how corporate entities use natural language processing to track user sentiment of the “Twitterverse.”
Learn how deep learning algorithms enable your machine to read numbers with the open-source frameworks TensorFlow and Keras.
How do we support a CFO of a fictitious company to chart a course that will simultaneously shift the company to a more high tech focus and maximize net present value (NPV). Construct mixed integer optimization model and set one of the largest United States based private companies on a path to sustainable growth.
Study a new approach to inventory management and consider a machine learning algorithm and optimal decision trees to improve operational performance.
Observe an airline as it uses Monte Carlo Simulations to set its fleet insurance policy. Consider insurance policy recommendations for an airline given fleet composition with three objectives: properly insure the airline’s assets over a 5-year window, minimize cost, and ensure we meet cash obligations in the first year.
Dimitris BertsimasA faculty member since 1988, his research interests include optimization, stochastic systems, machine learning, and their application. In recent years, he has worked in robust optimization, statistics, healthcare, transportation, and finance. Bertsimas was a cofounder of Dynamic Ideas, LLC, which developed portfolio management tools for asset management. In 2002, the assets of Dynamic Ideas were sold to American Express. He is also the founder of Dynamic Ideas Press, a publisher of scientific books, the cofounder of Benefits Science, a company that designs health care plans for companies, of Dynamic Ideas Financial, a company that provides financial advice to customers, of Alpha Dynamics, an asset management company, P2 Analytics, an analytics consulting company and of MyA Health, a personalized health care advice company.
Bertsimas has coauthored more than 200 scientific papers and books, including The Analytics Edge (with A. O'Hair and W. Pulleyblank, Dynamic Ideas, 2016). He is former department editor of Optimization for Management Science and of Operations Research in Financial Engineering. A member of the National Academy of Engineering and an INFORMS fellow, he has received numerous research awards, including the Harold Larnder Prize (2016). He has also received recognition for his educational contributions: The Jamieson prize (2013) and the Samuel M. Seegal prize (1999).
Bertsimas holds a BS in electrical engineering and computer science from the National Technical University of Athens, Greece, as well as an MS in operations research and a PhD in applied mathematics and operations research from MIT.
After successful completion of the program, your verified digital certificate will be emailed to you, at no additional cost, in the name you used when registering for the program. All certificate images are for illustrative purposes only and may be subject to change at the discretion of MIT Sloan.