NEW The goal of business analytics is to determine which datasets are useful and how they can be leveraged to solve problems and increase efficiency, productivity, and revenue. In this non-technical online program, you will learn a practical framework that will enable you to use data to improve decision-making.
This program is delivered in collaboration with Emeritus. Please register on the Emeritus website.
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Emeritus
Applied Business Analytics (self-paced online) Certificate Track: Management and Leadership Location:
Online Tuition:
$2,800 Program Days (for ACE Credit) 2
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 apply your human judgement into the decision mix? What are some of the most practical applications of artificial intelligence?
Business analytics skills are a requirement across a variety of job functions and are in high demand from employers. In fact, job postings for this skill set has increased by 130 per-cent from September 2016 to the present. The Bureau of Labor Statistics (BLS) expects growth for business analytics skills to jump 10.9 percent, outpacing the national growth average of 5.2 percent for all jobs, from 2018 to 2028.
In the non-technical Applied Business Analytics program, you will learn a practical framework that will enable you to use data to improve decision-making. The only prerequisite is high-school level math and basic statistics.
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.
In this program, you will learn to:
Recognize the breadth of analytic applications
Describe common algorithms, their appropriate applications across domains, and their limitations
Discuss how to use analytics problem solving to lead teams and design deliverables
Apply best practices for data analytics process management, including establishing workflows, identifying inter-dependencies, and recognizing when to utilize human judgement
The goal of business analytics is to determine which datasets are useful and how they can be leveraged to solve problems and increase efficiency, productivity, and revenue. Extract greater value from your data by learning about these time-tested categories of algorithms:
Linear Regression The "best fit" line through all data points. Predictions are numerical. Example: Learn how a linear regression algorithm can change outcomes for a professional sports team
Logistic Regression The adaptation of Linear regression to problems of classification (e.g., Yes/No questions,groups,etc.) Example: Use logistic regression to predict coronary heart disease
Decision Tree A graph that uses a brannching method to match all possible outcomes of a decision. Example: Using a cutting-edge algorithm called an optimal classification tree, we will establish optimal inventory positions for smart phones.
Random Forest Takes the average of many decision trees, each of which is made with a sample of the data. Each tree is weaker than a full decision tree, but by combining them we get better overall performance. Example: Predict Supreme Court decisions using random forest
Clustering Sees what groups the data points fall into when we apply a clustering algorithm, such as K-Means Example: Use hierarchical clustering to group movie genres for Netflix
AI/Deep learning Allows machines to solve complex problems by learning from large amounts of data, algorithms inspired by the human brain. Example: Train a computer to read numbers.
Through a series of case studies that lay out an analytics framework, this program helps prepare leaders to leverage data for better business outcomes and lead teams of data scientists.
WEEK 1 Netflix: How Clustering Built a Movies-You’ll-Love Feature 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.
WEEK 2 Moneyball: How Linear Regression Built a Winning Team 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.
Framingham Heart Study: Using Logistic Regression to Save Lives 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.
WEEK 3 Boston Real Estate: Algorithms to Predict Real Estate Values 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.
Supreme Court: Classification and Regression Trees (CART) to Predict Court Cases 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.
WEEK 4 D2Hawkeye: Healthcare Case Management 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.
Twitter: Mining Tweets to Understand Customer Sentiment at Apple 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.”
WEEK 5 Deep Learning: Training Computers to Get Smarter Learn how deep learning algorithms enable your machine to read numbers with the open-source frameworks TensorFlow and Keras.
Corporate Strategy: Integer Optimization to Drive Portfolio Decisions for Maximum Value 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 a mixed integer optimization model and set one of the largest U.S.-based private companies on a path to sustainable growth.
WEEK 6 Inventory Management: Machine Learning Helps with Optimization Study a new approach to inventory management and consider a machine learning algorithm and optimal decision trees to improve operational performance.
Commercial Airline Insurance Simulation: Finding the Best Policy 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
Ensure cash obligations are met in the first year
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:
General managers and senior executives
Consultants
Data and technology specialists
Functional leaders and individual contributors of their team
Entrepreneurs/business owners
Please note that faculty are subject to change and not all faculty teach in each session of the program.
Boeing Leaders for Global Operations Professor of Management
Dimitris Bertsimas is the Boeing Leaders for Global Operations Professor of Management, a Professor of Operations Research, the CoDirector of the Operations Research Center and the Director of the Master of Business Analytics at MIT.
A 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 the following books: Introduction to Linear Optimization (with J. Tsitsiklis, Athena Scientific and Dynamic Ideas, 2008); Data, Models, and Decisions (with R. Freund, Dynamic Ideas, 2004); Optimization over Integers (with R. Weismantel, Dynamic Ideas, 2005); and The Analytics Edge (with A. O'Hair andW. Pulleyblank, Dynamic Ideas, 2016). He is former department editor of Optimization for Management Science and of Operations Research in Financial Engineering. Bertsimas has supervised 59 doctoral and 31 Master students. He is currently supervising 22 doctorla students. A member of the National Academy of Engineering and an INFORMS fellow, he has received numerous research awards, including the Harold Larnder Prize (2016), the Philip Morse Lecturship prize (2013), the William Pierskalla best paper award in health care (2013), best paper award in Trapsoration (2013), the Farkas Prize (2008), the Erlang Prize (1996), the SIAM Prize in Optimization (1996), the Bodossaki Prize (1998), and the Presidential Young Investigator Award (1991–1996). 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.
To access our programs, participants will need the following:
Valid email address
Computing device connected to the internet: PC/laptop, tablet, or smartphone
Latest version of their preferred browser to access our learning platform
Microsoft Office suite and PDF viewer to view content such as documents, spreadsheets, presentations, PDF files or transcripts
Programs may necessitate the usage of different software, tools, and applications. Participants will be informed about these additional requirements at the registration stage or during program commencement. Our program advisors are also available to respond to any queries about these requirements.
MIT Sloan Executive Education is collaborating with online education provider EMERITUS Institute of Management to offer a portfolio of high-impact online programs. By working with EMERITUS, MIT Sloan Executive Education is able to broaden access beyond on-campus offerings in a collaborative and engaging format that stays true to the quality of MIT Sloan and MIT as a whole. EMERITUS’ approach to learning is based on a cohort-based design to maximize peer to peer sharing and includes live teaching with world-class faculty and hands-on project based learning. In the last year, more than 7,500 students from over 120+ countries have benefited professionally from EMERITUS’ courses.
Registration for this program is done through Emeritus.
Learn from an Industry Pioneer
Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management, is author of several popular textbooks, including Analytics Edge and Data Models & Decisions: The Fundamentals of Management Science. Together with expert collaborators, he condensed 30 years of MIT teaching experience and his own firsthand knowledge of business analytics to create a highly practical curriculum to create a highly practical curriculum that is the basis of this program. Designed for pragmatic leaders who refuse to be left behind, the program helps leaders make analytically-supported decisions to arrive at better business outcomes.
"Because you can't look an algorithim in the eye, leaders must know how to inspect and audit algorithms." - Professor Dimitris Bertsimas, MIT Sloan School of Management
The Learning Journey
The Anatomy of a Use Case: How You Learn
Intro Activity: Statement of the problem facing the industry in the form of a poll, activity, or discussion question
Intro Video from the Professor: Sets up the case and learning outcomes
Interactive Flipbook of Case: A storyboard for illustrating the key points of the case
Debrief with Faculty: Assessment of what has been learned from the case
Applied Learning Opportunity: Brief assignment after some cases to apply the learning
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MIT Sloan Alumni Discount
To take advantage of your discount, enter the Referral Code ALUM20 on your application form. Discount is valid on all MIT Sloan Executive Education open enrollment programs held in Massachusetts and cannot be combined with any other discount.
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