EXECUTIVE EDUCATION

Applied Business Analytics

A non-technical program - no background in coding required

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Course Dates
STARTS ON

January 27, 2021

Course Duration

DURATION

6 weeks, online
4-6 hours per week

Course Duration

PROGRAM FEE

US$2,800

Course Information Flexible payment available

Gain Business Analytics Skills to Make the Right Managerial Decisions

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.

Who Should Attend

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:

  • Data and technology specialists
  • General managers and senior executives
  • Functional leaders and individual contributors of their team
  • Consultants
  • Entrepreneurs/Business owners

Program Topics

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.

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 2A:

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.

Week 2B:

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 3A:

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.

Week 3B:

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 4A:

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.

Week 4B:

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 5A:

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.

Week 5B:

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 mixed integer optimization model and set one of the largest United States based private companies on a path to sustainable growth.

Week 6A:

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.

Week 6B:

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, and ensure we meet cash obligations in the first year.

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 4B:

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 2A:

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.

Week 5A:

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.

Week 2B:

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 5B:

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 mixed integer optimization model and set one of the largest United States based private companies on a path to sustainable growth.

Week 3A:

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.

Week 6A:

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.

Week 3B:

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 6B:

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, and ensure we meet cash obligations in the first year.

Week 4A:

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.

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Program Highlights

Interactive discussion posts

Interactive discussion posts

Non-technical curriculum for business professionals

Non-technical curriculum for business professionals

Highly visual learning techniques

Highly visual learning techniques

Case studies across multiple industries

Case studies across multiple industries

Hands-on application exercises

Hands-on application exercises

Optional code snippets for R and Python

Optional code snippets for R and Python

AI made more accessible

AI made more accessible

Faculty

Dimitris Bertsimas Faculty

Dimitris Bertsimas

Boeing Leaders for Global Operations Professor of Management, Professor of Operations Research, Associate Dean for the Master of Business Analytics

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. More info

Certificate

Certificate

Receive a verified digital certificate of completion from MIT Sloan School of Management. This program also counts towards an MIT Sloan Executive Certificate.

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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.

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