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Applied Business Analytics (Emeritus)

Applied Business Analytics (Emeritus)

Key Facts

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Provider
School
Language
Level
Emeritus
MIT Sloan School of Management
English
Advanced

By enrolling in this online course you will spend approx. 6 weeks, 4-6 hours per week to learn key concept of Engineering.

Course Overview

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:

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

Course Syllabus

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.

Meet your Instructors

* We try to provide you as many free online courses from multiple providers and universities but in some cases you may have pay nominal fee to get assignments and/or to claim verified Certificate or Degree from respective provider.

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