Applied Business Analytics

Decision-making with data

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Course Dates

STARTS ON

November 2, 2021

Course Duration

DURATION

6 weeks, online
4-6 hours per week

Course Duration

PROGRAM FEE

US$2,800

Course Information Flexible payment available

Key Outcomes

In this six-week program you will:

  • 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

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 can we leverage data for making the optimal decision? 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 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

This online program is ideal for managers and leaders who want to turn data into a competitive advantage by advancing their analytics skills. Participants will learn how to decide which is the best tool for their challenge, and how to apply different models. Participants come from a broad range of industries, job functions, and management levels. Past participants of this program include:

Senior executives who strive to leverage data for competitive advantage and build out analytics capabilities in their organizations. Representative roles include:

  • President/CEO
  • Chief Financial Officer
  • Chief Technology Officer
  • Chief Learning Officer
  • Chief Strategy Officer
  • Chief Operating Officer
  • Chief Solutions Officer
  • Managing Partner

Functional managers and business heads who want to advance their analytics skills and manage data analyst teams more effectively by asking better questions. Representative roles include:

  • Director of Product Marketing
  • Business Development Manager
  • Director Strategic Products and Services
  • Customer Engagement Manager
  • Business Intelligence Team Lead
  • Director of Engineering
  • Digital Business Head

Data and Business Analysts who want to deepen their understanding of how to apply analytics tools to common business challenges, such as predicting customer preferences and trends.

  • Data Analytics Manager
  • Digital Business Analyst
  • Financial Analyst
  • Information and Data Systems Analyst
  • Technology Innovation Designer
  • Senior Data Scientist
  • Manager of Informatics
  • Analytics Project Lead

Consultants who need to dig deeper into their client's data in order to make better recommendations and speak the same language as data analysts. Representative roles include:

  • Management Consultant, M&A
  • Principal Consultant
  • Managing Partner
  • Financial Consultant

Program Topics

Topic 1:

Making Predictions Using Data

Learn how to predict customer preferences by grouping like data. Explore the Netflix movie recommendation engine and understand the principles that can lead to greater customer satisfaction and retention.

Topic 2:

Tools and Frameworks to Build Predictive Models

Learn how analytics tools can help team managers choose major league players that outperform talent scouts’ picks, as the Oakland A’s did in the early 2000s. Also, review the Framingham Heart Study data to understand how this landmark study has helped to improve public health since it launched in 1948. We will consider how predictive analytics tools can save lives by predicting the chance that an individual will develop heart disease.

Topic 3:

Using Business Analytics to Interpret & Analyze Data

Using a historic Boston real estate data set, we will explore the basic logic employed by real estate technology firms. Additionally, learn about different analytics tools that can help predict Supreme Court decisions and how these tools can outperform the elite experts.

Topic 4:

Customer Analytics: Using Analytics to Serve Customers Better

Delve into how a healthcare system identifies high-risk patients before a major health complication and can intervene for better health outcomes. Plus, in an effort to understand Apple’s customers better, we will review a natural language processing technique for classifying Tweets, which will enable us to assess user sentiment.

Topic 5:

Understanding Machine Learning and its Business Applications

Through the lens of the Data-Models-Decisions-Value framework, learn how deep learning algorithms enable your machine to read numbers, a subset of computer vision capabilities. Further, we will seek to improve operational performance through machine learning algorithms and decision tools in inventory management.

Topic 6:

Managerial Decision-making with Business Analytics

While supporting the CFO of a fictitious company, we will interpret data for making crucial business decisions, namely how to shift it to a higher-tech focus while maximizing net present value. Finally, we will examine simulations for optimizing an airline’s fleet insurance policy.

Topic 1:

Making Predictions Using Data

Learn how to predict customer preferences by grouping like data. Explore the Netflix movie recommendation engine and understand the principles that can lead to greater customer satisfaction and retention.

Topic 4:

Customer Analytics: Using Analytics to Serve Customers Better

Delve into how a healthcare system identifies high-risk patients before a major health complication and can intervene for better health outcomes. Plus, in an effort to understand Apple’s customers better, we will review a natural language processing technique for classifying Tweets, which will enable us to assess user sentiment.

Topic 2:

Tools and Frameworks to Build Predictive Models

Learn how analytics tools can help team managers choose major league players that outperform talent scouts’ picks, as the Oakland A’s did in the early 2000s. Also, review the Framingham Heart Study data to understand how this landmark study has helped to improve public health since it launched in 1948. We will consider how predictive analytics tools can save lives by predicting the chance that an individual will develop heart disease.

Topic 5:

Understanding Machine Learning and its Business Applications

Through the lens of the Data-Models-Decisions-Value framework, learn how deep learning algorithms enable your machine to read numbers, a subset of computer vision capabilities. Further, we will seek to improve operational performance through machine learning algorithms and decision tools in inventory management.

Topic 3:

Using Business Analytics to Interpret & Analyze Data

Using a historic Boston real estate data set, we will explore the basic logic employed by real estate technology firms. Additionally, learn about different analytics tools that can help predict Supreme Court decisions and how these tools can outperform the elite experts.

Topic 6:

Managerial Decision-making with Business Analytics

While supporting the CFO of a fictitious company, we will interpret data for making crucial business decisions, namely how to shift it to a higher-tech focus while maximizing net present value. Finally, we will examine simulations for optimizing an airline’s fleet insurance policy.

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Program Highlights

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Case Studies and Examples

Analyze a myriad of cases through the lens of descriptive, predictive, and prescriptive tools leading you to identify the tool that offers the best outcome.

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The Analytics Blueprint

This personalized workbook can serve as a business analytics project action plan for you to implement with your team or organization at large. It also includes many pro tips.

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Weekly Office hours

Each week you will have the opportunity to address any questions through live office hours with Learning Facilitators, experts in this subject matter. All sessions are recorded.

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Discussion Boards on Industry-specific Topics

Whether you work in Finance, Healthcare, Energy, or IT, you will find a group of peers in a similar field to connect with – and learn from, which is a major benefit of joining an MIT Sloan program.

Case Studies

Image of a man watching Netflix on tablet computer to portray the case study example.

Netflix

How data analytics built a movies-you'll-love feature

Image of a laptop on a table displaying Twitter logo to portray the case study example.

Twitter

Mining Tweets to understand customer sentiment at Apple

Framingham Heart Study

How to save lives by predicting heart disease

Image of several high rise towers to portray the Boston Real Estate case study

Boston Real Estate

Leverage a historic Boston real estate data set to predict values

Image of people engaged in a discussion in an office to portray a corporate strategy case study

Corporate Strategy

Use an optimization model to maximize net present value and steer the corporate strategy

Inventory Management

Machine learning techniques work to improve operational performance

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

Testimonial

Tim Mccandlish

This course offered me a window into the world of Applied Business Analytics, which prior to this course, I had very little experience with. I’m very pleased to have taken it as the content and instructors made the topics approachable and allowed me to wrap my head around data in the business world. Specifically, I took away lessons on where to push back on the data, how to question it, and what different kinds of analysis are common practice. It touched on many subjects, but didn’t get so far into the weeds that I couldn’t keep up. That said, the content is there if you do want to dig deep as many of my cohort were well versed in data compared to myself. I would recommend this course to anyone looking to expand their knowledge on the subject, regardless of prior experience.

— Tim Mccandlish, Procurement Agent

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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Early registrations are encouraged. Seats fill up quickly!

Flexible payment options available. Learn more.