Applied Business Analytics

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.

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

Learn more about the self-paced online experience.

Learn more about Emeritus technical requirements.

The recommended weekly time commitment for core content is 4-5 hours, taking into account the busy lifestyles of working professionals, with an additional 2-3 hours recommended for non-compulsory weekly extension activities, should you have the time. Each module is released weekly, allowing a flexible but structured approach to learning. You’ll be supported as you engage in individual activities and group discussions, ensuring you feel confident to submit your best work at each weekly deadline.

Upon successful completion of your course, you will earn a certificate of completion from the MIT Sloan School of Management. This course may also count toward MIT Sloan Executive Certificate requirements.

In this course you will learn:

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 branching 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 smartphones.

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.

This course is for:

• 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

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.