An Introduction to Statistical and Machine Learning

Data Science for Decision Making


Data is Power. Insights are Everything.

Data Science for Decision Making: An Introduction to Statistical and Machine Learning will expand your data fluency by teaching you how to implement statistical applications and modern machine learning techniques to inform business and other decisions. This course provides an introduction to a large number of both classical and modern statistical/machine learning approaches. These approaches have tremendous practical applications from predicting customer purchases or donor giving to understanding which product characteristics impact purchase decisions or which variables influence real estate valuations.
  • Number of Days

    In-Person at the Penn Club in NYC
  • Number of Hours

    8:30 a.m. to 4:30 p.m. each day
  • Course Investment

    Emory alumni & group discounts available
Data Science for Decision Making

How It Works

In this two-day session, you will learn from experts who literally wrote the book on the topic (An Introduction to Statistical Learning) and harness the power of data to drive business forward.

Hands typing on a computer with different types of charts overlayed on top

You will gain an understanding of how to leverage modern machine learning techniques, build a foundation in statistical analysis, more accurately predict outcomes of decisions, better understand the relationship between different variables, and reduce bias in decision making.

  • Model Accuracy and the Bias/Variance Tradeoff
  • Linear Regression
  • Classification
  • Cross-Validation
  • Linear Model Selection and Regularization
  • Tree Methods for Regression and Classification
  • Support Vector Machines
  • Deep Learning

This Course Is Ideal For

The Data Science for Decision Making course is a good fit for those interested in using modern statistical and machine learning methods for modeling and prediction from data. This group includes business leaders, scientists, engineers, data analysts, data scientists, among others but also less technical individuals with degrees in non-quantitative fields such as the social sciences. Previous exposure to a programming language, such as R, MATLAB or Python, is useful but not required.

  • Those looking to better use data to make decisions in business and other settings
  • Those who have an interest in data science, machine learning, and business analytics
  • Those with a business or quantitative background
  • Those who have taken an entry-level statistics course in college

Faculty Spotlight: Gareth James

Gareth James
John H. Harland Dean and Professor of Information Systems & Operations Management

Gareth James is renowned for his visionary leadership, statistical mastery, and commitment to the future of business education. He is a noted scholar and researcher with extensive published works focused on statistical and machine learning methodologies. James is an elected fellow of both the American Statistical Association and the Institute of Mathematical Statistics and his work has been cited more than 20,000 times. James is also a superb teacher and mentor, having won multiple awards in both categories and co-authoring the extremely successful book, An Introduction to Statistical Learning. In July 2022 he moved to Emory University as the Dean of the Goizueta Business School.

Faculty Spotlight: Daniela Witten

Daniela Witten
Professor of Statistics and Biostatistics at University of Washington and the Dorothy Gilford Endowed Chair in Mathematical Statistics

Daniela Witten develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning. She has received a number of awards for her research: most notably the Leo Breiman Award for contributions to the field of statistical machine learning, and the Presidents’ Award from the Committee of Presidents of Statistical Societies for a statistician under age 41. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Her publications have received more than 35,000 citations. Witten is also co-author of the popular textbook, An Introduction to Statistical Learning.

Penn Club

30 W 44th St, New York, NY 10036

Act Fast

Data is power. It can be used to uncover opportunities and solve complex challenges. Enhance your statistical and machine learning skills today.

Fill out the form below to learn more.

Special Pricing

*We offer special pricing for select constituents.

- Emory University Faculty/Staff
- Goizueta Business School Alumni
- Emory University Alumni
- Government and Nonprofit
- Veterans
- Groups 3-9
- Groups 10+
- Custom Clients

Please contact us for more information about our special pricing or to receive your code before you register. Discounts cannot be combined.

Course Calendar

Download the Emory Executive Education calendar to view upcoming courses for open enrollment.