About this Program
Economics and data science are a powerful combination. More and more organizations are recognizing the value of data scientists who have an economics background. Armed with economics theory, these professionals can help evaluate the effectiveness of business decisions, forecast sales and other business metrics, design markets and inform pricing decisions.
In this three-course certificate program, we’ll cover the fundamentals of data science tools and methodologies and how you can apply them to large economic data sets to analyze a wide range of business problems. Using Python and R, you’ll discover how to understand, manage and visualize big data. You’ll also learn how to apply the machine learning tools for predictive analysis and statistical methods for causal inference that are routinely used for economic analysis at research institutions, government agencies, major tech companies and retailers.
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Technical professionals with an economics background and business economists who’d like to enter the field of data science.
What You’ll Learn
- How to manage a data pipeline and implement standard quality control through versioning
- Fundamental methods for causal inference in economic applications, including randomized control trials, matching, difference-in-difference, synthetic controls, instrumental variables and regression discontinuity designs
- The ability to set up, run and interpret causal findings in practical applications
- Machine learning techniques such as logistic regression and classification methods, the LASSO for variable selection, neural networks and regression trees
- How to understand which data science techniques are called for by the different structures of the data and the underlying reason for analysis
Get Hands-On Experience
- Apply Python and R to analyze, manipulate and visualize massive economic data sets
- Apply causal inference and machine learning methods in industry-relevant applications