Econometrics and Statistics for Business in R & Python
Econometrics and Statistics for Business in R & Python Coupon Code. Learn Causal Inference & Statistical Modeling to solve finance and marketing business problems in Python and R
Econometrics and Statistics for Business in R & Python
Updates November 2024: All Python tutorials have been remade and are up to date.
Econometrics and Statistics for Business in R & Python Course. Econometrics has horrible fame. The complex theorems, combined with boring classes where it feels like you are learning Greek, give every student nightmares. This course stays away from that. It will focus on (1) giving you the intuition and tools to apply the techniques learned, (2) making sure everything that you learn is actionable in your career, and (3) offer you a tool kit of peer-reviewed econometric causal inference techniques that will make you stand out and give you the ability to answer the tough questions.
WHY ECONOMETRICS AND CAUSAL INFERENCE FOR BUSINESS IN R AND Python?
In each section, you will learn a new technique. The learning process is split into three parts. The first is an overview of Use Cases. Drawing from business literature and my own experience, I will show examples where each Econometric technique has been applied. The goal here is to show that Econometric methods are actionable. The second part is the Intuition tutorials. The aim is for you to understand why the technique makes sense. All intuition tutorials are based on business situations. The last part is the Practice tutorials, where we will code and solve a business or economic problem. There will be at least one practice tutorial per section.
What you’ll learn
- Understand the application of econometric techniques in business settings
- Apply Google’s Causal Impact to measure the effect of an intervention on a time series.
- Code econometric techniques in R and Python from scratch.
- Solve real business or economic problems using econometric techniques.
- Use propensity score matching to compare outcomes between groups while controlling for confounding variables.
- Develop an intuitive understanding of Difference-in-differences, Google’s Causal Impact, Granger Causality, Propensity Score Matching, and CHAID
- Perform Granger causality to test for causality between two time series.
- Develop intuition for econometric techniques through business case studies.
- Practice coding and applying econometric techniques through challenging and interesting problems.
- Understand and apply basic statistical concepts and techniques in real-life business cases
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Who this course is for:
- Students or recent graduates interested in Econometrics and Data Science
- Data Scientists that would like to learn econometrics
- Business Analysts wanting to make a difference in their current job
- People curious about Econometrics and Data Science
- Professionals who would like to know more about analytics
Instructor
Diogo Alves de Resende – Diogo is a data analytics and business analytics professional with years of experience in the field. He has expertise in various methodologies, including time series forecasting for predicting sales trends, econometrics for analyzing economic data, and machine learning for optimizing marketing campaigns.
His background includes working for a major e-commerce company, where he used these techniques to drive business growth, and collaborating with the United Nations on a Mobile Money project in Lesotho, where he helped increase financial inclusion in the country.
In his courses, Diogo aims to provide practical and applicable knowledge through real-life examples and datasets. For example, he often uses case studies from his own work experiences to illustrate key concepts and demonstrate their relevance in the professional world. His goal is to equip students with the skills and tools necessary to succeed in their own careers in data science.
