Econometrics and Statistics for Business in R & Python

Updates: November 2024 - All Python and R tutorials have been completely remade and are now up-to-date!

Unlock Actionable Insights: Econometrics & Causal Inference for Business in R and Python

Tired of dry, theoretical econometrics? This course cuts through the complexity and focuses on practical application. We ditch the intimidating theorems and boring lectures, delivering a toolkit of powerful, peer-reviewed causal inference techniques you can actually use to answer tough business questions and stand out in your career.

Why Econometrics & Causal Inference for Business?

Each section introduces a new technique, broken down into three easy-to-follow parts:

• Real-World Use Cases: Discover how econometric methods are applied in practice, drawing from business literature and real-world examples. We'll show you how these techniques can be applied to HR, Marketing, Finance, Operations, and more.
• Intuitive Tutorials: Understand why each technique works, explained through relatable business scenarios. Gain the confidence to explain concepts to colleagues and stakeholders.
• Hands-On Practice: Apply your knowledge by coding and solving real-world business or economic problems in R and Python. You'll complete at least one practice tutorial per section.

What Makes This Course Different?

1. Comprehensive Coverage of Impactful Techniques: Master the causal inference techniques most relevant to today's business challenges. We cover:

   • Difference-in-differences
   • Google's Causal Impact
   • Granger Causality
   • Propensity Score Matching
   • CHAID

2. Business-Focused Examples: Learn by doing! Each section begins with real-world business cases and studies demonstrating the power of each technique. You'll see how to apply these methods to problems you might face in your own job. Examples include:

   • Impact of M&A on companies
   • Understanding the influence of weather on sales
   • Measuring the effectiveness of brand campaigns
   • Analyzing the impact of influencer and social media marketing
   • Investigating drivers of customer satisfaction

3. Challenging & Engaging Problems: Apply your skills to solve real-world problems using actual business or economic datasets. We'll tackle:

   • Analyzing the impact of the Cambridge Analytica Scandal on Facebook's stock price.
   • Assessing the effectiveness of employee training programs.
   • Testing the claim that minimum wage increases decrease employment.
   • Identifying the key reasons why people quit their jobs.
   • Exploring the classic "chicken or the egg" dilemma.

4. Hands-On Coding in R & Python: Learn by coding! We'll build code from scratch in every practice tutorial, using both R and Python. The code is designed for you to download, adapt to your own datasets, and apply to your projects. I'll provide clear explanations of how to modify the code for your specific needs.

Course Summary:

This course is designed to be immediately applicable to your career. You'll gain practical skills, real-world examples, and hands-on experience with causal inference techniques in R and Python.

Have questions? Feel free to reach out! I look forward to seeing you in the course.

— Diogo