Econometrics and Statistics for Business in R & Python

Learn Causal Inference & Statistical Modeling to solve finance and marketing business problems in Python and R

4.50 (655 reviews)

Udemy

platform

English

language

Data & Analytics

What you will 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

Course Gallery

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Rating & Reviews

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Comidoc Review

Our Verdict

This course offers a comprehensive overview of econometric techniques using R and Python. The instructor's real-world case studies, knowledgeable guidance, and clear explanations make learning accessible for beginners while remaining engaging for intermediate to advanced learners. However, the course slightly lacks in stressing important assumptions, contextualizing models from a mathematical standpoint, exploring result interpretation thoroughly, and incorporating varied practical examples. Overall, this Udemy course is an excellent starting point for those wanting to apply causal inference and statistical modeling techniques to business problems without delving extensively into theoretical foundations.

What We Liked

Excellent coverage of econometric techniques and their applications in business settings
Real-life case studies enhance understanding and applicability
Instructor is knowledgeable, attentive, and quick to respond to questions
Well-organized content with clear explanations
Covers both R and Python implementation

Potential Drawbacks

Assumptions behind techniques should be stressed more
Could benefit from in-depth summaries for beginners and clarification of model restrictions
Not all real-life examples are interesting or relevant; usage of free online data sources could improve practical cases
Limited mathematical explanations and lack of formulae presentation
Interpreting the results and their significance could be explored further