Learn Causal Inference & Statistical Modeling to solve finance and marketing business problems in Python and R
Instructed by Diogo Alves de Resende 10 hours on-demand video, 15 articles & 12 downloadable resources
What you’ll learn
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Understand the application of econometric techniques in business settings
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Apply Google’s Causal Impact to measure the effect of an intervention on a time series.
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Code econometric techniques in R and Python from scratch.
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Solve real business or economic problems using econometric techniques.
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Use propensity score matching to compare outcomes between groups while controlling for confounding variables.
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Develop an intuitive understanding of Difference-in-differences, Google’s Causal Impact, Granger Causality, Propensity Score Matching, and CHAID
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Perform Granger causality to test for causality between two time series.
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Develop intuition for econometric techniques through business case studies.
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Practice coding and applying econometric techniques through challenging and interesting problems.
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Understand and apply basic statistical concepts and techniques in real-life business cases
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
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