Econometrics and Statistics for Business in R & Python
Why take this course?
🎓 Econometrics and Statistics for Business in R & Python
🚀 Course Headline Master Causal Inference & Statistical Modeling to tackle finance and marketing challenges with precision using Python and R! 📊🧠
1. Impactful Techniques: Learn the most impactful econometric techniques essential for various business departments like HR, Marketing, Finance, and Operations. 🎯
- Difference-in-Differences (DiD)
- Google's Causal Impact
- Granger Causality
- Propensity Score Matching
- CHAID (Chi-squared Automatic Interaction Detection)
2. Intuitive Learning: Gain a deep understanding of each technique through business-centric examples from the field and my professional experience. 📈
3. Real-World Problems: Apply what you've learned by working on real datasets to solve actual business problems, like the Cambridge Analytica Scandal impact or assessing training effectiveness. 🌍
4. Hands-On Coding: Develop hands-on coding skills with R and Python, using downloadable code that you can adapt for your own projects. 🧵
Course Structure
Section 1: Use Cases 📚
- Business literature and real-world examples illustrate the application of each econometric method.
Section 2: Intuition Tutorials 🎥
- Engage with tutorials designed to give you a clear grasp of why these techniques work.
Section 3: Practice Tutorials 👩💻
- Tackle coding challenges that solve real business or economic problems using R and Python.
Course Breakdown
[Section 1: Use Cases]
- Explore how Difference-in-Differences (DiD) can answer the impact of M&A on companies.
- Understand the influence of weather patterns on sales performance.
- Measure the effectiveness of brand campaigns.
- Investigate the outcomes of influencer marketing.
- Analyze drivers behind customer satisfaction and churn rates.
[Section 2: Intuition Tutorials]
- Gain intuition for Google's Causal Impact, Granger Causality, Propensity Score Matching, and CHAID.
- Learn to explain these concepts clearly to your colleagues, manager, and stakeholders.
[Section 3: Practice Tutorials]
- Work on datasets to measure the impact of events like the Cambridge Analytica Scandal on stock prices.
- Assess training programs' effects on employee performance.
- Challenge the hypothesis around minimum wage and employment rates.
- Discover the primary drivers for job quits.
- Solve riddles that could be applied to various business scenarios.
Hands-On Coding
- Develop your coding skills with hands-on examples in R and Python.
- Receive step-by-step guidance to build models from the ground up.
- Learn how to adapt the provided code for your own dataset and problem.
Join the Journey
Embark on a learning adventure that directly translates into your professional career. 🚀
- Dive into the world of econometrics with practical, actionable insights.
- Enhance your data analysis skills and apply them to make informed business decisions.
- Engage with a community of learners and professionals in the field of statistics and economics.
Let's transform data into strategic insights together! 🌟
Instructor: Diogo 👩🏫
Join me, Diogo, on this enlightening journey through the world of econometrics and statistics. I'm here to guide you every step of the way with real-world examples, hands-on coding, and actionable insights that will elevate your business analysis skills. 🤝
Enroll Now
Take the first step towards mastering econometrics and statistics in business with R and Python. Your professional growth awaits! 🌐🚀
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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