Time 7-9 Pimental 1
Midterm review by gsi #1116
Up to lecture 20 Chapter 12 comparing two samples.
Discussion worksheet https://drive.google.com/drive/u/1/folders/1Mua1tsrMzA-oa75FNqUMl8vlMugG_Gdx Reference sheet: https://drive.google.com/file/d/1W2y9_W2U_AF4KAdpxeTd59YIDIUK-zM9/view
- Introduction
- Cause and Effect
- Programming in Python
- Data Types
- Tables
- Census
- Data Visualization
- Histograms
- Functions
- Groups
- Pivots & Joins
- Conditionals & Iteration
- Chance
- Sampling
- Models
- Hypothesis Testing
- Decisions and Uncertainty
- A/B Testing
- Hypothesis Testing and Causality
- Table Examples
- Midterm Review I
Potential Study Strategy
- Catch up on any lectures, readings, and assignments if necessary.
- Think about any glaring topics of confusion, and review the lectures and textbook readings on those topics.
- Focus on reviewing the discussion worksheets first.
- Take one full past midterm as a diagnostic tool.
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- Do not look at the solutions until you complete the full midterm!
- Evaluate what you got incorrect on that midterm. Decide if it is a one-time silly error or something that points to a larger conceptual misunderstanding.
- Do not look at the solutions until you complete the full midterm!
- Work on your weaker points and make them your stronger points.
- Tutoring worksheets are a good resource to strengthen your fundamental understanding of topics.
- Review relevant lectures and textbook readings, do questions on other midterms that refer to the same concepts, redo similar problems on assignments, etc.
- Test for effectiveness. A good sign is if you can recognize the type of problem on the midterm (e.g., when to use
pivot
), answer the question correctly, be confident in your answer, and be able to teach it to another Data 8 student. - Take another midterm to see if there are any more concepts you are unsure of or need more practice with.
- Rinse and repeat if necessary.
My plan.
- Watch Review session.
- Recap topics
- Discussion
- Past exams
Pivot Group more than one hypotest ab test causation association