Data Science A-Z™: Real-Life Data Science Exercises Included

1. Successfully perform all steps in a complex Data Science project
2. Create Basic Tableau Visualisations
3. Perform Data Mining in Tableau
4. Understand how to apply the Chi-Squared statistical test
5. Apply Ordinary Least Squares method to Create Linear Regressions
6. Assess R-Squared for all types of models
7. Assess the Adjusted R-Squared for all types of models
8. Create a Simple Linear Regression (SLR)
9. Create a Multiple Linear Regression (MLR)
10. Create Dummy Variables
11. Interpret coefficients of an MLR
12. Read statistical software output for created models
13. Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
14. Create a Logistic Regression
15. Intuitively understand a Logistic Regression
16. Operate with False Positives and False Negatives and know the difference
17. Read a Confusion Matrix
18. Create a Robust Geodemographic Segmentation Model
19. Transform independent variables for modelling purposes
20. Derive new independent variables for modelling purposes
21. Check for multicollinearity using VIF and the correlation matrix
22. Understand the intuition of multicollinearity
23. Apply the Cumulative Accuracy Profile (CAP) to assess models
24. Build the CAP curve in Excel
25. Use Training and Test data to build robust models
26. Derive insights from the CAP curve
27. Understand the Odds Ratio
28. Derive business insights from the coefficients of a logistic regression
29. Understand what model deterioration actually looks like
30. Apply three levels of model maintenance to prevent model deterioration
31. Install and navigate SQL Server
32. Install and navigate Microsoft Visual Studio Shell
33. Clean data and look for anomalies
34. Use SQL Server Integration Services (SSIS) to upload data into a database
35. Create Conditional Splits in SSIS
36. Deal with Text Qualifier errors in RAW data
37. Create Scripts in SQL
38. Apply SQL to Data Science projects
39. Create stored procedures in SQL
40. Present Data Science projects to stakeholders