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

✓ Successfully perform all steps in a complex Data Science project
✓ Create Basic Tableau Visualisations
✓ Perform Data Mining in Tableau
✓ Understand how to apply the Chi-Squared statistical test
✓ Apply Ordinary Least Squares method to Create Linear Regressions
✓ Assess R-Squared for all types of models
✓ Assess the Adjusted R-Squared for all types of models
✓ Create a Simple Linear Regression (SLR)
✓ Create a Multiple Linear Regression (MLR)
✓ Create Dummy Variables
✓ Interpret coefficients of an MLR
✓ Read statistical software output for created models
✓ Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
✓ Create a Logistic Regression
✓ Intuitively understand a Logistic Regression
✓ Operate with False Positives and False Negatives and know the difference
✓ Read a Confusion Matrix
✓ Create a Robust Geodemographic Segmentation Model
✓ Transform independent variables for modelling purposes
✓ Derive new independent variables for modelling purposes
✓ Check for multicollinearity using VIF and the correlation matrix
✓ Understand the intuition of multicollinearity
✓ Apply the Cumulative Accuracy Profile (CAP) to assess models
✓ Build the CAP curve in Excel
✓ Use Training and Test data to build robust models
✓ Derive insights from the CAP curve
✓ Understand the Odds Ratio
✓ Derive business insights from the coefficients of a logistic regression
✓ Understand what model deterioration actually looks like
✓ Apply three levels of model maintenance to prevent model deterioration
✓ Install and navigate SQL Server
✓ Install and navigate Microsoft Visual Studio Shell
✓ Clean data and look for anomalies
✓ Use SQL Server Integration Services (SSIS) to upload data into a database
✓ Create Conditional Splits in SSIS
✓ Deal with Text Qualifier errors in RAW data
✓ Create Scripts in SQL
✓ Apply SQL to Data Science projects
✓ Create stored procedures in SQL
✓ Present Data Science projects to stakeholders