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In this course, you will :
- 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.
