Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024]

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

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What you'll learn

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