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

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

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

## 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
• 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

### Requirements

• Only a passion for success
• All software used in this course is either available for Free or as a Demo version
Description
• 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
• 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