Get Excited
- Welcome to Data Science A-Z™
- BONUS: Learning Paths
- Get the materials
- Your Shortcut To Becoming A Better Data Scientist!
What is Data Science?
- Intro (what you will learn in this section)
- Updates on Udemy Reviews
- Profession of the future
- Areas of Data Science
- IMPORTANT: Course Pathways
- Some Additional Resources!!
- BONUS: Interview with DJ Patil
--------------------------- Part 1: Visualisation ---------------------------
- Welcome to Part 1
Introduction to Tableau
- Intro (what you will learn in this section)
- Installing Tableau Desktop and Tableau Public (FREE)
- Challenge description + view data in file
- Connecting Tableau to a Data file - CSV file
- Navigating Tableau - Measures and Dimensions
- Creating a calculated field
- Adding colours
- Adding labels and formatting
- Exporting your worksheet
- Section Recap
- Tableau Basics
How to use Tableau for Data Mining
- Intro (what you will learn in this section)
- Get the Dataset + Project Overview
- Connecting Tableau to an Excel File
- How to visualise an AB test in Tableau?
- Working with Aliases
- Adding a Reference Line
- Looking for anomalies
- Handy trick to validate your approach / data
- Section Recap
Advanced Data Mining With Tableau
- Intro (what you will learn in this section)
- Creating bins & Visualizing distributions
- Creating a classification test for a numeric variable
- Combining two charts and working with them in Tableau
- Validating Tableau Data Mining with a Chi-Squared test
- Chi-Squared test when there is more than 2 categories
- Visualising Balance and Estimated Salary distribution
- Bonus: Chi-Squared Test (Stats Tutorial)
- Bonus: Chi-Squared Test Part 2 (Stats Tutorial)
- Section Recap
- Part Completed
--------------------------- Part 2: Modelling ---------------------------
- Welcome to Part 2
Stats Refresher
- Intro (what you will learn in this section)
- Types of variables: Categorical vs Numeric
- Types of regressions
- Ordinary Least Squares
- R-squared
- Adjusted R-squared
Simple Linear Regression
- Intro (what you will learn in this section)
- Introduction to Gretl
- Get the dataset
- Import data and run descriptive statistics
- Reading Linear Regression Output
- Plotting and analysing the graph
Multiple Linear Regression
- Intro (what you will learn in this section)
- Caveat: assumptions of a linear regression
- Get the dataset
- Dummy Variables
- Dummy Variable Trap
- Understanding the P-Value
- Ways to build a model: BACKWARD, FORWARD, STEPWISE
- Backward Elimination - Practice time
- Using Adjusted R-squared to create Robust models
- Interpreting coefficients of MLR
- Section Recap
Logistic Regression
- Intro (what you will learn in this section)
- Get the dataset
- Binary outcome: Yes/No-Type Business Problems
- Logistic regression intuition
- Your first logistic regression
- False Positives and False Negatives
- Confusion Matrix
- Interpreting coefficients of a logistic regression
Building a robust geodemographic segmentation model
- Intro (what you will learn in this section)
- Get the dataset
- What is geo-demographic segmenation?
- Let's build the model - first iteration
- Let's build the model - backward elimination: STEP-BY-STEP
- Transforming independent variables
- Creating derived variables
- Checking for multicollinearity using VIF
- Correlation Matrix and Multicollinearity Intuition
- Model is Ready and Section Recap
Assessing your model
- Intro (what you will learn in this section)
- Accuracy paradox
