Data Science And Analytics
This course covers running and evaluating linear regression models (simple regression, multiple regression, and hierarchical regression), including assessing the overall quality of models and interpreting individual predictors for significance. By the end of this course you will be skilled in running and interpreting your own linear regression analyses, as well as critically evaluating the work of others. Examples of running regression in both SPSS and Excel programs provided.
What you'll learn
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Understand when to use simple, multiple, and hierarchical regression
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Understand the meaning of R-Square and the role it plays in regression
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Assess a regression model for statistical significance, including both the overall model and the individual predictors
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Effectively utilize regression models in your own work and be able to critically evaluate the work of others
Who this course is for:
- Anyone interested in learning more about regression analysis.
- This course is not for those looking for a general introduction to statistics course. For this we recommend taking a look at our descriptive statistics or inferential statistics courses. (This course specializes in regression analysis.)
- Those looking to increase their knowledge of regression.
Curriculum
- 13 Sections
- 85 Lessons
- 2 Quizzes
- 7h 20m Duration
Introduction
5 Lessons1 Quiz
- Introduction
- Correlation - Part 1
- Correlation - Part 2
- Correlation With More Than Two Variables - Excel
- Correlation with More than Two Variables - SPSS
- Correlation
What is Data Science?
5 Lessons
- Intro (what you will learn in this section)
- Profession of the future
- Areas of Data Science
- IMPORTANCE: Course Pathway
- EXTRA: ChatGPT For Data Science
------Part 1: Visualization--------
1 Lesson
- Welcome to Part 1
Introduction to Tableau
10 Lessons1 Quiz
- 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
9 Lessons
- 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
11 Lessons
- Intro (what you will learn in this section)
- Creating bins & Visualizing distributions
- 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
- Quick Note
- Visualising Balance and Estimated Salary distribution
- Extra: Chi-Squared Test (Stats Tutorial)
- Extra: Chi-Squared Test Part 2 (Stats Tutorial)
- Section Recap
- Part Completed
------------Part 2: Modelling------------
1 Lesson
- Welcome to Part 2
Stats Refreshers
6 Lessons
- 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
6 Lessons
- 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
11 Lessons
- Intro (what you will learn in this section)
- Get the dataset
- Assumptions of Linear Regression
- 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
8 Lessons
- 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
Regression in Microsoft Excel
2 Lessons
- Multiple Regression in Excel - Part 1
- Multiple Regression in Excel - Part 2
Accessing your Model
10 Lessons
- Intro (what you will learn in this section)
- Accuracy paradox
- Cumulative Accuracy Profile (CAP)
- How to build a CAP curve in Excel
- Assessing your model using the CAP curve
- Get my CAP curve template
- How to use test data to prevent overfitting your model
- Applying the model to test data
- Comparing training performance and test performance
- Section Recap