Self-Paced Course
Data Science in Python: Regression
Master the foundations for regression analysis in Python, including linear & regularized regression, forecasting, validation & testing, and more
Course Description
This is a hands-on, project-based course designed to help you master the foundations for regression analysis in Python.
We’ll start by reviewing the data science workflow, discussing the primary goals & types of regression analysis, and do a deep dive into the regression modeling steps we’ll be using throughout the course.
You’ll learn to perform exploratory data analysis, fit simple & multiple linear regression models, and build an intuition for interpreting models and evaluating their performance using tools like hypothesis tests, residual plots, and error metrics. We’ll also review the assumptions of linear regression, and learn how to diagnose and fix each one.
From there, we’ll cover the model testing & validation steps that help ensure our models perform well on new, unseen data, including the concepts of data splitting, tuning, and model selection. You’ll also learn how to improve model performance by leveraging feature engineering techniques and regularized regression algorithms.
Throughout the course you'll play the role of Associate Data Scientist for Maven Consulting Group on a team that focuses on pricing strategy for their clients. Using the skills you learn throughout the course, you'll use Python to explore their data and build regression models to help firms accurately predict prices and understand the variables that impact them.
Last but not least, you'll get an introduction to time series analysis & forecasting techniques. You’ll learn to analyze trends & seasonality, perform decomposition, and forecast future values.
If you're an aspiring data scientist looking for an introduction to the world of regression modeling with Python, this is the course for you.
COURSE CONTENTS:
8.5 hours on-demand video
14 homework assignments
10 quizzes
3 projects
2 skills assessments (1 benchmark, 1 final)
COURSE CURRICULUM:
- Welcome to the Course!
- Benchmark Assessment
- Course Introduction
- About This Series
- Course Structure & Outline
- DOWNLOAD: Course Resources
- Introducing the Course Project
- Setting Expectations
- Jupyter Installation & Launch
- What is Data Science?
- Data Science Skill Set
- What is Machine Learning?
- Common Machine Learning Algorithms
- Data Science Workflow
- Step 1: Scoping a Project
- Step 2: Gathering Data
- Step 3: Cleaning Data
- Step 4: Exploring Data
- Step 5: Modeling Data
- Step 6: Sharing Insights
- Regression Modeling
- Key Takeaways
- QUIZ: Intro to Data Science
- Regression 101
- Goals of Regression
- Types of Regression
- Regression Modeling Workflow
- Key Takeaways
- QUIZ: Regression 101
- EDA for Regression
- Exploring the Target
- Exploring the Features
- ASSIGNMENT: Exploring the Target & Features
- SOLUTION: Exploring the Target & Features
- Linear Relationships & Correlation
- Linear Relationships in Python
- Feature-Target Relationships
- Feature-Feature Relationships
- PRO TIP: Pairplots & Lmplots
- ASSIGNMENT: Exploring Relationships
- SOLUTION: Exploring Relationships
- Preparing For Modeling
- Key Takeaways
- QUIZ: Pre-Modeling Data Prep & EDA
- Simple Linear Regression
- The Linear Regression Model
- Least Squared Error
- Linear Regression in Python
- Linear Regression in Statsmodels
- Intepreting The Model
- Making Predictions
- R-Squared
- Hypothesis Tests
- The F-Test
- Coefficient Estimates & P-Values
- Residual Plots
- CASE STUDY: Modeling Health Insurance Prices
- ASSIGNMENT: Simple Linear Regression
- SOLUTION: Simple Linear Regression
- Key Takeaways
- QUIZ: Simple Linear Regression
- Multiple Linear Regression Equation
- Fitting a Multiple Linear Regression
- Interpreting Multiple Linear Regression Models
- Variable Selection
- Adjusted R-Squared
- ASSIGNMENT: Multiple Linear Regression
- SOLUTION: Multiple Linear Regression
- Mean Error Metrics
- DEMO: Mean Error Metrics
- ASSIGNMENT: Mean Error Metrics
- SOLUTION: Mean Error Metrics
- Key Takeaways
- QUIZ: Multiple Linear Regression
- Assumptions of Linear Regression
- Linearity
- Independence of Errors
- Normality of Errors
- DEMO: Normality of Errors
- PRO TIP: Interpreting Transformed Targets
- No Perfect Multi-Collinearity
- Equal Variance of Errors
- Outliers, Leverage & Influence
- RECAP: Assumptions of Linear Regression
- ASSIGNMENT: Model Assumptions
- SOLUTION: Model Assumptions
- Key Takeaways
- QUIZ: Model Assumptions
- Model Scoring Steps
- Data Splitting
- Overfitting & Underfitting
- The Bias-Variance Tradeoff
- Validation Data
- Model Tuning
- Model Scoring
- Cross Validation
- Simple vs. Cross Validation
- ASSIGNMENT: Model Testing & Validation
- SOLUTION: Model Testing & Validation
- Key Takeaways
- QUIZ: Model Testing & Validation
- Intro to Feature Engineering
- Feature Engineering Techniques
- Polynomial Terms
- Combining Features
- Interaction Terms
- Categorical Features
- Dummy Variables
- DEMO: Dummy Variables
- Binning Categorical Data
- Binning Numeric Data
- DEMO: Additional Feature Engineering Ideas
- ASSIGNMENT: Feature Engineering
- SOLUTION: Feature Engineering
- Key Takeaways
- QUIZ: Feature Engineering
- Project Brief
- Solution Walkthrough
- Intro to Regularized Regression
- Ridge Regression
- Standardization
- Fitting a Ridge Regression Model
- DEMO: Fitting a Ridge Regression
- PRO TIP: RidgeCV
- ASSIGNMENT: Ridge Regression
- SOLUTION: Ridge Regression
- Lasso Regression
- PRO TIP: LassoCV
- ASSIGNMENT: Lasso Regression
- SOLUTION: Lasso Regression
- Elastic Net Regression
- DEMO: Fitting an Elastic Net Regression
- PRO TIP: ElasticNetCV
- ASSIGNMENT: Elastic Net Regression
- SOLUTION: Elastic Net Regression
- RECAP: Regularized Regression Models
- PREVIEW: Tree Based Models
- Key Takeaways
- QUIZ: Regularized Regression
- Project Brief
- Solution Walkthrough
- Intro to Time Series Analysis
- Moving Averages
- Exponential Smoothing
- ASSIGNMENT: Smoothing
- SOLUTION: Smoothing
- Decomposition
- PRO TIP: Autocorrelation Chart
- Forecasting
- Data Splitting
- Linear Regression with Trend & Season
- Facebook Prophet
- Key Takeaways
- QUIZ: Time Series
- Project Brief
- Solution Walkthrough
- Final Assessment
- Course Feedback Survey
- Share the love!
- Next Steps
WHO SHOULD TAKE THIS COURSE?
Data analysts or BI experts looking to transition into a data science role
Python users who want to build the core skills for applying regression models in Python
Anyone interested in learning one of the most popular open source programming languages in the world
WHAT ARE THE COURSE REQUIREMENTS?
- We strongly recommend taking our Data Prep & EDA course first
- Jupyter Notebooks (free download, we'll walk through the install)
- Familiarity with base Python and Pandas is recommended, but not required
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- Interactive Project files
- Downloadable e-books
- Graded quizzes and assessments
- 1-on-1 Expert support
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