Data Science In Python: Regression & Forecasting

Learn Python for Data Science & Machine Learning, and build regression and forecasting models with hands-on projects

What you'll learn

Master the machine learning foundations for regression analysis in Python

Perform exploratory data analysis on model features, the target, and relationships between them

Build and interpret simple and multiple linear regression models with Statsmodels and Scikit-Learn

Evaluate model performance using tools like hypothesis tests, residual plots, and mean error metrics

Diagnose and fix violations to the assumptions of linear regression models

Tune and test your models with data splitting, validation and cross validation, and model scoring

Leverage regularized regression algorithms to improve test model performance & accuracy

Employ time series analysis techniques to identify trends & seasonality, perform decomposition, and forecast future values

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

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.COURSE OUTLINE:Intro to Data ScienceIntroduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflowRegression 101Review the basics of regression, including key terms, the types and goals of regression analysis, and the regression modeling workflowPre-Modeling Data Prep & EDARecap the data prep & EDA steps required to perform modeling, including key techniques to explore the target, features, and their relationshipsSimple Linear RegressionBuild simple linear regression models in Python and learn about the metrics and statistical tests that help evaluate their quality and outputMultiple Linear RegressionBuild multiple linear regression models in Python and evaluate the model fit, perform variable selection, and compare models using error metricsModel AssumptionsReview the assumptions of linear regression models that need to be met to ensure that the model’s predictions and interpretation are validModel Testing & ValidationTest model performance by splitting data, tuning the model with the train & validation data, selecting the best model, and scoring it on the test dataFeature EngineeringApply feature engineering techniques for regression models, including dummy variables, interaction terms, binning, and moreRegularized RegressionIntroduce regularized regression techniques, which are alternatives to linear regression, including Ridge, Lasso, and Elastic Net regressionTime Series AnalysisLearn methods for exploring time series data and how to perform time series forecasting using linear regression and Facebook Prophet__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:8.5 hours of high-quality video14 homework assignments10 quizzes3 projectsData Science in Python: Regression ebook (230+ pages)Downloadable project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf you're an aspiring data scientist looking for an introduction to the world of regression modeling with Python, this is the course for you.Happy learning!-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)

Overview

Section 1: Getting Started

Lecture 1 Course Introduction

Lecture 2 About This Series

Lecture 3 Course Structure & Outline

Lecture 4 READ ME: Important Notes for New Students

Lecture 5 DOWNLOAD: Course Resources

Lecture 6 Introducing the Course Project

Lecture 7 Setting Expectations

Lecture 8 Jupyter Installation & Launch

Section 2: Intro to Data Science

Lecture 9 What is Data Science?

Lecture 10 Data Science Skillset

Lecture 11 What is Machine Learning?

Lecture 12 Common Machine Learning Algorithms

Lecture 13 Data Science Workflow

Lecture 14 Step 1: Scoping a Project

Lecture 15 Step 2: Gathering Data

Lecture 16 Step 3: Cleaning Data

Lecture 17 Step 4: Exploring Data

Lecture 18 Step 5: Modeling Data

Lecture 19 Step 6: Sharing Insights

Lecture 20 Regression Modeling

Lecture 21 Key Takeaways

Section 3: Regression 101

Lecture 22 Regression 101

Lecture 23 Goals of Regression

Lecture 24 Types of Regression

Lecture 25 Regression Modeling Workflow

Lecture 26 Key Takeaways

Section 4: Pre-Modeling Data Prep & EDA

Lecture 27 EDA for Regression

Lecture 28 Exploring the Target

Lecture 29 Exploring the Features

Lecture 30 ASSIGNMENT: Exploring the Target & Features

Lecture 31 SOLUTION: Exploring the Target & Features

Lecture 32 Linear Relationships & Correlation

Lecture 33 Linear Relationships in Python

Lecture 34 Feature-Target Relationships

Lecture 35 Feature-Feature Relationships

Lecture 36 PRO TIP: Pairplots & Lmplots

Lecture 37 ASSIGNMENT: Exploring Relationships

Lecture 38 SOLUTION: Exploring Relationships

Lecture 39 Preparing For Modeling

Lecture 40 Key Takeaways

Section 5: Simple Linear Regression

Lecture 41 Simple Linear Regression

Lecture 42 The Linear Regression Model

Lecture 43 Least Squared Error

Lecture 44 Linear Regression in Python

Lecture 45 Linear Regression in Statsmodels

Lecture 46 Interpreting the Model

Lecture 47 Making Predictions

Lecture 48 R-Squared

Lecture 49 Hypothesis Tests

Lecture 50 The F-Test

Lecture 51 Coefficient Estimates & P-Values

Lecture 52 Residual Plots

Lecture 53 CASE STUDY: Modeling Health Insurance Prices

Lecture 54 ASSIGNMENT: Simple Linear Regression

Lecture 55 SOLUTION: Simple Linear Regression

Lecture 56 Key Takeaways

Section 6: Multiple Linear Regression

Lecture 57 Multiple Linear Regression Equation

Lecture 58 Fitting a Multiple Linear Regression

Lecture 59 Interpreting Multiple Linear Regression Models

Lecture 60 Variable Selection

Lecture 61 ASSIGNMENT: Multiple Linear Regression

Lecture 62 SOLUTION: Multiple Linear Regression

Lecture 63 Mean Error Metrics

Lecture 64 DEMO: Mean Error Metrics

Lecture 65 Adjusted R-Squared

Lecture 66 ASSIGNMENT: Mean Error Metrics

Lecture 67 SOLUTION: Mean Error Metrics

Lecture 68 Key Takeaways

Section 7: Model Assumptions

Lecture 69 Assumptions of Linear Regression

Lecture 70 Linearity

Lecture 71 Independence of Errors

Lecture 72 Normality of Errors

Lecture 73 DEMO: Normality of Errors

Lecture 74 PRO TIP: Interpreting Transformed Targets

Lecture 75 No Perfect Multicollinearity

Lecture 76 Equal Variance of Errors

Lecture 77 Outliers, Leverage & Influence

Lecture 78 RECAP: Assumptions of Linear Regression

Lecture 79 ASSIGNMENT: Model Assumptions

Lecture 80 SOLUTION: Model Assumptions

Lecture 81 Key Takeaways

Section 8: Model Testing & Validation

Lecture 82 Model Scoring Steps

Lecture 83 Data Splitting

Lecture 84 Overfitting & Underfitting

Lecture 85 The Bias-Variance Tradeoff

Lecture 86 Validation Data

Lecture 87 Model Tuning

Lecture 88 Model Scoring

Lecture 89 Cross Validation

Lecture 90 Simple vs. Cross Validation

Lecture 91 ASSIGNMENT: Model Testing & Validation

Lecture 92 SOLUTION: Model Testing & Validation

Lecture 93 Key Takeaways

Section 9: Feature Engineering

Lecture 94 Intro To Feature Engineering

Lecture 95 Feature Engineering Techniques

Lecture 96 Polynomial Terms

Lecture 97 Combining Features

Lecture 98 Interaction Terms

Lecture 99 Categorical Features

Lecture 100 Dummy Variables

Lecture 101 DEMO: Dummy Variables

Lecture 102 Binning Categorical Data

Lecture 103 Binning Numeric Data

Lecture 104 DEMO: Additional Feature Engineering Ideas

Lecture 105 ASSIGNMENT: Feature Engineering

Lecture 106 SOLUTION: Feature Engineering

Lecture 107 Key Takeaways

Section 10: Project 1: San Francisco Rent Prices

Lecture 108 Project Brief

Lecture 109 Solution Walkthrough

Section 11: Regularized Regression

Lecture 110 Intro to Regularized Regression

Lecture 111 Ridge Regression

Lecture 112 Standardization

Lecture 113 Fitting a Ridge Regression Model

Lecture 114 DEMO: Fitting a Ridge Regression

Lecture 115 PRO TIP: RidgeCV

Lecture 116 ASSIGNMENT: Ridge Regression

Lecture 117 SOLUTION: Ridge Regression

Lecture 118 Lasso Regression

Lecture 119 PRO TIP: LassoCV

Lecture 120 ASSIGNMENT: Lasso Regression

Lecture 121 SOLUTION: Lasso Regression

Lecture 122 Elastic Net Regression

Lecture 123 DEMO: Fitting an Elastic Net Regression

Lecture 124 PRO TIP: ElasticNetCV

Lecture 125 ASSIGNMENT: Elastic Net Regression

Lecture 126 SOLUTION: Elastic Net Regression

Lecture 127 RECAP: Regularized Regression Models

Lecture 128 PREVIEW: Tree Based Models

Lecture 129 Key Takeaways

Section 12: Project 1: San Francisco Rent Prices (Continued)

Lecture 130 Project Brief

Lecture 131 Solution Walkthrough

Section 13: Time Series Analysis

Lecture 132 Intro to Time Series

Lecture 133 Moving Averages

Lecture 134 DEMO: Moving Averages

Lecture 135 Exponential Smoothing

Lecture 136 ASSIGNMENT: Smoothing

Lecture 137 SOLUTION: Smoothing

Lecture 138 Decomposition

Lecture 139 DEMO: Decomposition

Lecture 140 PRO TIP: Autocorrelation Chart

Lecture 141 ASSIGNMENT: Decomposition

Lecture 142 SOLUTION: Decomposition

Lecture 143 Forecasting

Lecture 144 Linear Regression With Trend & Season

Lecture 145 DEMO: Linear Regression With Trend & Season

Lecture 146 Facebook Prophet

Lecture 147 ASSIGNMENT: Forecasting

Lecture 148 SOLUTION: Forecasting

Lecture 149 Key Takeaways

Section 14: Project 2: Electricity Consumption

Lecture 150 Project Brief

Lecture 151 Solution Walkthrough

Section 15: Next Steps

Lecture 152 EXTRA LESSON

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