Data Science in Python: Regression & Forecasting
Maven Analytics,Chris Bruehl
8:26:18
Description
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
Who is this for?
What You Need to Know?
More details
DescriptionThis 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.
COURSEÂ OUTLINE:
Intro to Data Science
Introduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflow
Regression 101
Review the basics of regression, including key terms, the types and goals of regression analysis, and the regression modeling workflow
Pre-Modeling Data Prep & EDA
Recap the data prep & EDA steps required to perform modeling, including key techniques to explore the target, features, and their relationships
Simple Linear Regression
Build simple linear regression models in Python and learn about the metrics and statistical tests that help evaluate their quality and output
Multiple Linear Regression
Build multiple linear regression models in Python and evaluate the model fit, perform variable selection, and compare models using error metrics
Model Assumptions
Review the assumptions of linear regression models that need to be met to ensure that the model’s predictions and interpretation are valid
Model Testing & Validation
Test model performance by splitting data, tuning the model with the train & validation data, selecting the best model, and scoring it on the test data
Feature Engineering
Apply feature engineering techniques for regression models, including dummy variables, interaction terms, binning, and more
Regularized Regression
Introduce regularized regression techniques, which are alternatives to linear regression, including Ridge, Lasso, and Elastic Net regression
Time Series Analysis
Learn 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 video
14 homework assignments
10 quizzes
3 projects
Data Science in Python: Regression ebook (230+Â pages)
Downloadable project files &Â solutions
Expert support and Q&AÂ forum
30-day Udemy satisfaction guarantee
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.
Happy learning!
-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)
Who this course is for:
- 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
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.
COURSEÂ OUTLINE:
Intro to Data Science
Introduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflow
Regression 101
Review the basics of regression, including key terms, the types and goals of regression analysis, and the regression modeling workflow
Pre-Modeling Data Prep & EDA
Recap the data prep & EDA steps required to perform modeling, including key techniques to explore the target, features, and their relationships
Simple Linear Regression
Build simple linear regression models in Python and learn about the metrics and statistical tests that help evaluate their quality and output
Multiple Linear Regression
Build multiple linear regression models in Python and evaluate the model fit, perform variable selection, and compare models using error metrics
Model Assumptions
Review the assumptions of linear regression models that need to be met to ensure that the model’s predictions and interpretation are valid
Model Testing & Validation
Test model performance by splitting data, tuning the model with the train & validation data, selecting the best model, and scoring it on the test data
Feature Engineering
Apply feature engineering techniques for regression models, including dummy variables, interaction terms, binning, and more
Regularized Regression
Introduce regularized regression techniques, which are alternatives to linear regression, including Ridge, Lasso, and Elastic Net regression
Time Series Analysis
Learn 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 video
14 homework assignments
10 quizzes
3 projects
Data Science in Python: Regression ebook (230+Â pages)
Downloadable project files &Â solutions
Expert support and Q&AÂ forum
30-day Udemy satisfaction guarantee
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.
Happy learning!
-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)
Who this course is for:
- 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
User Reviews
Rating
Maven Analytics
Instructor's Courses
Chris Bruehl
Instructor's Courses
Udemy
View courses Udemy- language english
- Training sessions 149
- duration 8:26:18
- Release Date 2023/10/04
