Description
Data Science in Python: Regression & Forecasting course. This is a hands-on, project-based course designed to help you master the basics of regression analysis in Python. We begin by reviewing the data science workflow, discussing the basic goals and types of regression analysis, and an in-depth look at the regression modeling steps we will use throughout the course. You will learn to perform exploratory data analysis, fit simple and multiple linear regression models, and develop intuition for interpreting models and evaluating their performance using tools such as hypothesis tests, residual plots, and error measures. We also examine the assumptions of linear regression and learn how to recognize and fix each one. From there, we cover model testing and validation steps that help ensure our models perform well on new and unseen data, including concepts of data partitioning, tuning, and model selection. You will also learn how to improve model performance using feature engineering techniques and regular regression algorithms. During the course, you will play the role of Associate Data Scientist for Maven Consulting Group in a team focused on pricing strategy for our clients. Using the skills you learn throughout the course, you’ll use Python to explore their data and build regression models to help companies accurately predict prices and understand the variables that affect them. Last but not least, you will get an introduction to time series analysis and forecasting techniques. You will learn to analyze trends and seasonality, perform analysis and forecast future values. Summary of the course:
- An introduction to data science
- Introduce the fields of data science and machine learning, review essential skills, and introduce each step of the data science workflow.
- Regression 101
- Review the basics of regression, including key terms, types and purposes of regression analysis, and regression modeling workflow.
- Pre-Modeling Data Prep & EDA
- Review the data preparation and EDA steps required to perform modeling, including key techniques for target discovery, attributes, and relationships.
- Simple linear regression
- Build simple linear regression models in Python and learn about metrics and statistical tests that help evaluate their quality and output.
- Multiple linear regression
- Build multiple linear regression models in Python and evaluate model fit, perform variable selection, and compare models using error measures.
- Model assumptions
- Check the assumptions of linear regression models that must be met to ensure that model predictions and interpretations are valid.
- Model testing and validation
- Testing the performance of the model by splitting the data, fitting the model with train data and validation, selecting the best model, and scoring it on the test data.
- Feature engineering
- Use feature engineering techniques for regression models, including dummy variables, interaction terms, binning, and more.
- Ordinal regression
- Introduce regular regression techniques, which are alternatives to linear regression, including Ridge, Lasso, and Elastic Net regression.
- Time series analysis
- Learn how to explore time series data and how to perform time series forecasting using linear regression and Facebook Prophet.
Ready to dive in? Join today and get instant and lifetime access to:
- 8.5 hours of high quality video
- 14 assignments
- 10 tests
- 3 projects
- Data Science in Python: Regression eBook (230+ pages)
- Downloadable project files and solutions
- Expert support and question and answer forum
- Udemy’s 30-day satisfaction guarantee
If you are an aspiring data scientist looking for an introduction to the world of regression modeling with Python, this course is for you. Happy learning! -Chris Bruehl (data science expert and senior Python trainer, Maven Analytics)
What you will learn in Data Science in Python: Regression & Forecasting course
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Master the basics of machine learning for regression analysis in Python
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Perform exploratory data analysis on model features, targets, and relationships between them
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Building and interpreting simple and multiple linear regression models with Statsmodels and Scikit-Learn
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Evaluate model performance using tools such as hypothesis tests, residual plots, and mean error measures
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Diagnosing and fixing violations of the assumptions of linear regression models
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Tune and test your models with data partitioning, validation and cross-validation, and model scoring
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Use regular regression algorithms to improve test model performance and accuracy
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Use time series analysis techniques to identify trends and seasonality, perform analysis and forecast future values
This course is suitable for people who
- Data analysts or BI experts are looking to transition into a data science role
- Python users who want to develop basic skills for using regression models in Python.
- Anyone interested in learning one of the most popular open source programming languages in the world
Data Science in Python: Regression & Forecasting course specifications
- Publisher: Udemy
- teacher: Maven Analytics
- Training level: beginner to advanced
- Training duration: 8 hours and 31 minutes
- Number of courses: 152
Course topics Data Science in Python: Regression & Forecasting
Prerequisites for the Data Science in Python: Regression & Forecasting course
- We strongly recommend taking our Data Prep & EDA course first
- Jupyter Notebooks (free download, we’ll walk through the installation)
- Familiarity with basic Python and Pandas is recommended, but not required
Course images
Sample video of the course
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