Description
Data Science in Python: Regression and Forecasting course. This is a practical, project-based course designed to help you master the fundamentals of regression analysis in Python. We start by reviewing the data science workflow, discussing the basic goals and types of regression analysis, and taking an in-depth look at the regression modeling steps used throughout the course. You’ll learn to perform exploratory data analysis, learn how to fit simple and multiple linear regression models, and develop intuition for interpreting models and evaluating their performance using tools like hypothesis testing, residual plots, and error measures. We also examine the assumptions of linear regression and learn how to identify and fix each. From there, we cover the model testing and validation steps that help ensure our models perform well on new and unseen data, including the concepts of data splitting, tuning, and model selection. You’ll also learn how to improve model performance using feature engineering techniques and regularized regression algorithms. During the course, you will take on the role of Associate Data Scientist for Maven Consulting Group on a team focused on pricing strategy for our clients. Using the skills learned throughout the course, you will use Python to explore companies’ data and build regression models to help them accurately predict prices and understand the variables that influence 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 prices. Course Summary:
- Introduction to Data Science
- Introduce the fields of data science and machine learning, review the skills required, 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 the regression modeling workflow.
- Pre-modeling data preparation and EDA
- Review the data preparation and EDA steps required to perform modeling, including key techniques for goal 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 the linear regression model that must be met to ensure that the model’s predictions and interpretations are valid.
- Model Testing and Validation
- Testing the model performance by splitting the data, fitting the model with train data and validation, selecting the best model, and scoring it on test data.
- Feature Engineering
- Apply feature engineering techniques to regression models, including dummy variables, interaction terms, binning, and more.
- Normal Regression
- Introduce regularized 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 do 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 Sufferings
- 10 tests
- 3 Projects
- Data Science in Python: Regression eBook (230+ pages)
- Downloadable project files and solutions
- Expert support and Q&A forum
- Udemy’s 30-day satisfaction guarantee
If you are an aspiring data scientist and 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 the Data Science in Python: Regression and Forecasting course
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Master the fundamentals of machine learning for regression analysis in Python
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Perform exploratory data analysis on model features, targets, and the 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 testing, residual plots, and mean error measures
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Diagnosing and fixing violations of assumptions of linear regression models
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Tune and test your model with data splitting, validation and cross-validation, and model scoring
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Use regularized regression algorithms to improve the performance and accuracy of test models
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Use time series analysis techniques to identify trends and seasonality, analyse, and forecast future values
This course is suitable for those who
- Data analyst or BI specialist 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 and Forecasting Course Specification
- Publisher: Udemy
- coach: Maven Analytics
- Training level: Beginner to advanced
- Training duration: 8 hours and 31 minutes
- No. of courses: 152
Course Topics Data Science in Python: Regression and Forecasting
Prerequisites for Data Science in Python: Regression and Forecasting Course
- We strongly recommend taking our Data Preparation and EDA course first
- Jupyter Notebook (free download, we’ll walk through the install)
- Familiarity with base Python and pandas is recommended, but not required
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