25% Off Data Science in Python: Regression & Forecasting | Udemy Review & Coupon

Learn Python for Data Science & Machine Learning to build regression and forecasting models through hands-on projects.

Quick Summary

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Data Science in Python: Regression & Forecasting is a comprehensive course that will provide participants with the machine learning fundamentals needed for regression analysis in Python. Specifically, students will learn how to perform comprehensive exploratory data analysis on features, target and relationships between them. With this knowledge they’ll be able build and interpret simple and multiple linear regression models with Statsmodels and Scikit-Learn. Moreover, they’ll be able to evaluate model performance using tools such as hypothesis tests, residual plots, and mean error metrics so that any violations of the assumptions of linear regression models can be quickly diagnosed and fixed.

Ultimately, this course is an easy way for people to gain the skills necessary for using Python for data science applications by focusing on a specific area: regression & forecasting. By mastering this area of data science through guided lessons and example datasets learners can take the first big steps towards being highly successful in their data-driven endeavours.

Course length:

This 8.5 hour on-demand video course on Data Science in Python: Regression & Forecasting provides a comprehensive overview of the fundamental concepts and techniques used to build effective predictive models. Through this course, you will learn about linear regression, logistic regression, time series analysis, forecasting models and more. The video lectures are accessible from both mobile and TV devices, with full lifetime access provided.

Best for:

Individuals with a background in data analysis or business intelligence may consider transitioning into a data science role.

Python users can develop the necessary skills for applying regression models in Python.

If anyone is interested in learning one of the most popular open source programming languages in the world.

About Chris Bruehl

Chris has experience as a data scientist, working in Data Science positions within the financial services industry. He received initial training on SAS before developing a preference for Python as his primary tool. Chris made a transition from applying data science in the field to teaching at a top-tier data science bootcamp. He has a strong interest in teaching and has the ability to simplify complicated concepts into manageable lessons. He has obtained a Masters Degree in Analytics from NCSU.

Description

This course is designed to help students master regression analysis in Python and understand the topics related to it. Starting with a thorough review of the data science workflow, primary goals and types of regression analysis, this course dives deep into regression modelling steps.

The students will learn how to do exploratory data analysis, fitting simple and multiple linear regression models, developing intuition for interpreting models and evaluating their performance through hypothesis tests, residual plots and error metrics. The assumptions of linear regression will be discussed in depth as well as how to diagnose and fix each one. Model testing & validation will also be covered such as the importance of data splitting, tuning & model selection. Feature engineering techniques and regularized regression algorithms are introduced when introducing ways to improve model performance.