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Self-paced course
Self-paced course
Data Science in Python: Regression
Data Science in Python: Regression
Rating 4.7
57 reviews
57 reviews
57 reviews
Course 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.
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.
Course 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.
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.
Course Content
21.75 video hours
Skills you'll learn in this course
Apply linear & polynomial regression to model continuous outcomes
Diagnose model fit with residual plots, R² & cross‑validation
Handle multicollinearity, transformations & regularization
Deploy regression pipelines to predict real‑world KPIs in Python
Meet your instructors
Chris Bruehl
Analytics Engineer & Lead Python Instructor
Chris is a Python expert, certified Statistical Business Analyst, and seasoned Data Scientist, having held senior-level roles at large insurance firms and financial service companies. He earned a Masters in Analytics at NC State's Institute for Advanced Analytics, where he founded the IAA Python Programming club.
Student reviews
<p>I recently completed the Maven Analytics course, and I am thoroughly impressed with the experience. The course is exceptionally well-structured, providing clear and concise content that builds upon foundational concepts step-by-step. The practical exercises and real-world case studies greatly enhanced my understanding and allowed me to apply what I learned in a meaningful way. The instructors are knowledgeable and articulate, making complex topics accessible and engaging. The interactive components of the course, including quizzes and hands-on projects, were invaluable in reinforcing the material. Overall, this course exceeded my expectations and provided significant value. I highly recommend it to anyone looking to deepen their analytics skills and gain practical insights in a well-supported learning environment.</p>
NORAINI IBRAHIM
I must say the Data Science in Python: Regression course has exceeded my expectations in every way. As someone who's passionate about data analysis and keen on enhancing my skills, this course was an absolute gem. The course content was not only comprehensive but also presented in a manner that made it incredibly easy to follow. From understanding the fundamentals of regression analysis to diving into advanced techniques, each module was structured logically, allowing for a seamless learning experience. The hands-on exercises and real-world examples further solidified my understanding and provided practical insights that I can apply in my own projects. What truly sets this course apart is the platform itself. Maven Analytics provides an excellent learning environment, with user-friendly interfaces and seamless navigation. The platform's intuitive design made it effortless to access course materials, engage with fellow learners, and track my progress along the way. Overall, I highly recommend the Data Science in Python: Regression course to anyone looking to delve into the world of data science or enhance their skills in regression analysis. Kudos to Maven Analytics for offering such a valuable resource to aspiring data scientists like myself. I look forward to exploring more courses on their platform in the future
Andrew
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Data Science in Python: Regression
Data Science in Python: Regression
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
Course 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.
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.
Course 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.
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.
Curriculum
1
Orientation & Benchmark Assessment
1
Orientation & Benchmark Assessment
1
Orientation & Benchmark Assessment
2
Getting Started
2
Getting Started
2
Getting Started
3
Intro to Data Science
6 MIN
6 MIN
3
Intro to Data Science
6 MIN
6 MIN
3
Intro to Data Science
6 MIN
6 MIN
4
Regression 101
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
4
Regression 101
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
4
Regression 101
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
Pre-Modeling Data Prep & EDA
6 MIN
6 MIN
6 MIN
5
Pre-Modeling Data Prep & EDA
6 MIN
6 MIN
6 MIN
5
Pre-Modeling Data Prep & EDA
6 MIN
6 MIN
6 MIN
6
Simple Linear Regression
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6
Simple Linear Regression
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6
Simple Linear Regression
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
7
Multiple Linear Regression
6 MIN
6 MIN
6 MIN
6 MIN
7
Multiple Linear Regression
6 MIN
6 MIN
6 MIN
6 MIN
7
Multiple Linear Regression
6 MIN
6 MIN
6 MIN
6 MIN
8
Model Assumptions
6 MIN
6 MIN
6 MIN
8
Model Assumptions
6 MIN
6 MIN
6 MIN
8
Model Assumptions
6 MIN
6 MIN
6 MIN
9
Model Testing & Validation
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
9
Model Testing & Validation
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
9
Model Testing & Validation
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
10
Feature Engineering
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
10
Feature Engineering
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
10
Feature Engineering
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
11
PROJECT 1: San Francisco Rent Prices
6 MIN
6 MIN
11
PROJECT 1: San Francisco Rent Prices
6 MIN
6 MIN
11
PROJECT 1: San Francisco Rent Prices
6 MIN
6 MIN
12
Regularized Regression
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
12
Regularized Regression
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
12
Regularized Regression
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
13
PROJECT 1: San Francisco Rent Prices (Continued)
6 MIN
6 MIN
13
PROJECT 1: San Francisco Rent Prices (Continued)
6 MIN
6 MIN
13
PROJECT 1: San Francisco Rent Prices (Continued)
6 MIN
6 MIN
14
Time Series Analysis
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
14
Time Series Analysis
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
14
Time Series Analysis
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
15
PROJECT 2: Electricity Consumption
6 MIN
6 MIN
15
PROJECT 2: Electricity Consumption
6 MIN
6 MIN
15
PROJECT 2: Electricity Consumption
6 MIN
6 MIN
16
Final Assessment
6 MIN
16
Final Assessment
6 MIN
16
Final Assessment
6 MIN
17
Course Feedback & Next Steps
17
Course Feedback & Next Steps
17
Course Feedback & Next Steps
Meet your instructors
Chris Bruehl
Analytics Engineer & Lead Python Instructor
Chris is a Python expert, certified Statistical Business Analyst, and seasoned Data Scientist, having held senior-level roles at large insurance firms and financial service companies. He earned a Masters in Analytics at NC State's Institute for Advanced Analytics, where he founded the IAA Python Programming club.
Student reviews
<p>I recently completed the Maven Analytics course, and I am thoroughly impressed with the experience. The course is exceptionally well-structured, providing clear and concise content that builds upon foundational concepts step-by-step. The practical exercises and real-world case studies greatly enhanced my understanding and allowed me to apply what I learned in a meaningful way. The instructors are knowledgeable and articulate, making complex topics accessible and engaging. The interactive components of the course, including quizzes and hands-on projects, were invaluable in reinforcing the material. Overall, this course exceeded my expectations and provided significant value. I highly recommend it to anyone looking to deepen their analytics skills and gain practical insights in a well-supported learning environment.</p>
NORAINI IBRAHIM
I must say the Data Science in Python: Regression course has exceeded my expectations in every way. As someone who's passionate about data analysis and keen on enhancing my skills, this course was an absolute gem. The course content was not only comprehensive but also presented in a manner that made it incredibly easy to follow. From understanding the fundamentals of regression analysis to diving into advanced techniques, each module was structured logically, allowing for a seamless learning experience. The hands-on exercises and real-world examples further solidified my understanding and provided practical insights that I can apply in my own projects. What truly sets this course apart is the platform itself. Maven Analytics provides an excellent learning environment, with user-friendly interfaces and seamless navigation. The platform's intuitive design made it effortless to access course materials, engage with fellow learners, and track my progress along the way. Overall, I highly recommend the Data Science in Python: Regression course to anyone looking to delve into the world of data science or enhance their skills in regression analysis. Kudos to Maven Analytics for offering such a valuable resource to aspiring data scientists like myself. I look forward to exploring more courses on their platform in the future
Andrew
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Data Science in Python: Regression
Data Science in Python: Regression
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
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