Multiple Linear Regression with scikit-learn from Coursera

In this 2-hour long project-based course, you will build and evaluate multiple linear regression models using Python. You will use scikit-learn to calculate the regression, while using pandas for data management and seaborn for data visualization. The data for this project consists of the very popular Advertising dataset to predict sales revenue based on advertising spending through media such as TV, radio, and newspaper.

By the end of this project, you will be able to:

- Build univariate and multivariate linear regression models using scikit-learn

- Perform Exploratory Data Analysis (EDA) and data visualization with seaborn

- Evaluate model fit and accuracy using numerical measures such as R² and RMSE

- Model interaction effects in regression using basic feature engineering techniques

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, this means instant access to a cloud desktop with Jupyter Notebooks and Python 3.7 with all the necessary libraries pre-installed.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.

- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

What's inside

Syllabus

Project: Multiple Linear Regression with scikit-learn

In this project-based course, you will build and evaluate multiple linear regression models using Python. You will use scikit-learn to calculate the regression, while using pandas for data management and seaborn for plotting. The data for this project consists of the very popular Advertising dataset to predict sales revenue based on advertising spending through media such as TV, radio, and newspaper. By the end of this project, you will be able to build univariate and multivariate linear regression models using scikit-learn, perform Exploratory Data Analysis (EDA) and data visualization with seaborn, evaluate model fit and accuracy using numerical measures such as R² and RMSE, and model interaction effects in regression using basic feature engineering techniques.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops intermediate to advanced data management skills using Python

Teaches modeling techniques foundational for data science and machine learning

Emphasizes model evaluation, which aids in critical evaluation of models

Uses a real-world dataset and case study to deepen understanding of applications

Requires some prior experience with Python and data management libraries

Reviews summary

Intermediate guide to linear regression

Learners say that this course is a good overview linear regression for beginners with practical examples and a guided project. Some reviewers say that some of the materials are relatively short and lack detail, but content is overall clear.

Material is well-organized and easy to understand.

"Very informative vedios"

"it's very clear"

"Well paced, very informative, I felt I learnt skills that I can apply to practical problems immediately."

Good primer for learners with little experience in linear regression.

"Better than the Michigan data science curses by 1 billion miles!"

"Best Course to linear regression basic to get advanced knowledge in neural network"

"I highly recommend any project from this instructor, he clearly defines all goals and the steps to get there, provides numerous examples, and simplifies complex concepts so that those with little to no experience could understand."

Engaging assignments and a real-world project help learners apply concepts.

"Good teacher and explanation!"

"Very good learning guide, thanks for the real project."

"It helps a lot that the programming assignment (= the functions and methods of the various Python libraries for data analysis) is demonstrated in real-time."

Some learners say that the content is insufficient and that video or written material is missing.

"there was no sound in video no. 6 after minute"

"The project explained the basic concepts effectively but it is very short. Otherwise, it's good."

"Overall a good project, just a few functions here and there whose use I needed to figure out myself."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Multiple Linear Regression with scikit-learn with these activities:

Read 'Applied Regression Analysis' by Montgomery, Peck, & Vining

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Provides a comprehensive overview of regression analysis techniques, including multiple linear regression.

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Review the chapters on multiple linear regression.
Solve practice problems from the book.

Review Descriptive Statistics

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Refreshes knowledge of descriptive statistics, a fundamental building block for multiple linear regression.

Browse courses on Descriptive Statistics

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Review measures of central tendency (mean, median, mode).
Review measures of variability (range, variance, standard deviation).
Practice calculating descriptive statistics using a calculator or spreadsheet.

Follow scikit-learn Tutorials on Multiple Linear Regression

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Provides guided instructions on how to perform multiple linear regression using scikit-learn.

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Follow the scikit-learn tutorial on linear regression.
Complete the exercises in the tutorial.

Five other activities

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Solve Multiple Linear Regression Problems

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Provides practice applying multiple linear regression techniques to solve problems.

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Solve multiple linear regression problems using the ordinary least squares (OLS) method.
Interpret the coefficients of the regression model.
Evaluate the goodness-of-fit of the regression model using metrics like R-squared and RMSE.

Build a Linear Regression Model in Python

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Provides hands-on experience building and evaluating multiple linear regression models in the context of the course dataset.

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Load the Advertising dataset into a Python environment.
Use scikit-learn to build a multiple linear regression model.
Evaluate the model's performance using metrics like R-squared and RMSE.
Visualize the relationship between the independent variables and the dependent variable using seaborn.
Write a report summarizing the results of the analysis.

Contribute to scikit-learn

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Provides an opportunity to contribute to the open-source community while enhancing understanding of scikit-learn.

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Identify an area in scikit-learn's multiple linear regression functionality that can be improved.
Propose a change or fix to the scikit-learn development team.
Implement the proposed change or fix.
Submit a pull request to scikit-learn.

Attend Data Science Meetups and Conferences

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Provides opportunities to connect with experts, learn about industry trends, and expand knowledge beyond the classroom.

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Attend meetups or conferences focused on data science and machine learning.
Engage with speakers and attendees to learn about multiple linear regression and its applications.

Mentor Students on Multiple Linear Regression

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Provides an opportunity to reinforce learning by teaching and assisting others.

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Join a tutoring or mentoring program focused on multiple linear regression.
Provide guidance and support to students learning about multiple linear regression.

Career center

Learners who complete Multiple Linear Regression with scikit-learn will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists use statistical and machine learning techniques to analyze data and extract insights. They may use multiple linear regression to build predictive models, identify patterns, and make recommendations for action. This course will help you develop the skills you need to succeed as a data scientist, and to make data-driven decisions that can help your organization achieve its goals.

See salaries and explore the career path for Data Scientist

Actuary

Actuaries use statistical and mathematical techniques to assess and manage risk in various fields, such as insurance and finance. They may use multiple linear regression to analyze data and develop models for pricing insurance policies, assessing financial risk, and making investment decisions. This course will help you build a strong foundation in actuarial science and prepare you for a successful career in this field.

See salaries and explore the career path for Actuary

Quantitative Analyst

Quantitative analysts use statistical and mathematical models to analyze financial and economic data for investment and trading purposes. They use multiple linear regression and other statistical techniques to develop trading strategies, manage risk, and make investment decisions. This course will help you build a strong foundation in quantitative analysis and prepare you for a successful career in this challenging and rewarding field.

See salaries and explore the career path for Quantitative Analyst

Biostatistician

Biostatisticians use statistical techniques to analyze data in the biological sciences. They may use multiple linear regression to analyze clinical trials, genetic data, and other biological data. This course will help you develop the analytical and problem-solving skills you need to succeed as a biostatistician, and to contribute to the advancement of medical research.

See salaries and explore the career path for Biostatistician

Statistician

Statisticians collect, analyze, and interpret data to provide insights into various fields. They use statistical techniques, including multiple linear regression, to design studies, analyze results, and make inferences. This course will help you build a strong foundation in statistics and prepare you for a successful career in this field.

See salaries and explore the career path for Statistician

Econometrician

Econometricians apply statistical principles to economic data to model and analyze economic relationships between variables, such as prices, employment, and output. They use their models to make forecasts, conduct research, and provide insights on economic trends. The skills learned in this course, like multiple linear regression, data management, and visualization, are essential for econometrics work. This course will help you build a strong foundation in the statistical techniques used in many economic and business fields.

See salaries and explore the career path for Econometrician

Data Analyst

Data analysts collect, clean, and analyze data to provide insights to businesses and organizations. They use statistical techniques, including multiple linear regression, to identify trends and patterns in data, and to develop predictive models. This course will help you build a foundation in data analysis and modeling, and prepare you for a successful career in this rapidly growing field.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial analysts evaluate and interpret financial data to provide investors and businesses with insights into the performance and value of stocks, bonds, and other financial instruments. They use their expertise to make recommendations on investment decisions and strategies, and to analyze market trends. The multiple linear regression techniques taught in this course are essential for modeling and understanding financial relationships, such as the relationship between stock prices and economic indicators.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business analysts identify and analyze business needs and problems, and then develop solutions and improvements using data and statistical techniques. They may use multiple linear regression to analyze sales data and customer behavior, or to forecast demand and optimize pricing. This course will provide you with the skills you need to succeed as a business analyst, and to make data-driven decisions that can help your organization thrive.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations research analysts use mathematical and statistical techniques to analyze and improve business operations. They may use multiple linear regression to optimize scheduling, inventory management, and other operational processes. This course will help you build a strong foundation in operations research and prepare you for a successful career in this field.

See salaries and explore the career path for Operations Research Analyst

Epidemiologist

Epidemiologists investigate the causes and spread of diseases. They may use multiple linear regression to analyze data and identify risk factors for diseases, and to develop strategies for prevention and control. This course will help you develop the analytical and problem-solving skills you need to succeed as an epidemiologist, and to protect the public from disease.

See salaries and explore the career path for Epidemiologist

Market Researcher

Market researchers collect, analyze, and interpret market data to understand consumer behavior and trends. They use statistical techniques, including multiple linear regression, to identify factors that influence consumer choices and to develop marketing strategies. This course will help you develop the skills you need to conduct market research and to make data-driven decisions that can help your organization succeed.

See salaries and explore the career path for Market Researcher

Management Consultant

Management consultants analyze business problems and develop solutions for organizations. They may use multiple linear regression to analyze data and identify trends, and to develop recommendations for improvement. This course will help you develop the analytical and problem-solving skills you need to succeed as a management consultant, and to help organizations achieve their goals.

See salaries and explore the career path for Management Consultant

Product Manager

Product managers are responsible for the development and success of a product. They may use multiple linear regression to analyze data and understand customer needs, and to develop strategies for product improvement. This course will help you develop the analytical and problem-solving skills you need to succeed as a product manager, and to bring successful products to market.

See salaries and explore the career path for Product Manager

Software Engineer

Software engineers design, develop, and maintain software systems. They may use multiple linear regression to analyze data and develop algorithms, and to optimize software performance. This course will help you develop the analytical and problem-solving skills you need to succeed as a software engineer, and to build innovative software solutions.

See salaries and explore the career path for Software Engineer