Data Science Challenge from Coursera

In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model.

Important Information:

How to register?

To participate, you’ll need to complete simple steps. First, click the “Start Project” button to register.

Next, you’ll need to create a Coursera Skills Profile, which only takes a few minutes. We’ll send you a profile link the week of the challenge.

When does the challenge start?

The coding challenge begins Friday, June 30th, at 8 AM (PST) and closes Sunday, July 2nd, at 11:59 PM (PST). If you’re registered, you’ll receive a reminder email on the challenge start date.

Please note this is a timed competition. Once the challenge is unlocked, you’ll have 72 hours to complete it. You can submit as many times as you would like within this timeframe.

What will the winners receive?

Participants will be evaluated based on their model’s prediction accuracy. The top 20% of participants will receive an achievement badge on their Coursera Skills Profile, highlighting their performance to recruiters. The top 100 performers will get complimentary access to select Data Science courses.

All participants can showcase their projects to potential employers on their Coursera Skills Profile.

Winners will be notified by email the week of July 17th.

Good luck, and have fun!

What's inside

Syllabus

Data Science Challenge

Congratulations on qualifying for Coursera's first-ever Data Science coding competition! Click on the assignment below to learn more about the challenge you'll be asked to complete.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores a machine learning problem using practical tools

Utilizes Python and Jupyter Notebook for hands-on experience

Provides an opportunity to compete with other learners, fostering a competitive and engaging environment

Focuses on prediction accuracy, emphasizing a crucial skill in machine learning

Offers recognition and rewards to top performers, incentivizing excellence and motivating participants

Provides a showcase opportunity for participants to demonstrate their projects to potential employers

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 Data Science Challenge with these activities:

Review Python Basics

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Refresh your Python skills to ensure a strong foundation for the course, making it easier to follow along and fully grasp the concepts introduced.

Browse courses on Python

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Review online tutorials or documentation on Python basics
Work through practice exercises and code challenges
Test your understanding by solving simple programming problems

Volunteer for a Data Science Organization

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Gain practical experience and make a meaningful contribution by volunteering for a data science organization, applying your skills to real-world projects and gaining exposure to diverse perspectives.

Browse courses on Data Science

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Research data science organizations that align with your interests
Identify volunteering opportunities and apply
Participate in projects and contribute your expertise
Network with professionals and learn from their experiences

Attend Data Science Meetups

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Attend data science meetups to connect with other learners, professionals, and potential mentors, expanding your network and gaining insights into the field.

Browse courses on Data Science

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Research and identify relevant data science meetups in your area
Attend meetups and actively participate in discussions
Introduce yourself and share your interests
Exchange contact information and follow up with connections

Four other activities

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Show all seven activities

Review a Text on Python Machine Learning

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Review a book on Python machine learning to reinforce your understanding of the concepts taught in the course and gain additional insights and practical tips from an expert.

View Machine Learning with Python Cookbook:... on Amazon

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Choose a book on Python machine learning
Read through the chapters and take notes
Work through the practice exercises
Summarize the key concepts and techniques

Follow Tutorials on Data Science with Python

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Seek out and follow tutorials on data science with Python to expand your knowledge and skills, reinforcing what you learn in the course and gaining practical experience.

Browse courses on Data Science

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Search for tutorials on data science with Python
Select a tutorial that aligns with your learning objectives
Follow the tutorial step-by-step
Apply the techniques in your own projects

Participate in Coursera's Data Science Coding Competition

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Participate in this competition to test your data science, Python, and machine learning skills and solidify your learning, potentially earning an achievement badge and complimentary access to select Data Science courses.

Browse courses on Data Science

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Register for the Coursera Data Science Coding Competition
Familiarize yourself with the competition guidelines and requirements
Download the required software and tools
Build a model to address the provided challenge
Submit your solution for evaluation

Practice Data Science Problems on LeetCode

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Test and strengthen your data science skills by practicing problems on LeetCode, honing your problem-solving abilities and reinforcing the concepts learned in the course.

Browse courses on Data Science

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Sign up for a LeetCode account
Filter for data science problems
Solve problems and review solutions
Track your progress and identify areas for improvement

Career center

Learners who complete Data Science Challenge will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. This course will provide you with the skills you need to get started in this field, including how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Data Analysts.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists apply machine learning and statistical modeling to data to extract insights that can inform decision-making. This course will provide you with the skills you need to get started in this field, including how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Data Scientists.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models. This course will provide you with the skills you need to get started in this field, including how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to solve business problems. This course will provide you with the skills you need to get started in this field, including how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Business Analysts.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers collect and analyze data to understand consumer behavior. This course will provide you with the skills you need to get started in this field, including how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Market Researchers.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial Analysts use data to make investment recommendations. This course will provide you with the skills you need to get started in this field, including how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Financial Analysts.

See salaries and explore the career path for Financial Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This course may be useful for Software Engineers who want to learn how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Software Engineers who work on data-driven applications.

See salaries and explore the career path for Software Engineer

Operations Research Analyst

Operations Research Analysts use data to improve the efficiency of operations. This course may be useful for Operations Research Analysts who want to learn how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Operations Research Analysts who work on data-driven projects.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

Quantitative Analysts use data to make investment decisions. This course may be useful for Quantitative Analysts who want to learn how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Quantitative Analysts who work on data-driven projects.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use data to assess risk and uncertainty. This course may be useful for Actuaries who want to learn how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Actuaries who work on data-driven projects.

See salaries and explore the career path for Actuary

Statistician

Statisticians collect, analyze, and interpret data. This course may be useful for Statisticians who want to learn how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Statisticians who work on data-driven projects.

See salaries and explore the career path for Statistician

Data Engineer

Data Engineers design, build, and maintain data pipelines. This course may be useful for Data Engineers who want to learn how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Data Engineers who work on data-driven projects.

See salaries and explore the career path for Data Engineer

Database Administrator

Database Administrators manage and maintain databases. This course may be useful for Database Administrators who want to learn how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Database Administrators who work on data-driven projects.

See salaries and explore the career path for Database Administrator

IT Manager

IT Managers plan and manage the IT infrastructure of an organization. This course may be useful for IT Managers who want to learn how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for IT Managers who work on data-driven projects.

See salaries and explore the career path for IT Manager

Project Manager

Project Managers plan and manage projects. This course may be useful for Project Managers who want to learn how to use Python and Jupyter Notebooks to work with real-world datasets. You will also learn how to build prediction and classification models, which are essential skills for Project Managers who work on data-driven projects.

See salaries and explore the career path for Project Manager