We may earn an affiliate commission when you visit our partners.
Course image
Justin Flett

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.

Read more

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 Tuesday, August 29th, at 8 AM (PST) and closes Thursday, August 31st, 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 September 10th.

Good luck, and have fun!

Enroll now

Two deals to help you save

We found two deals and offers that may be relevant to this course.
Save money when you learn. All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

What's inside

Syllabus

Data Science Coding Challenge
Congratulations on qualifying for Coursera's 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
Introduces learners to a timed competition format, a possible standard in data science and machine learning competitions
Taught by Justin Flett, a recognized data scientist
Applications for the live competition portion are required
Develops Python and Jupyter Notebook skills, which are core to data science practice and research
Examines industry-relevant real-world datasets, providing foundational knowledge

Save this course

Save Data Science Coding Challenge: Loan Default Prediction to your list so you can find it easily later:
Save

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 Coding Challenge: Loan Default Prediction with these activities:
Machine Learning Concepts Overview
Revisit key machine learning concepts to refresh your memory before the course begins.
Browse courses on Machine Learning
Show steps
  • Review different machine learning algorithms and their applications
  • Familiarize yourself with data preprocessing and model evaluation techniques
Python Coding Refresher
Review essential Python concepts to ensure a strong foundation for the course.
Browse courses on Python
Show steps
  • Review basic syntax, data types, and operators
  • Practice writing simple Python programs
Practice Coding Challenges
Solve coding challenges on platforms like LeetCode or HackerRank to strengthen your coding skills and prepare for the coding competition.
Browse courses on Python
Show steps
  • Select a challenge
  • Read the problem statement and understand the requirements
  • Implement the solution in Python
Six other activities
Expand to see all activities and additional details
Show all nine activities
Build a Machine Learning Model
Start a project to gain hands-on experience building and evaluating a machine learning model, solidifying your understanding of the course concepts.
Browse courses on Machine Learning
Show steps
  • Define the problem and gather data
  • Preprocess and explore the data
  • Select and train a model
  • Evaluate the model and make predictions
Coursera Guided Projects
Gain practical experience with real-world datasets and projects.
Browse courses on Data Science
Show steps
  • Browse through different Guided Projects that align with your skill level
  • Choose a project that interests you
  • Follow along with the step-by-step instructions and complete the hands-on exercises
Kaggle Competitions
Participate in Kaggle competitions to test your skills against other data scientists and win prizes.
Browse courses on Data Science
Show steps
  • Find a Kaggle competition that aligns with your interests
  • Explore the competition dataset and understand the problem statement
  • Develop and submit your solution before the deadline
Machine Learning Challenge
Test your understanding of various machine learning techniques and algorithms.
Browse courses on Machine Learning
Show steps
  • Register and join the competition
  • Download the dataset and start building your model
  • Monitor your progress and compare your results with others
Data Science Hackathons
Collaborate with other learners and industry professionals to solve real-world data science problems.
Show steps
  • Find relevant hackathons that fit your interests
  • Form a team or work independently to build a solution
  • Present your project and get feedback from experts
Data Science Portfolio
Showcase your data science skills by creating a portfolio of your best projects.
Browse courses on Data Science
Show steps
  • Select a few of your most impressive data science projects
  • Create a visually appealing and informative portfolio website
  • Share your portfolio with potential employers or clients

Career center

Learners who complete Data Science Coding Challenge: Loan Default Prediction will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to extract meaningful insights. This course provides a solid foundation in data science principles and techniques, including data wrangling, machine learning, and statistical modeling. By completing this course, you will develop the skills necessary to build predictive models that can help businesses make better decisions.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course provides a comprehensive overview of machine learning algorithms and techniques, as well as hands-on experience in building and evaluating machine learning models. By completing this course, you will gain the skills necessary to work on a wide range of machine learning projects.
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course provides a foundation in data analysis techniques, including data visualization, statistical analysis, and data mining. By completing this course, you will develop the skills necessary to extract valuable insights from data and communicate your findings to stakeholders.
Business Analyst
Business Analysts help businesses understand their data and make better decisions. This course provides an introduction to data analysis and visualization techniques, as well as hands-on experience in using data to solve business problems. By completing this course, you will develop the skills necessary to work as a Business Analyst and help businesses improve their performance.
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions about the world around us. This course provides a foundation in statistical theory and methods, as well as hands-on experience in using statistical software. By completing this course, you will develop the skills necessary to work as a Statistician and help businesses and organizations make better decisions.
Financial Analyst
Financial Analysts use data to make investment decisions. This course provides an introduction to financial analysis techniques, as well as hands-on experience in using financial data to make investment recommendations. By completing this course, you will develop the skills necessary to work as a Financial Analyst and help investors make better investment decisions.
Marketing Analyst
Marketing Analysts use data to understand customer behavior and develop marketing campaigns. This course provides an introduction to marketing analytics techniques, as well as hands-on experience in using marketing data to develop marketing campaigns. By completing this course, you will develop the skills necessary to work as a Marketing Analyst and help businesses improve their marketing performance.
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course provides an introduction to quantitative analysis techniques, as well as hands-on experience in using financial data to make investment recommendations. By completing this course, you will develop the skills necessary to work as a Quantitative Analyst and help investors make better investment decisions.
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides an introduction to software engineering principles and practices, as well as hands-on experience in developing software applications. By completing this course, you will develop the skills necessary to work as a Software Engineer and help businesses build better software products. This course provides an introduction to machine learning concepts, as well as hands-on experience in developing and deploying machine learning models. By completing this course, you will gain the skills necessary to work as a Software Engineer and develop software systems that can learn and improve over time.
Data Engineer
Data Engineers build and maintain data pipelines. This course provides an introduction to data engineering principles and practices, as well as hands-on experience in building data pipelines. By completing this course, you will develop the skills necessary to work as a Data Engineer and help businesses manage their data more effectively.
Product Manager
Product Managers are responsible for the development and management of software products. This course provides an introduction to product management principles and practices, as well as hands-on experience in developing and managing software products. By completing this course, you will develop the skills necessary to work as a Product Manager and help businesses build successful software products. This course can also be helpful for Product Managers who want to learn more about data science and machine learning. By understanding how data science and machine learning can be used to improve product development and decision-making, Product Managers can build better products that meet the needs of their customers.
User Experience Researcher
User Experience Researchers study how users interact with products and services. This course provides an introduction to user experience research principles and practices, as well as hands-on experience in conducting user research studies. By completing this course, you will develop the skills necessary to work as a User Experience Researcher and help businesses improve the user experience of their products and services. This course can also be helpful for User Experience Researchers who want to learn more about data science and machine learning. By understanding how data science and machine learning can be used to improve user research methods and insights, User Experience Researchers can conduct more effective user research studies and provide more valuable insights to businesses.
Data Architect
Data Architects design and manage data systems. This course provides an introduction to data architecture principles and practices, as well as hands-on experience in designing and managing data systems. By completing this course, you will develop the skills necessary to work as a Data Architect and help businesses build data systems that meet their business needs. This course can also be helpful for Data Architects who want to learn more about data science and machine learning. By understanding how data science and machine learning can be used to improve data architecture, Data Architects can design data systems that are more efficient and effective.
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses make better decisions. This course provides an introduction to business intelligence principles and practices, as well as hands-on experience in using data to make better decisions. By completing this course, you will develop the skills necessary to work as a Business Intelligence Analyst and help businesses improve their decision-making. This course can also be helpful for Business Intelligence Analysts who want to learn more about data science and machine learning. By understanding how data science and machine learning can be used to improve business intelligence practices, Business Intelligence Analysts can make better use of data to help businesses make better decisions.
Actuary
Actuaries use data to assess risk and uncertainty. This course provides an introduction to actuarial principles and practices, as well as hands-on experience in using data to assess risk and uncertainty. By completing this course, you will develop the skills necessary to work as an Actuary and help businesses and organizations manage risk.

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Science Coding Challenge: Loan Default Prediction.
Focuses on practical Python implementations of data science techniques, providing hands-on examples that complement the course's theoretical concepts.
This practical guide to machine learning libraries and techniques aligns well with the course's emphasis on hands-on coding, offering valuable insights for learners seeking to apply their knowledge.
This practical guide focuses on Python implementations of machine learning algorithms, offering a hands-on approach that complements the course's emphasis on coding.
This practical guide focuses on deep learning using Python and the Keras library, providing hands-on examples that complement the course's emphasis on coding.
This practical guide focuses on data analysis using the Pandas library in Python, providing hands-on examples that complement the course's emphasis on coding.
This comprehensive reference provides a broad overview of data mining techniques, including methods for data preprocessing, feature selection, and model evaluation, complementing the course's focus on loan default prediction.
This beginner-friendly book introduces the fundamentals of data science from scratch, providing a solid foundation for learners with no prior knowledge in the field.
This classic textbook offers a rigorous mathematical treatment of machine learning algorithms, providing a deeper understanding of the underlying principles for learners with a strong background in mathematics and statistics.
This practical guide focuses on the business applications of data science, providing insights into how data can be used to solve real-world problems in various industries.
This practical guide focuses on implementing data science solutions on Amazon Web Services (AWS), providing insights into cloud-based data processing and analysis.
This advanced textbook covers a wide range of statistical learning methods, providing a solid theoretical foundation for learners seeking a deeper understanding of the statistical principles underlying machine learning.
For learners interested in exploring advanced topics beyond the course's scope, this comprehensive reference on deep learning provides in-depth coverage of theoretical concepts and practical applications.
This advanced textbook provides a comprehensive overview of machine learning from a probabilistic perspective, offering a deeper understanding of the underlying principles for learners with a strong background in mathematics and statistics.

Share

Help others find this course page by sharing it with your friends and followers:
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser