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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.
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.
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.
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 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!
Register for this course and see more details by visiting:
OpenCourser.com/course/g62b0p/data
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
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
Save this course
Save Data Science Challenge to your list so you can find it easily later:
Save
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 Challenge with these
activities:
Review Python Basics
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Expand to see all activities and additional details
Show all seven activities
Review a Text on Python Machine Learning
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
Sign up for a LeetCode account
-
Filter for data science problems
-
Solve problems and review solutions
-
Track your progress and identify areas for improvement
Review Python Basics
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all seven activities
Review a Text on Python Machine Learning
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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 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.
Data Scientist
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.
Machine Learning Engineer
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.
Business Analyst
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.
Market Researcher
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.
Financial Analyst
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.
Software Engineer
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.
Operations Research Analyst
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.
Quantitative 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.
Actuary
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.
Statistician
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.
Data Engineer
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.
Database Administrator
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.
IT Manager
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.
Project 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.
For more career information including salaries, visit:
OpenCourser.com/course/g62b0p/data
Reading list
We've selected 13 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 Challenge.
Save
Provides a comprehensive and authoritative overview of computer vision, covering the fundamental principles, algorithms, and applications of computer vision. It is particularly useful for readers who want to gain a deep understanding of the field of computer vision.
Provides a comprehensive and authoritative overview of statistical learning, covering the fundamental principles, algorithms, and applications of statistical learning. It is particularly useful for readers who want to gain a deep understanding of the field of statistical learning.
Provides a comprehensive and authoritative overview of pattern recognition and machine learning, covering the fundamental principles, algorithms, and applications of pattern recognition and machine learning. It is particularly useful for readers who want to gain a deep understanding of the field of pattern recognition and machine learning.
Provides a comprehensive and authoritative overview of machine learning from a probabilistic perspective, covering the fundamental principles, algorithms, and applications of machine learning from a probabilistic perspective. It is particularly useful for readers who want to gain a deep understanding of the field of machine learning from a probabilistic perspective.
Save
Provides a comprehensive and authoritative overview of deep learning, covering the fundamental principles, algorithms, and applications of deep learning. It is particularly useful for readers who want to gain a deep understanding of the field of deep learning.
Provides a comprehensive and authoritative overview of reinforcement learning, covering the fundamental principles, algorithms, and applications of reinforcement learning. It is particularly useful for readers who want to gain a deep understanding of the field of reinforcement learning.
Provides a comprehensive and in-depth introduction to machine learning using Python. It is particularly useful for readers who have some programming experience and want to develop a strong foundation in machine learning concepts.
Provides a practical and hands-on introduction to machine learning using Python and popular libraries like Scikit-Learn, Keras, and TensorFlow. It is particularly useful for readers who want to develop practical skills in building and deploying machine learning models.
Provides a practical and hands-on introduction to deep learning using Python and the Keras library. It is particularly useful for readers who want to develop practical skills in building and deploying deep learning models.
Provides a comprehensive and practical introduction to machine learning using R. It is particularly useful for readers who have some programming experience and want to develop a strong foundation in machine learning concepts.
Provides a practical and hands-on introduction to machine learning using Python and the scikit-learn library. It is particularly useful for readers who want to develop practical skills in building and deploying machine learning models.
Provides a comprehensive and practical introduction to natural language processing using Python. It is particularly useful for readers who want to develop practical skills in building and deploying natural language processing models.
Provides a visual and intuitive introduction to deep learning, making it accessible to readers with a limited mathematical background. It is particularly useful for readers who want to gain a conceptual understanding of deep learning and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/g62b0p/data
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Effort
8 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Justin Flett
Language
English
Good to know
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
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