Practice Your Data Science Skills
Hands-on learning with Data Science Challenges 🌟 Level up your machine learning skills and grow your data science career 🔥
Use key tools, libraries and frameworks
What is
Data-Driven Science
You have taken online courses and wonder what to do next. Start joining Challenges on Data-Driven Science! 
It's all about practicing ML problems, learning state-of-the-art methods, and progressing in your data science career. You will compete against yourself and develop the competencies needed in the industry. It's never been easier to reach the next level as a data-driven software engineer.
What makes Challenges different from online courses
Data-Driven Science Challenges
- Effective skill development through active ML practice
- Become proficient in particular tasks such as object detection
- Engaging interactive learning journey using real-world scenarios
- High variety of tasks, industry problems, data sets, and methods
- Business use cases as fundamental part of each Challenge
- Developing universal problem solving and storytelling skills
Traditional Online Courses
- Courses usually focus on absorbing content (passive learning)
- Learning of foundational data science knowledge (e.g. Python)
- Consist of mostly video lectures and a few exercises
- Most online courses follow the same structure and flow
- Predominantly teaching theory, concepts and basic knowledge
- Hard to actually finish: overall completion rate only around 3%
What makes Challenges different from online courses
How Challenges will impact your machine learning skills
Practice, Practice, Practice
The 10,000 hour rule reflects the idea to obtain elite performance levels with enough practice. Data-Driven Science gives you the opportunity to do exactly that.
Make yourself a real-life problem solver
Everyone loves problem solving skills. Why? Because you are able to come up with new and creative solutions to any problem. Challenges help you developing that critical skill.
Learn application not just theory
Practical application is the holy grail of learning. Just knowing the theory and concepts of machine learning won’t make you a good data scientist. Period.
Finish always with an achievement
Challenges have a clear goal and always end with a result that demonstrates your newly learned skill. Now you can take this accomplishment and move on to the next Challenge.
Work with data like in the real-world
Ideally data would always be as clean and structured as in Kaggle competitions. However, the industry requires you to work with pretty messy data sometimes. Time to learn it.
Build new skills through a learning habit
Learning a little each day adds up. Research shows that students who make learning a habit are more likely to reach their goals. Challenges are ideal for building new habits.
Why Challenges
Practice ML
Put your data science knowledge into practice and work on exciting machine learning problems to build the skills needed in the industry.
Showcase Skills
Show others what you have accomplished in a Challenge and tell your personal data science story through actual project results.
Grow Career
Learn in-demand machine learning skills that make you job ready and allow you to build the data science career you want for yourself.
Hands-On Learning
- Different machine learning problems and industry use cases
- Wide range of tasks such as object detection or text classification
- Challenges range from beginner to advanced difficulty levels
- Flexible (self-paced) and scheduled start dates (cohort learning)
Community Support
You are not alone – the Data-Driven Science Community will be your learning buddy throughout every Challenge. Other students, alumni and mentors will help you get your questions answered and allow you to collaborate with peers whenever you need.
Portfolio Building
In every Challenge you will work on actual data science projects and develop your own solution. Share your project results with your social network and put them on your GitHub profile. This will be super helpful for your next job application. Promise!
Career Development
Practicing your data science skills will be a real boost for your career. You will be able to solve ML problems in the industry faster and become much more valuable for any company that is seeking talent with experience in the application of state-of-the-art models and methods.
Challenges cover multiple fields & tasks in machine learning
Frequently Asked Questions
What is a Data Science Challenge?
A Challenge is a goal-oriented learning journey that teaches in-demand machine learning skills through hands-on practice in an interactive and engaging way. You work with state-of-the-art methods, real-world data sets, and apply your skills to industry problems. Challenges will support your career growth by building critical data science competencies.
Do Challenges replace online courses?
Absolutely not. Online courses continue to be very important for building fundamental knowledge and are crucial for your data science education. However, Challenges will take you to the next level by allowing you to practice in-demand machine learning skills through hands-on projects. Overall, Challenges are more similar to data science competitions than online courses.
Who is Data-Driven Science for?
Anyone who already has some basic knowledge in machine learning and wants to hone their skills by regularly joining new Challenges – because practice is the only way to become truly proficient. Active skills development is key to build highly sought-after competencies and progress in your data science career.
What kind of support can I expect?
Most Challenges are on-demand and we structure them in a way so that they are self-explanatory with links to resources and supplemental material. In addition, we have set up channels for all Challenges in our online community where you can directly connect with us and other students. There will also be periodic live events to answer questions.
I have more questions — how can I get in touch?
Please send us an email at [email protected]