Photo of a review of Kaggle as a data science resource

A review of Kaggle as a data science resource

Hi, I’m Olga! I have years of experience in data science, most recently at eBay and now I work as an industry mentor at Pathrise, helping data scientists land a great role through technical workshops and 1-on-1s.

What does Kaggle do?

Kaggle is a platform with resources for people who are interested in data science. They have 19,000 public datasets and 200,000 public notebooks for people who are looking to get started or contribute to data science projects in order to advance their skills or build up their portfolios. They offer a no-setup, customizable, Jupyter Notebooks environment with access to free GPUs and a huge repository of community published data & code.

Photo of Kaggle platform

In addition, they have micro-courses on topics like machine learning, Python, deep learning, and more, which take about 3-7 hours, as well as competitions to solve real-world data science problems. These competitions give users the opportunity to put their skills to the test and learn from the community of over 3 million other data scientists. Some of the competitions also include prize money, which can be as much as $100,000.

On their blog, Kaggle provides even more data notes and sets as well as news, tutorials, and interviews with prominent people in the field. They also have a job board where they source open data science positions from around the world. Candidates can subscribe to get the job board in their inbox. 

Who is Kaggle for?

Data scientists of all levels can benefit from the resources and community on Kaggle. Whether you are a beginner, looking to learn new skills and contribute to projects, an advanced data scientist looking for competitions, or somewhere in between, Kaggle is a good place to go.

What does Kaggle cost? How much work is involved?

All of the resources on Kaggle are free. In order to access them, users just need to create an account using their email address or Google account. Some of the micro-courses have prerequisite knowledge that is needed in order to understand the content. This depends on the topic of the course.

Ratings and reviews

The Kaggle community highly rates the platform and the users really enjoy the competitions and opportunities to continue learning. There are lots of positive stories of people who are novices in competitions who grow to become very strong, even winning some. 

That being said, for the most part, people describe Kaggle as beneficial for users who already have some background in data science. There is almost no introductory material, and most of the micro-courses (and all of the competitions) require some background knowledge in data science languages (like R or Python) and machine learning. Once the user has this background, they can use Kaggle to continue learning and advancing their skills.

Alternatives to Kaggle

For data scientists who are looking to join a community and contribute to projects,

  • GitHub is a good alternative to Kaggle. There are 40 million developers, which includes software engineers and data scientists, on GitHub and a large number of open source projects.
  • For people with an interest in data science, Metis has part-time introduction courses as well as onsite and online bootcamp options. Learn more about Metis in our review.
  • If you are looking to advance your data science skills through a capstone project (and you have a fair amount of experience), The Data Incubator could be a good option.
  • DataCamp has courses, skill tracks, and career tracks. Users can filter by technology (R, Python, SQL, Git, etc) or by topic (programming, machine learning, data visualization, probability & statistics, etc).
  • Similarly, Dataquest offers online, 24-week project-based data science courses focused on data analysis using R and Python.
  • You can check out Udacity and Springboard, which are online educational platforms that students can use to brush up on the basics or advance their current tech skills. Read more about Udacity and Springboard in our reviews.
  • Users can also consider Udemy, Coursera, and Pluralsight, which are all online platforms that offer courses on a variety of topics within the umbrella of data science.
  • Coding Temple and RMOTR are Python data science and web development bootcamps, focusing on the fundamentals.

For more alternatives, check out our best resources to learn data science.

How does Kaggle compare to Pathrise?

Kaggle is a community for data scientists that includes competitions and some micro-courses as well as repositories and datasets for projects. It is a great resource for people who are looking to expand their knowledge and keep their skills sharp.

At Pathrise, not only do we provide 1-on-1 and group technical interview prep, including whiteboarding sessions, but we also work with our fellows on behavioral interviews, resume and LinkedIn optimization, portfolio building and strengthening, cold email and reverse recruiting, and negotiation templates and guidance. The community of current fellows and alumni provide job-seekers in our program with support as they move through the often difficult job search.

That being said, there is definitely an opportunity for fellows to use Kaggle as a resource alongside Pathrise, especially for data scientists who want to use the competitions as interview prep. We always encourage fellows in our program to practice and learn as much as possible, which means that Kaggle is a good place to go for our data science fellows.

Pathrise is a career accelerator that works with students and young professionals 1-on-1 so they can land their dream job in tech. With our tips and guidance, we’ve seen our fellows interview performance scores double.

If you want to work with any of our advisors 1-on-1 to get help with your technical and behavioral interviews or with any other aspect of the job search, become a Pathrise fellow. 

Apply today.

Pathrise logo

Leave a Reply

Your email address will not be published. Required fields are marked *