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How to Launch Your Data Science Career

Are you interested in getting into the field of data science? We don’t blame you


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Ido Zehori

3 years ago | 3 min read

1. Get comfortable with Python and SQL

Learning Python and SQL will serve you well as you dive into your data science career. For python, There’s an entire ecosystem of data science packages and tools that you should learn. To help, install the Anaconda distribution and check out this great resource to get you started pythonprogramming.net. For your first steps in SQL, we recommend you check out the great w3schools.com.

2. Take online courses

Gone are the days when a degree was a must-have for launching a new career. These days, you can learn independently through online courses and get equipped with the skills and know-how you’ll need to succeed in data science. Look into the great Andrew Ng. Gain an understanding of the basic concepts of computer science, statistics and math — and you’ll be good to go. Some recommended courses and resources you can look into include:

  1. Machine Learning by Stanford University
  2. Deep Learning Specialization with Andrew Ng
  3. Statistics with Python Specialization from the University of Michigan
  4. Brilliant.org

3. Compete in Kaggle

Building a portfolio is essential for aspiring data scientists the same way it’s essential for artists. With Kaggle, you can do just that. Kaggle is the world’s largest data science community, offering tools and resources that can help you jumpstart your data science career. Kaggle also hosts competitions where data scientists around the world can compete to produce the best models for predicting and describing data.

Want to compete? Here’s a tip: Don’t give up on a competition until you’re in the top 10% of submissions and have your name up there on the leaderboard. This will force you to push yourself, and as a result, you’ll become familiarized with the best practices of the domain and the most modern tools and frameworks.

4. Familiarize yourself with the full stack developer world and toolkit

Data scientists spend most of their time at work writing code. But data scientists aren’t just coders — they’re technologists. To write good code and make a stronger impact, it’ll serve you well to get closely familiar with the world of technology and the best methods for doing things.

5. Listen to podcasts and read tech blogs

Podcasts and technology blogs offer a fantastic way to stay up-to-date with everything that’s happening in the world of data science. Podcasts also offer a great way to hear from some of the greatest minds in the field about the most pressing issues in the industry and about what they’re currently working on and thinking about. You might even encounter a story from someone who has solved a problem similar to one that you’re working on, and it’ll give you some fresh ideas and insights. This is what happened to us when we first started building our feature store, which you can read about here.

Want to know where to start? The number of great data science blogs can be overwhelming. We recommend starting with these:

As of this writing, these data science podcasts are active and still in production. Start deep in the archives and work your way up:

6. Talk to people

Join forums and other online groups where data scientists are talking with one another. You’ll find that most data scientists are facing the same types of issues. Talking with others and understanding how they’ve solved various problems they’ve faced will help you learn and move forward.

Conclusion

We live in an exciting time where there’s more access than ever before to specialized information that can help us boost our career. This applies to people seeking to launch a career in data science, too. With courses, groups, competitions and information that are all easily accessible on the web — and with practice and dedication — you can acquire the skills you need to start a successful career as a data scientist

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Ido Zehori

Data Science Team Leader @ Bigabid. Creating real business impact with Data Science and Machine Learning.


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