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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. Data science is an exciting field that’s constantly changing and developing, which gives data scientists’ work endless potential.

Here are six tips for ways that you can launch your data science career.

  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. 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, as it 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 is a tip: Don’t give up on a competition until you are 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.
  2. 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 will serve you well to get closely familiar with the world of technology and the best methods for doing things.
  3. 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 are currently working on and thinking about. You might even encounter a story from someone who has solved a problem similar to one that you are working on, and it will 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, but a great place to start is with Kaggle blog.

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

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

We live in an exciting time where there is 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.

About the Authors

Ido Zehori is the Data Science Team Leader and Mia Dor is a Data Scientist, Research Architect at Bigabid, a data science company that has developed a second-generation DSP optimized for in-app advertising user acquisition & re-engagement for mobile app developers. 

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