When I am looking through resumes, what I am really looking for is a portfolio of experiences. We’re looking for someone with the necessary background, knowledge, experience, and skill set, not necessarily of every ML algorithm out there, but there’s proof in a portfolio that you’ve tried to apply different frameworks. The idea is to come into the organization not necessarily with one framework that you know really well, but with a diversity of knowledge.

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I recommend forming a close relationship with your faculty members and your peers and establish yourselves as data scientist students so you can learn together. Speak with your faculty because they often have opportunities cross their that they’re looking for students to provide them to. Explain to your professors what you’re interested in and put your name in their mind. And as for your peers, spend time with people who you have common interests with because it makes your goals seem more attainable.

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Traditionally, you’d do a job search and send out your resume, waiting for interviews. But I took an unconventional route to land internships in data science, so it is possible! One unconventional thing I did was select my electives very carefullyu003c/emu003e and took an a capstone project working as an unpaid worker at a data science firm. This gave me the knowledge and experience to put on my resume, and for me, that experience then turned into a full internship with that firm.

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In terms of specific tools, I would say Python and R are great languages to start with to get your feet wet and get programming on some level. And I don’t think you need a degree in Computer Science to get started in Python or R or other platforms because there’s so much information available online. You can follow YouTube tutorials online, do programing challenges or tutorials, everything is out there. And then another area to focus on is visualization tools like Power BI or Tableau so you can learn to represent your data. The goal is to be able to share your work and get quick recognition for it’s usability in a non-data science space.

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I realized when I started working that collaborative skills and having an open mindset are very important. Tools change and companies adopt new policies. So you need to be able to adapt and have a growth mindset. Know that if you can teach yourself R or Python, you can teach yourself how to learn anything and you’ll be well set up for success.

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It depends on where you’re coming from. If you’re in middle school or high school, you’ll need to do something different than someone switching from another career field. So first consider where you are. That said, focus on strong mathematics, statistics, modeling, simulation, programming skills, visualization analysis – those are all important tools. Develop skills with those before you go to a Ph.D. program. If you’re in high school, take all the math classes available to you. Equally important is the ability to be able to listen to people in other fields or backgrounds, understand their problems and goals. Learn from them and communicate back effectively on technical things to people who may not be technically savvy themselves. Those are important skills if you want to be an effective data scientist.

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