6 Step Plan to Starting Your Data Science Career

When people want to launch data science careers but haven't made the first move, they're in a scenario that's understandably daunting and full of uncertainty. Here are six steps to get started.

By Kayla Matthews, Productivity Bytes on December 5, 2018 in Career, Data Science

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When people want to launch data science careers but haven't made the first move, they're in a scenario that's understandably daunting and full of uncertainty.

However, when they follow mapped-out processes that help them get into the field, success becomes easier to visualize and achieve. Here are six steps to get started:

1. Talk to People in the Field

Although it's valuable to read articles written by data science professionals or watch YouTube videos of those people being interviewed, it's ideal if aspiring data scientists can have face-to-face conversations with people currently in the field.

Some companies offer data science mentorships through an income-sharing model that allows people who want to become data scientists to receive instruction from experienced professionals. Then, if the mentees get data science jobs within a certain amount of time, they pay back the mentors with a small portion of their first-year salaries.

However, it's also possible to take a less formalized approach. People could go to data science conferences in their areas and sign up for workshops or intimate Q&A sessions with experts. There are also opportunities to get insights through message boards and Skype conversations.
Talking to people who are in the know and ready to share their experiences is crucial. It could help a data scientist-to-be confirm that the career path is the right one for them or highlight why another job choice is more appropriate.

2. Learn About Specializations

Contrary to what some people may think, there are numerous specializations for data scientists to choose. For example, people who pursue data analytics majors at Ohio State University can choose from one of five specializations, ranging from business analytics to biomedical informatics.

Outside of academia, it's necessary for people to think about how they could beef up their skills to increase the chances of working in certain data science roles. Those who are particularly problem-solving oriented may realize they should specialize in operations-related data analytics, which involves digging through information to determine how to help companies perform better.
Those who love looking through data to spot trends could specialize in statistical analysis. By taking that approach, they'd be equipped to help employers uncover patterns that ultimately drive business decisions.

Looking into data science specializations is essential when people are in the early stages of their data science careers. The knowledge they gain could help them become aware of passions that would otherwise stay hidden.

3. Peruse the Job Market as a Purposeful Browser

It may seem premature to assess the job market now, especially if landing work may be a goal that's at least a few years away. However, it's valuable to get an idea of the companies that typically hire data scientists and the skills candidates should have before looking for work.

Moreover, it's useful to find out whether certain cities or regions have higher-than-average numbers of data scientist job postings. Then, as people continue planning their careers, they can measure the likelihood of having to move to get the jobs they want.

The not-recommended alternative to doing this research early is waiting until it's time to get a job. Then, people could find out that although they've worked hard to reach their goals, they still lack necessary skills or are not prepared to relocate even though there are not many data science jobs available near them.

4. Start an Independent Data Science Project

There's no reason for people to hold off on getting engaged in data science programs until they receive a formal education. Online resources exist that let data science learners start projects when they're still beginners. They can start by browsing the datasets that are freely available, then use those as jumping-off points that inspire the questions they want to answer.

If data science enthusiasts aren't feeling up to designing independent projects, that's no problem. There are premade projects and tutorials online that pose problems for people to solve. Those possibilities let individuals begin getting hands-on experience without investing in expensive tools or training.

5. Consider Taking a Data Science Boot Camp

Many people who want to work as data scientists are already in other careers when they make that decision. So, starting a data science career may require learning how to balance the duties of one line of work while preparing for another.

In that case, enrolling in a data science boot camp could be an ideal solution. Some are as short as a week, and others are a few months long. There are also opportunities to enroll in boot camps that are entirely online.

6. Look for Data Science Internships

By the time they reach this step in the process, people getting set for their data science careers should have real-world experience through independent data science projects, and they may have opted for formal training.

Now is an excellent time for them try getting data science internships. Many people view internships as paths to their future jobs, and indeed, they can be if people impress employers enough.

However, anyone who's working toward a data science career must realize that most internships are valuable even when participants don't directly get job offers from them.
Depending on the length of the internship and the needs a company has, people might get to build data visualizations and create reports, among other tasks. Doing these things might feel overwhelming at times, but the experience gets individuals ready for their futures.

No Single Path Suits Everyone

These steps should give people the momentum they need to take decisive action for their eventual data science careers.

However, it's necessary to be mindful that although this list provides suggested foundational components, everyone is different. People may not progress in this order, and they may not perform all the steps. That's OK, as long as they keep their objectives in sight.

Bio: Kayla Matthews discusses technology and big data on publications like The Week, The Data Center Journal and VentureBeat, and has been writing for more than five years. To read more posts from Kayla, subscribe to her blog Productivity Bytes.

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