Becoming a data scientist is not easy.
It requires the right education, previous data science experience, and relevant expertise and skills.
Although you can launch a career without all three, your road will generally be much harder.
To help us determine what you need to become a data scientist, Interview Query analyzed the profiles of more than 15,000 FAANG and start-up professionals on LinkedIn to see what the most common paths to becoming a data scientist are. These are the steps to becoming a data scientist that we found in our analysis:
A data scientist has two main functions. First, they search for insights in large datasets by analyzing patterns and anomalies. Second, they make recommendations based on this analysis, and answer questions like: How should we improve upon this? Is something working? What is it that you want to know? How can we turn this insight into something valuable?
Some of the most important functions of a data scientist include:
Data analysis - Data scientists help organizations analyze the large volumes of data that they create. In particular, a data scientist identifies analytics problems for the company, cleans and prepares the data for analysis, and analyzes the data for trends and patterns.
Communicating with non-technical stakeholders - Data scientists communicate actionable insights to various departments (like marketing, product, or operations) to reduce costs, improve decision-making, and/or improve efficiency.
Building models - Not only do data scientists analyze historical data, they also use machine learning and statistics to make predictions, to forecast, and to develop models that improve operations. Fraud detection and credit card approval models are two examples of how data science is commonly leveraged in finance.
It’s well known that you need at least a Bachelor’s degree to launch a career in data science, given the advanced statistics and mathematics background required. However, it’s unclear how common advanced graduate degrees are for data scientists.
We scraped 15,000+ LinkedIn user profiles of data scientists that worked at top tech companies like Facebook, Google, Amazon, etc…
A few things stand out from the data. First, it’s exceedingly difficult to become a data scientist without a Bachelor’s degree. Second, a Master’s degree is becoming increasingly common among data scientists. Here’s what the data shows:
Also note that only 3% of data scientists have completed certificates. This runs contrary to what many certificate companies want you to believe - that you need certifications to become a data scientist.
Given that around 2⁄3 of all data scientists have a Master’s degree, does this mean that getting a Master’s is required to become a data scientist?
We found that previous data science experience is a more important factor than getting a Master’s degree. Specifically, as my data scientist friend Tyler states - a Master’s is definitely a plus but not a requirement:
However, Master’s degrees in data science are a solid solution for:
Ultimately, a Master’s degree provides intensive training and can help open doors. But internships or jobs in related positions (like data analytics) can be just as effective at helping you land a job in data science.
The ideal solution is a Master’s degree combined with professional experience, which usually qualifies candidates for mid-level and higher data scientist positions.
What do you need to study to become a data scientist? You can study any subject and become a data scientist. However, it’s easier if you study a topic adjacent to data science, if not directly within the field. Some of the most commonly-studied fields for data scientists include:
Computer science is widely recommended for aspiring data scientists because it provides a strong basis in programming. A CS degree expands your choices if you decide you want to go down the path of software engineering, which is often a prerequisite to data science. And it also broadens your options across multiple fields.
Mathematics is another highly recommended field of study for aspiring data scientists. Studying statistics or applied math for data science careers will provide a strong basis in complex mathematical theories, how they are applied to real-world problems, and how to communicate their complexities.
“Any subject in math that focuses on going from a discrete dataset and generating a continuous conclusion, forecast, or estimation is useful in data science. And that’s basically what you will study in applied math,” says Simon, who earned a master’s in applied math and now works in data science.
In particular, applied math provides in-depth training in numerical analysis, combinatorics, graph theory, numerical linear algebra, statistics, and probability, all of which are used extensively in data science.
“In data science, oftentimes, you’re looking for correlation and causation, and [a master’s in applied math] develops your ability to determine if a correlation is actually meaningful and how to quantify the meaningfulness of correlations.”
After analyzing the education levels of data scientists, it’s clear that the skills and experiences you have matter most of all. Someone with a PhD who can’t do basic SQL could easily lose the job to a SQL whiz with in-depth machine learning expertise.
To become a data scientist, you need these skills:
Python Programming - Data science jobs require strong Python skills, including data mining, data analysis with pandas, scripting, data manipulation, and data aggregation.
SQL - SQL is used every day by data scientists to manage and query data. To land a job in data science, you should excel in SQL code writing.
Machine Learning - Not all data scientists are ML experts. Therefore, if you have strong machine learning skills, you become more competitive for data science jobs. In particular, successful candidates can use ML and AI for automating tasks, like data cleaning and advanced statistical analysis.
Product / Business Sense - Data scientists must understand how to solve product and business problems with data science. Therefore, a strong understanding of product development, business models, and marketing are essential to get a data science job.
Statistics and Probability - Data scientists use statistics and math for analysis, forecasting, algorithms, etc. Therefore, it’s important to have a working knowledge of statistics, linear algebra, calculus, and probability to excel in the field.
Ultimately, you don’t need to pursue a degree to gain these skills. However, a Bachelor’s degree program in a field like computer science, statistics, or engineering can provide many of the required skills.
Another option: Consider one of the many recent graduate data science mentorship programs that offer training and on-the-job experience. These programs help new grads build the skills they need for success in the field.
A question that comes up a lot for new graduates and people transitioning in their careers is, “How much experience do I actually need?”
The majority of professionals in our sample had 2-3 years of professional experience before landing their first data science jobs. Many got that experience through internships or working in a data science adjacent job first.
Here’s a look at both of these career paths:
Of the people who became data scientists, 25% had done an internship before landing their first job.
Internships are huge boosters to your resume. They show value in terms of working in a professional environment and act as professional proof. On a resume, internships command attention and prove experience.
Most data science jobs are like any other kind of credential funnel: the more that you have, the better off you’ll be. But to many hiring managers, internships outrank other credentials like grades, courses, certificates, or projects, because they prove experience in a similar role.
If you want to work as a data scientist… consider taking a job that’s related to data science first. From our research, more than 50% of those who went on to become data scientists worked in another role beforehand.
Data analyst was far-and-away the highest ranked previous job, followed by software engineering, machine learning engineer, and data engineer. Similarly, finance to data science and tech consulting to data science were other common paths into data science.
The data analyst to data scientist transition is one of the most common pathways into data science. For example, Armon, an Interview Query member, worked in business analytics before landing a data science job at Facebook.
Data analysts gain hands-on experience analyzing and drawing insights from data, skills that are applicable to any data science job. Consequently, as they progress and excel in analytics positions, many move up the data stack to data scientist.
There are other paths you can take. You could take contract positions and freelance data science work on Upwork, which was Alex’s path to becoming a data scientist at NextNetwork.
Finally, working with a mentor is another option to build data science skills and receive guidance for self-directed learning. However, mentorships generally aren’t as reliable as internships or having professional experience.
Let’s say you’re about to graduate, and you want to land a data science job with no experience. Although experience is highly valued by hiring managers, there are strategies new grads with no experience can use to make themselves more competitive and that look good to recruiters and hiring managers.
Here are some strategies you can try:
Writing about data science helps you:
Additionally, writing improves your ability to communicate data science concepts and techniques, and it can help you learn and increase the depth of your domain knowledge.
But ultimately, writing helps you build an audience for your work, and if your blog post captures the attention of a recruiter or hiring manager, you’re getting a backdoor into the interview process.
Interview Query’s founder Jay landed his first data science job after doing a regression analysis project on Seattle housing data, writing about it, and promoting the blog to the front page of Hacker News.
You can do data science projects on your own, or you can find opportunities for paid work on sites like Upwork. Projects increase domain knowledge like:
If you’re a non-US citizen looking for jobs in the US, consider a visa sponsorship. Visa sponsorships for data science jobs–like H-1B visas for highly specialized workers, L-1A/1B visas for MNC transfers, or F visas for pursuing a PhD–are all options that can open doors to jobs in Silicon Valley and the US.
Leveling up your skills is always a good path to pursue, and there are several options for those who don’t necessarily want to pursue a Master’s degree. Data science bootcamps are an alternative that provide intensive training.
Start-ups are great for new grads with no experience. First, you’ll have a chance to work on a variety of projects, which will help you identify what areas you love most. Secondly, start-ups vs FAANG companies are more inclined to hire new data scientists.
At the end of the day, setting up your portfolio, building up projects, and getting interviews is just one-half of the job search process. Passing the interview is another barrier to entry that will help you land your first data science job.
One suggestion: learn from successful candidates. For example, to become an Apple data scientist, Taher practiced a variety of SQL and product sense questions. Similarly, a difficult Amazon question taught Janette the importance of slowing down and asking clarifying questions during data science interviews.
You’ll also find more interview experiences, interview guides, and resources on Interview Query to help you become a data scientist: