How to Become a Data Scientist

With the COVID-19 pandemic not yet behind us, the economic disruption caused by the virus continues to leave many without work. In January 2021, the US unemployment level stood at over 10 million, all while American business leaders predicted a coronavirus-induced recession to last through the remainder of 2021.

Despite the struggle, some key trends of both the job market and business world have continued at pre-pandemic rates. One is the need for businesses to increase their capacity for big data and make sense of newly digitized business models Another is the related demand for professionals with significant data know-how. These trends are so powerful that hiring for these roles grew nearly 46 percent from 2019 into 2020, helping land data science specialists on LinkedIn’s 2021 rankings of fastest-growing jobs. Note that growth occurred at a time when the overall economy was shrinking.

Although a challenging path, a career in data science is worthwhile for many reasons. Job security aside, the field offers opportunities to tackle creative, complex tasks and transform tech as we know it. But forging a data science career also requires considerable dedication and effort. So, where should you get started? Let’s take a look.

Our guide to how to become a data scientist covers:

• What is a data scientist?
• How to become a data scientist
• How long does it take to become a data scientist?
• Why become a data scientist?
• How much does a data scientist make?
• Should I become a data scientist?
• Top data science master’s programs
• Top online data science master’s programs

What is a data scientist?

As organizations grapple with making sense of an extraordinary amount of disparate data, data science continues among the most in-demand career paths. It is an especially apt choice for professionals who’ve not only mastered the full spectrum of the data science life cycle but also possess a level of flexibility and understanding to maximize value at each phase of the process.

Put simply, data scientists develop processes for sourcing, managing, and analyzing large amounts of structured and unstructured data to help shape or meet specific business needs and goals. They often collaborate with data analysts, data engineers, business intelligence specialists, and data architects to create and maintain data-based deliverables like prediction engines, pattern-detection analysis, and optimization algorithms. More specific responsibilities may depend on their industry and the needs of their organization or department.

Data scientists’ work typically combines domain expertise, programming skills, and knowledge of mathematics and statistics, as well as an aptitude for visualizing data using graphical techniques. Some data scientists may also be responsible for setting best practices for collecting data as well as synthesizing and communicating the results of their research to key stakeholders.

What is data science?

Data science is the extraction of insights and information from raw data. The field involves a blend of data inference, algorithm development, and technology to uncover findings that can help organizations understand complex behaviors, trends, and inferences. It frequently leads to smarter business decisions.

As data science grows and evolves at an ever-quickening pace, the field’s impact is becoming increasingly clear—and profitable. Take Amazon Web Services (AWS), for example. The pay-as-you-go cloud computing service from Amazon designed to help businesses harness data to build, launch, and scale their organizations closed out 2020 with more than $13.5 billion in annual operating profit. It was responsible for more than 63 percent of the entire company’s operating profits for the year.

Types of data science jobs

As data scientists increasingly enter the job market with different skillsets and strengths to suit different types of organizational needs, professionals in the field have moved away from “data scientist” as an umbrella term. These days, a data science career can take on a wide range of roles and responsibilities, such as:

• Business intelligence (BI) analyst: BI analysts equip managers, executives, and departments with real-time actionable insights to enhance workforce efficiency, increase productivity, strengthen market positioning, and improve competitive edge, among other goals. Besides tasks involving data analysis, data modeling, and data warehouse management, their day-to-day activities often involve interacting and collaborating with organizational stakeholders, giving presentations on key performance metrics, and writing reports to share insights gained from their work.
• Data analyst: As a more junior data scientist role, data analysts usually spend their time developing systems for collecting data and compiling their findings into reports to help boost organizational efficiency and effectiveness. Depending on their employer and team size, they may have a hand in many points across the analytical process or play a more specialized role, such as designing and deploying algorithms or identifying new process improvement opportunities.
• Machine learning engineer: Machine learning engineers (ML engineers for short) design and implement machine learning applications to automate predictive machine learning models. Each time the application operates, it uses those results to carry out future operations with a greater accuracy rate. As the amount of data grows, the machine learning algorithm’s recommendations become more accurate—all without direct human assistance.
• Software engineer: Whereas data scientists deal with data extraction, data cleaning, statistical analysis, and building statistical models, software engineers generally deal with creating functional and scalable software systems. However, as machine learning becomes an increasingly integral part of many new products, the overlap between software engineering and data science responsibilities grows too, from working with unstructured data, web crawlers, and data scraping to applying knowledge to scalability and data reuse.
• Statistician: Like data scientists, statisticians collect, organize, analyze, and interpret data to help predict trends, understand quantitative information, and make better business decisions. Day-to-day, their responsibilities may include analyzing and interpreting data to identify relationships and trends in data and any factors that could affect research results. They also evaluate the methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.

How to become a data scientist

Forging a data science career is no small undertaking, not only in terms of education but also in mastering the skills and tools that are typically required. At the same time, a universal agreement on a precise set of preferred qualifications is hard to come by, making it challenging for a beginner to know where to start. The qualifications listed below, however, can lend a significant leg up in your career path—and maximize your chances of success in the job market.

Skills

Data science requires a diverse set of skills, problem-solving chief among them. As problem solvers, data scientists must have as much curiosity to dig to the root of issues as they do a knack for using the most effective existing methods and resources to solve them. Skills in this realm—alongside others like communication skills and business acumen—don’t require extensive formal training or certification. Still, they’re crucial among the soft skills that data scientists need to succeed.

While different businesses will use different tools and languages in their workflow, most data scientist roles require candidates to possess a broad scope of STEM-based expertise, making the list of technical skills that they must be familiar with extensive. Data science skills in this realm include in-depth knowledge of programming languages like Python, Java, and SQL and how to apply them to techniques such as machine learning, artificial intelligence (AI), data visualization and wrangling, regression modeling, and data mining. A strong familiarity with mathematics—such as calculus, linear algebra, and statistics—involved in these techniques is essential, too, as it enables data scientists to think critically about the value of various data sets as well as any limitations they may present.

While not always required, many employers also prefer candidates experienced with data analysis and big data tools like Hadoop and Spark, as well as platforms like GitHub, which can be used to organize work, track changes, and provide a clear and well-documented path for analysis and quality control.

Education requirements

According to 2020 findings from Burtch Works Executive Recruiting, data scientists’ education typically extends further than a bachelor’s degree. In their survey of over 500 data scientists whose roles range from entry-level to hands-on to subject experts, most data scientists reported that they hold an advanced degree. More specifically, 50 percent indicated that their highest level of education was a master’s degree, which another 43 percent responded that their highest was a PhD.

While Burtch Works’ sample size was relatively small, their findings do reflect degree requirements for data scientists positions in general. A strong educational background is often a must-have to develop the depth of knowledge that data scientists require. Outside of the immediate data science realm, common fields of study include mathematics and statistics, computer science, physics, engineering, and other related fields.

Certifications and other training

Completing certification focused on specific computer platforms and technical skills can help make data scientists more marketable candidates, whether they’re looking for jobs, securing a promotion, or enhancing their existing skill sets.

Certifications from industry-leading organizations and commercial businesses are widely recognized and valued in the field and include:

• Certified Analytics Professional (CAP)
• Cloudera Certified Associate Data Analyst
• Cloudera Certified Professional: CCP Data Engineer
• Data Science Council of America (DASCA) Principal Data Scientist (PDS)
• Data Science Council of America (DASCA) Senior Data Scientist (SDS)
• Dell EMC Data Science Track (EMCDS)
• Google Professional Data Engineer Certification
• IBM Data Science Professional Certificate
• Microsoft Certified: Azure AI Fundamentals
• Microsoft Certified: Azure Data Scientist Associate
• Open Certified Data Scientist (Open CDS)
• SAS Certified AI and Machine Learning Professional
• SAS Certified Advanced Analytics Professional
• SAS Certified Big Data Professional
• SAS Certified Data Scientist

Can I be a data scientist without a degree?

Despite the preference commonly given to advanced degrees, it’s possible to gain the data science skill set needed to enter the field without a master’s or doctorate. For the most part, any formal education gaps can be made up for with an impressive portfolio of data science projects. Proven participation in industry events like Kaggle Competitions and Hackathons can be a big help too.

There are plenty of resources available for growing your expertise, too. Books, bootcamps, blogs, and massive open online courses (MOOCS) from platforms like Coursera, edX, and Kaggle offer many options for targeted skills development through affiliations with prominent educational institutions and companies, as well as pathways for a range of certifications in the field.

Aspiring data scientists should also use social media and LinkedIn to keep tabs on companies they’d like to work for. They should also network aggressively. Often—especially in the non-profit realm—opportunities exist in the form of short- or long-term data science projects or collaborations that individuals can donate their time to in exchange for real-world industry experience.

How long does it take to become a data scientist?

Ultimately, the time it takes to become a data scientist depends on your educational background and existing skills and experience, and the path you choose to take to realize a career in the field. That part is clear. But a universal timeline? Not so much.

Those who consider themselves true beginners will not only have to learn the basics but build up their mastery of data science. They’ll have to build projects that answer crucial questions and showcase their skills, engage with others in the field, and push the boundaries of their work. For those who don’t have a STEM-related bachelor’s or postsecondary degree and don’t plan to pursue either, the time it takes to upskill is murky and largely depends on work effort and self-discipline.

Those who have a bachelor’s degree and experience in programming or computer science have a bit of a head start and may choose to upskill through a more clear-cut timeline: the master’s degree. Programs in the field of data science generally encompass a minimum of 30-semester credits and typically permit students to earn their degree in a length of time that depends on their course format and other factors. The online Master of Science in Data Science from the University of Virginia Main Campus, for example, accommodates the needs of students balancing professional and academic goals, allowing them to complete their degree in as little as five terms or two years.

Why become a data scientist?

There are many motivations behind pursuing a data science career. For starters, data scientists can work virtually anywhere, with opportunities spanning healthcare and pharmaceuticals, finance, marketing, retail, manufacturing, gaming, consulting firms, and government.

What’s more, their place among the most sought-after professionals in tech continues to hold today despite the overall job market’s decline resulting from the coronavirus pandemic. As millions of Americans kicked off 2021 unemployed, data scientists claimed second on Glassdoor’s 50 Best Jobs in America in 2021 based on salary (more on that later), job openings, and overall job satisfaction.

How much does a data scientist make?

The foremost driving factor behind lucrative data science salaries is the ever-growing number of organizations that realize the power of big data to drive smart business decisions and need qualified candidates to help them do that. The supply of data professionals hasn’t yet caught up with demand, so starting salaries for these positions remain high. Per Glassdoor’s most recent list of Best Jobs in America, data scientists make a median base salary of $113,736 per year.

Other data science-focused roles can expect the following in average annual base pay, which we’ve sourced from Glassdoor salary data:

• BI analyst: $76,402
• Data analyst: $62,453
• Machine learning engineer: $114,121
• Software engineer: $92,046
• Statistician: $76,884

Should I become a data scientist?

While high salaries, job security, and career satisfaction are all selling points of the data science field, these factors alone can’t guarantee that it’s right for you. Weighing these pros with other criteria can help make the decision process easier, however.

First, it’s essential to reflect on your personality type, as it influences the kinds of roles you might naturally gravitate toward, as well as your strengths. Are you curious and results-oriented with a strong desire to learn? This is crucial, as designing new data science techniques to boost efficiency is an essential part of the job—and the amount of data available to analyze is ever-growing. Are you a clear and rational thinker who never accepts a statement at face value? This disposition is a must for data scientists, as it empowers them to cultivate clear and sensible plans for approaching data and analyzing it objectively.

Identifying your interests can also help you determine whether the field suits you and even uncover subfields or niche areas in the data science field that you may be especially passionate about. Are you motivated by how customers think, act, and spend? Business intelligence might be the ideal career path. Do you have a passion for problem solving and process but aren’t the most technical person? Operational analytics could be an ideal path, as it focuses on leveraging the tools and data provided by the other members of the data science team to find solutions.

Another crucial part of your decision-making process is your current level of education and industry experience, which must be weighed against the typical requirements of data science careers. Remember, while starting in the field with a bachelor’s degree in a relevant field coupled with completion of a coding bootcamp or extensive MOOCs is possible, often, a master’s degree in data science or similar discipline is necessary.

While this path may require a significant financial investment, a wealth of financial assistance options exist through loans, work-study programs, Pell grants, and other resources. Some programs, like the text online MS in Data Science from Stevens Institute of Technology, offer assistantships that may pay for a student’s tuition alone, tuition and fees, or part of their tuition, as well as a range of internal and external fellowships.

Top data science master’s programs

We consulted US News and World Report‘s most recent rankings of top schools, among other resources, to identify top master’s in data science, data analytics, or a closely related field. Top programs inlcude:

• Carnegie Mellon University
• Columbia University
• Cornell University
• Georgia Institute of Technology Main Campus
• Harvard University
• Princeton University
• Stanford University
• University of California – Berkeley
• University of Washington
• University of Wisconsin – Madison

Top online data science master’s programs

Top online data science programs include:

• Auburn University
  Ball State University
• Boston University
• Drexel University
• Johns Hopkins University
• Pennsylvania State University – World Campus
• Stevens Institute of Technology
• University of California – Riverside
• University of Southern California
• University of Virginia Main Campus
• Virginia Polytechnic Institute and State University

Questions or feedback? Email editor@noodle.com