How to Become a Data Scientist in 2021: A Guide to Getting an Entry-Level Position

Maybe you always knew you were a math person. You might have discovered your knack for logical puzzles as a child or realized in college that the data sets that bamboozled your friends weren’t all that confusing to you. Or maybe the epiphany came later — after you spent a few years in a career that didn’t speak to you in the way that math and analysis did.

In any case, if any of the above scenarios feels familiar, you’ve probably determined that you want to explore building a career in data science. The question is, how can you get there?

Everyone who wants to become a data scientist will need to take several key steps. These include choosing a career path and expanding your industry knowledge. Further education is a must; your options include a traditional college program or data science bootcamp, which has become a more popular choice among working adults.

In the below guide, we’ll walk you through what you’ll need to do to obtain an education, what to expect from an entry-level position, and how you can increase your employment potential.

5 Steps to Becoming a Data Scientist

There’s no doubt that a career in data science can be both personally rewarding and financially lucrative, but you shouldn’t opt into it lightly. Make no mistake — becoming a data scientist takes time, effort, and dedication.

That said, if you’re determined to make your mark on the field, we can help you start on the journey! Below, we’ve covered the steps you’ll need to take when aspiring to enter the data science field.

1. Choose a Path

Data science is a broad field that offers countless opportunities for growth and success. If you are looking to become a data scientist, it will be beneficial to identify which industry you ultimately want to work within.

Generally speaking, data scientists tend to perform similar duties across industries. However, the kind of information you will manage, what you analyze, and how you present your findings will vary according to your employer and sub-field. If you’re planning to become a data scientist, it may be helpful to have a general idea of where you want to work — though you should be careful not to limit yourself too early in your career.

While data science is used across most industries to some degree, there are five primary sectors in which data scientists tend to thrive: retail, healthcare, finance, manufacturing, and transportation. These industries tend to have a high demand for data scientists because they need to collect massive amounts of data, conduct accurate, efficient analysis to identify trends, provide insights that assist in business decision-making, and optimize processes for their operations, clients, and customers.

Data scientist skill sets are relatively standard, but their applications may vary across industries. Here’s a quick rundown:

  • Retail: Will need strong communication skills and an eye for detail, especially if they are expected to present their findings to business leaders or consumers.
  • Healthcare: Will need a thorough understanding of medicine and healthcare terminology as well as medical record management and spreadsheet analysis.
  • Finance: Will need to be proficient in Excel and data collection from diverse sources (including face-to-face interactions).
  • Manufacturing: Will need an advanced understanding of predictive analytics, as one of their primary roles will be determining when machines will need maintenance or repairs.
  • Transportation: Will need to be top-notch collaborators with exceptional data visualization and econometric modeling skills.

It may help prospective data scientists to look into these industries and determine which align most closely with their natural skills and interests. However, it is worth noting that data science is relevant and useful to the vast majority of industries — so don’t feel as though you need to limit yourself!

2. Brush up on Essential Skills

In terms of essential skills for a data scientist role, mathematics and computer science rank at the top of the list.

If it has been several years since you took a class, or you don’t regularly apply mathematical knowledge in your current career, refreshing your memory and studying relevant topics will be immensely beneficial — especially since most data science courses expect you to have a grasp of applicable principles and practices before coming to class.

Math skills aspiring industry professionals should brush up on include (but are not limited to):

As far as technical skills go, an understanding of coding languages such as Python, R, Java, SQL, NoSQL, and HTML will be necessary. Additionally, proficiency with industry-relevant software like Hadoop (which is useful for data storage and management) or Tableau (which allows data scientists to isolate data based on variables) can make aspiring data scientists more marketable to potential employers.

Soft skills such as problem solving, critical thinking, and communication are also crucial. Data scientists should be skilled at identifying obstacles, proposing and trialing solutions, and conveying their findings in a clear, comprehensive fashion.

If you don’t have a handle on the mathematic, technical, or soft skills mentioned above, you may want to set aside some time to study up and ensure that you’re prepared for your data science coursework. Even if you already have some relevant knowledge or experience, a dedicated effort to increase your understanding and improve your skills will be valuable.

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3. Get an Education

Specialized education is a necessity for data scientists. It doesn’t matter how much of a knack you might have for statistics or linear algebra — if you don’t have certain industry skills and theoretical knowledge, no one is likely to hire you.

Thankfully, accessing a data science education is easier than ever. Below, we’ve outlined the three common academic paths that data scientists often take on their way into data science. Each comes with its own set of pros and cons, so think carefully before deciding on a path!

A chart that shows how to become an entry-level data scientist by comparing the top educational options.

College Education

Time Commitment: 4–7 years
Budget: $$$
Flexibility: Full-time, online options

While a formal degree isn’t always necessary for aspiring data science professionals, obtaining one is a common educational choice and can help off set a lack of professional experience. According to a 2020 Burtch Works study (PDF, 1.1MB), the majority (83 percent) of predictive analytics professionals (PAPs) hold advanced degrees — this breaks down to 68 percent holding a Master’s degree, and 15 percent holding a doctorate.

To lead a successful data science career, you need to ensure your knowledge of relevant principles and practices is sufficient. If you already have a background in mathematics or coding, pursuing a Master’s in data science may be a suitable course. If you want to earn a comprehensive and accredited education in the field, such programs are a good choice; today, many even offer flexible online options for busy learners.

However, if you’re just starting and plan to enroll in an undergraduate degree, don’t worry. The Burtch Works study also noted that the importance of additional schooling has declined in recent years, as the number of people who have a bachelor’s as their highest degree has increased by three percent year-over-year. You may be able to get an entry-level data science degree without advanced schooling.

Naturally, this education path is the most expensive and time-consuming, so if you have limitations in these regards, you may want to consider alternatives.

Data Science Bootcamp

Time Commitment: 3–6 months
Budget: $$
Flexibility: Part-time, full-time, online options

For working adults, individuals with an unrelated undergraduate degree, or motivated hobbyists, a data science bootcamp presents an appealing option to start on your path into the field. These courses take anywhere from 3 to 6 months — and while they are short, they are thorough and intensive.

A background in related fields will help those attending bootcamps, but it is not strictly necessary. This accessibility makes these intensive programs suitable for dedicated novices and individuals looking to further their existing knowledge or give themselves a refresher course on data science topics.

Bootcamps look good to employers, too. According to HackerRank’s 2020 Developer Skills Report (PDF, 2.4MB), employers typically view individuals who have completed a data science bootcamp as qualified, hard-working high performers. Roughly 40 percent of surveyed developers believe that bootcamp-trained candidates are just as qualified as their college-trained peers, and 33 percent believe that bootcamp alumni are more qualified.

While bootcamps are considerably shorter and less expensive than a college education, they do require participants to commit a substantial amount of time and effort to their lessons during the course and to their experience building and job search afterward. As with any other formal education, interested individuals should understand that they will need to be disciplined and diligent to reap the most benefits from the bootcamp.

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Self-Guided Learning

Those who have reservations about committing to a formal education may instead opt for a self-guided education. This is comparatively the most flexible and inexpensive option. However, unlike the other two highlighted above, independent study does not provide a concrete “graduation” that marks learners as being adequately prepared to handle the job duties of a data scientist.

Additionally, some employers may prefer job applicants who have a degree or a bootcamp certification that “proves” capability. Lacking these credentials may put you at a disadvantage during the job search, so pursuing additional certifications as you learn on your own is a good idea.

You should only opt for a self-guided education if you are intrinsically motivated and self-accountable; those who do not fall into these categories may find themselves procrastinating their lessons to the point of arresting their career journey.

4. Know What to Expect From an Entry-Level Role

Before applying to open data science positions, you should know what to expect from an entry-level position.

While the ways companies use data will vary, data scientists often provide analysis to improve cost-efficiency and financial performance, influence strategies, manage risk levels, and assess product or service quality and success. Knowing how to use the tools of the trade is a must, so you should be confident in your abilities and be able to demonstrate your expertise if prompted.

The primary responsibilities of an entry-level data scientist include:

  • Setting up and managing studies to facilitate data collection
  • Building tools to automate data collection
  • Reformatting data to make analysis easier
  • Parsing large data sets for usable information
  • Using data to develop strategic business conclusions
  • Creating reports and presentations

Another point to consider is industry specifications — when considering job listings, you should identify the elements of data science used in the role. Data scientists provide a vital service to companies, government entities, and other organizations by processing and distilling insights from immense amounts of data. That said, each business or organization may have different priorities, goals, and restrictions, and any existing knowledge you have will serve you well.

At the end of the day, an entry-level role requires data scientists to understand data: where it comes from, as well as how to process, gather, and analyze it. You will undoubtedly encounter nuances distinct to each open position, but entry-level data science positions tend to present similar expectations. With these in mind, you can better tailor your resume, professional portfolio, and other materials before submitting a job application.

5. Boost Your Hiring Potential

The demand for data scientists is growing, but the industry is also highly competitive. It’s not enough to have the skills and the will to use them; you also need to stand out among your peers.

Let’s walk through a few methods of doing so.


In any field, networking is a key part of launching your career.

Connecting with data science professionals on social media sites like LinkedIn or Handshake, where you can connect with alumni from any programs you have completed, is one of the simplest and most effective ways to forge professional relationships. Doing so will expose you to more opportunities than conducting an Internet search or posting on a job board.

While the pandemic has rendered some traditional networking practices temporarily obsolete, you can still network through digital conferences and lectures. At the very least, attending or participating in such events can expand your knowledge and give you a better understanding of the industry at large. You don’t need to wait until you’re ready to look for jobs, either! Keep an eye out for any relevant events while pursuing your data science education.

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Create a Portfolio

Another way to boost your potential of securing an entry-level data scientist role is to compose a portfolio of your work. This tactic will be especially beneficial for those who opt for a self-guided education, but it can be productive for anyone looking to enter the industry. A well-stocked portfolio can overcome a newcomer’s lack of job experience and give them a platform for demonstrating their skills.

Your portfolio should spotlight your completed data science projects and demonstrate your technical capabilities. The purpose of this portfolio is, after all, to show that you are qualified for any entry-level data science role.

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What’s the Difference Between Data Science and Data Analytics?

While similar, it is crucial to recognize that data science and data analytics are not interchangeable.

In a sense, data science is an extension of data analytics, building upon the responsibilities of managing and analyzing data to include the construction, development, and maintenance of machine learning models designed to facilitate data analysis and prediction.

It’s not unusual for data scientists to begin their careers as data analysts. After all, many of the skills required for the former role — programming, analysis visualization, etc. — also apply to the latter. Here’s the difference: while data analysts focus on identifying trends and drawing strategic conclusions from large data sets, data scientists use algorithms, predictive models, and prototyping to design and construct new data modeling processes.

Demand for both roles is relatively high, though competition tends to be somewhat higher for data science than data analytics. Companies that want to accomplish niche tasks such as incorporating advanced technology, artificial intelligence, and machine learning into their data models will seek a data scientist over an analyst. However, analysts may find more opportunities for work among companies that have less of a technological focus.

Who Can Become a Data Scientist?

The good news for anyone looking to make a career shift to the data science field is that there are no limitations on who can gain the skills, education, and experience needed to become a data scientist. Your age or current field of study or occupation do not restrict your potential. Whether you have just finished school or are closer to retirement age, you can become an entry-level data scientist with dedication, hard work, and the right attitude, resources, and education.

While it is beneficial to have a background in mathematics or computer science and you will need to put in extra dedication, it is possible to acquire the right knowledge and experience to become a data scientist in an entry-level role without a formal degree. The timeline for securing an entry-level data scientist role may be shorter if you already have some relevant knowledge and training. However, there is no impending deadline for your data science career to begin.

How Much Do Data Scientists Make in Atlanta, GA, Compared to Nationally?

When committing to a career change, especially one that will require additional schooling, you should consider how much you can expect to make in a new role. This process can help you determine the job prospects in your new field; by conducting this research, you can better plan for your future from a financial perspective.

The average salary for data scientists in the United States currently stands at $100,560. Salary prospects can vary greatly and can depend on a number of factors, including background, experience and location. States like California, Illinois, and New York are among those that employ the most data scientists. On average, data scientists make above the national average in these states.

In Georgia, data scientists can expect to earn less, on average, than the national average, with a mean salary of $81,520. That being said, data scientists in the urban hub of Atlanta can expect to make more than the state average — the mean salary for data scientists in the city tops $90,040. Even though the salary prospects are generally lower in Atlanta, GA, the industry is expected to continue growing rapidly, thereby increasing the potential for a more competitive job market and higher wages.

A graph that shows how much data scientists make in Atlanta, GA, compared to the national average.

As we navigate the ongoing pandemic, the future of the data science industry is still coalescing. The trend of remote work is likely to continue and will almost certainly impact wage disparities across state boundaries.

With data scientists able to work from home, their geographic location will have a less significant impact on their employment opportunities. Faced as they now are with a larger pool of qualified candidates and an influx of competitors across the nation, companies may be compelled to offer competitive salaries to prospective data scientists to secure top talent and attract the right people.

Over time, as the remote work trend continues, we can expect the data scientist salary deviations to steadily become more balanced across the nation. Because of this knowledge, aspiring data scientists can work toward their goals without continually worrying about needing to move for better employment opportunities or higher wages.

Throughout the pandemic, the data science industry has remained strong, demonstrating its durability, demand, and flexibility.


If you are looking to make a significant career change this year, becoming a data scientist may be a worthwhile option. It is possible to make this transition over a few months if you have preliminary knowledge or experience and are dedicated and disciplined enough to hold yourself accountable.

The job prospects, salary range, and potential for initiating meaningful changes within this role are undeniably appealing, and by taking the aforementioned advice into account, you can increase your chances of succeeding in your pursuit of an entry-level data scientist role in 2021. If you want to kickstart your education, check out Georgia Tech Data Science and Analytics Boot Camp!

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