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Our Step-by-Step Guide to Becoming a Data Scientist

It was named the “sexiest job of the 21st century” in 2012, it retained its title in 2022, and the future looks bright: the data scientist will continue to be in high demand over the next decade, with those possessing data science skills able to demand astronomical salaries as a result. But how high is the demand, and how high are the salaries? According to US Bureau of Labor Statistics data from 2021, headcount in data science will grow by 36% by 2031, over 7x the rate of the US labor market as a whole. These data scientists can expect an average annual salary of $100,910, more than double the median annual wage for all US workers in 2021 ($45,760).

A hungry job market and high compensation are natural attractors for those looking to jumpstart their careers and optimize their futures, but for many interested in data science, perhaps you included, figuring out how to become a data scientist remains a daunting task. That’s where we come in: with this step-by-step guide, we’re hoping to lay out the path to landing your first data science job and embarking on an impactful and lucrative data science career. We’ll dive deep into what a data scientist does, the skills one needs to be successful, the educational pathways that can help you acquire these skills, and how to optimize your chances of leveraging your education to land a job.

What does a data scientist do?

Data scientists draw on expertise in computer science, information technology, applied mathematics, and machine learning to design and execute methods and techniques for analyzing big data sets to power technology products and derive insights that can support business decision-making. For the latter the typical process usually runs as follows:

A data scientist

  • defines relevant research interests and questions,

  • prepares data for analysis (often this means structuring unstructured data (or "raw data"),

  • selects a method of analysis or designs a new one,

  • analyzes the data,

  • and communicates insights using data visualization tools.

As you’ll see in the next section on how to become a data scientist, to land a job with this kind of day-to-day requires not just a foundation in the expertise and skills needed to carry out these steps, but experience doing so.

How to become a data scientist

Step One: Get educated.

To begin your journey into data science, it’s best to begin building out any relevant skill sets you don’t already possess. Successful data scientists typically draw on the following skills on a daily basis:

Computer science and information technology skills such as coding in a programming language like R and Python and database querying and maintenance using Structured Query Language (SQL)

Advanced applied mathematics skills, including probability, statistics, data analysis, and linear algebra

Data management skills, including data cleansing, data visualization, cloud computing, data engineering, and data mining for big data

Machine learning skills, including supervised learning, unsupervised learning, reinforcement learning, predictive analytics, natural language processing, and deep learning using neural networks trained on big data

Soft skills such as critical thinking, research design, communication, data storytelling, leadership, and teamwork

While at first glance these skills can be overwhelming, rest assured that you needn’t be alone as you acquire them: there’s an abundance of educational options that can help you get going, regardless of your background. We’ll dive into some of the more common options below.

Data Science Bachelor’s Degree Programs


  • High school graduates interested in data science who are willing to spend the time and money on a four-year degree.


  • A bachelor’s program in data science will offer broad training in the skills listed above. Some aspiring data scientists, rather than choosing to major in data science or if a data science major isn’t available, will major in an adjacent field like applied mathematics or computer science and supplement this major with a minor in a complementary field.


  • Bachelor’s degrees have traditionally been considered the surest entry-way into a white collar career, especially in a highly technical field such as data science.

How much?

  • According to, pursuing a bachelor’s degree in-state at a public university costs $102,828 over 4 years, while pursuing a bachelor’s degree at a private university averages $218,004 over 4 years.

What next?

Data Science Bootcamps


  • Individuals with or without a bachelor’s degree looking to land an entry-level data analyst or data science job without investing as much time and money as is required for an undergraduate or master’s degree.


  • A data science bootcamp offers intensive, comprehensive instruction in data science basics with an emphasis on practical application. Bootcamps often culminate in capstone projects that students design themselves.

  • A core component of the bootcamp experience is the suite of career services offered to students to help them get their first offer for a data science job.


  • Especially since many have moved online, bootcamps offer a shorter, less expensive, and more flexible alternative to traditional degrees while still providing a feasible path to a career in data science.

How much?

  • The average bootcamp cost $11,727 in 2020.

  • Many bootcamps allow students to pay in installments, defer payments until they get a job through income-share arrangements, and/or money-back guarantees if you don’t find a job within a certain time-frame. With any of these options it’s important to read the fine print to ensure you retain eligibility.

What next?

  • To learn more about data science bootcamps, check out our guides for in-person and online bootcamps.

Data Science Certificates, Certifications, and Other Short Courses


  • Those interested in moving into or progressing in data science who already possess some relevant skills and/or aren’t ready to commit to a traditional degree program or bootcamp.


  • Data science certificates and other short courses are short-term, often self-paced educational programs offering either comprehensive instruction or targeted instruction for one or several data science skills.

  • Data science certifications require participants to pass an exam to demonstrate a particular ability or skill.


  • Data science certificates and other short courses are great for those who require the flexibility of a more bespoke online course of study due to their background, time constraints, or interests.

  • Data science certifications are great for those who already possess skills and need to demonstrate this to potential employers.

How much?

  • These kinds of programs are generally much less expensive than degrees and bootcamps, ranging from free to several thousand dollars.

What next?

Data Science Apprenticeships


  • Data science apprenticeships are open to individuals from a variety of backgrounds looking to transition into data-centric fields, from those holding GEDs and associate’s degrees with little formal experience to professionals working in a different capacity.


  • Apprenticeships combine instruction in data science basics with practical, on-the-job training.


  • Apprenticeships are a low-risk way to study data science, with the added benefit of gaining work experience while embedded in a real company.

How much?

  • Apprenticeships offering training for free, or even with a modest salary. 

What next?

  • Check out our data science apprenticeships guide guide for the latest opportunities.

Data Science Master’s Degree Programs


  • Bachelor’s degree-holders from almost any background who can demonstrate aptitude or ability in computer science and applied mathematics.


  • Data science master’s programs combine instruction in programming, data management, applied mathematics, and machine learning with opportunities for practical application.


  • Graduate students generally receive more advanced training than undergraduates, and so master’s degree-holders can command higher salaries.

  • Many data science master’s programs are now offered online or part-time, allowing students to continue working while studying — ultimately a substantial cost-savings. 

How much?

  • According to, master’s of science degrees in the US average $61,200, but the true cost of the degree can run from $30,000 to $120,000 once factoring in fees.

What next?

Step Two: Get experienced.

While education is crucial to landing your first data science job, it’s probably not enough: recruiters and hiring managers are increasingly wanting to see significant work experience on candidate resumes, even for entry-level positions. This raises an age-old question: how to get on-the-job experience before you have a job? Here are some ways:


Internships offer a perspective that traditional classroom education can’t match, providing a glimpse into the workings of an actual organization. It's common for students pursuing a bachelor's or master's degree to participate in one or more internships during their summer breaks. Some degree programs have established ties with local businesses, making it easier for students to secure internships. Additionally, companies often advertise internship opportunities online via their career pages or job boards such as LinkedIn and Indeed.

Independent or Collaborative Projects

A noteworthy addition to a data science resume is a collection of personal or team-based projects. A data science portfolio can provide recruiters and hiring managers with a glimpse into your abilities and work ethics. While many host their portfolios on GitHub, it’s also not uncommon for individuals to create personal websites that showcase their innovative data analysis methods, cutting-edge machine learning models, or visually pleasing data visualizations.

Freelancing and Pro Bono Work

Freelancing is also a great way to gain experience, but if you’re new to data science building up a client base can be difficult. Sometimes, it’s best to start with pro bono work through a social-good outfit like DataKind, Catchafire, or Statistics Without Borders. While you’ll likely have to work for free, you can usually showcase any work product in your portfolio and use it to land paying gigs.

Join a Data Science Club or Organization

The majority of colleges and universities have student-run data science clubs that organize events like hackathons. Participating in these clubs and events can be an excellent approach to meet individuals in the industry and expand your knowledge of current trends and practices.

Step Three: Get polished.

Once you've gotten educated and gained experience, the next step is to ensure that you showcase your abilities to potential employers in the most effective way possible. Using an impressive portfolio and resume, you want to tell your professional story persuasively, highlighting your experiences and setting the stage for your future aspirations.

However, it's not just about having one resume. Each job that you apply to requires you to play a particular role in a team, and as such, you need to customize your resume to showcase the aspects of your background that are most applicable to that role. Additionally, you should practice telling your story in a manner that accentuates your strengths, so that when you do get an interview, you'll be fully prepared to stand out from the crowd.

To learn more about getting polished for the job market, check out our data science resume and interview tips.

Step Four: Get busy.

Once you've honed your skills and expertise and have a solid approach to showcasing your abilities to potential employers, the next step is to secure an interview. While many people tend to distribute their resumes far and wide, it's crucial to tailor each application to increase your chances of being selected for an interview. Leveraging your professional network can also help you to generate leads and potentially secure referrals, which can significantly improve your prospects. In some cases, having a connection on the inside can be the key to getting your resume read and landing an interview.

However, getting an interview is just the beginning. You'll likely have to undergo several rounds, speaking with potential colleagues at various levels of seniority. You’ll likely also have to complete and present projects or assignments that demonstrate your skills and suitability for the job.

Step Five: Profit!

Although you may not be successful in landing a position right away, persistence and patience can ultimately lead to success. It's important to keep in mind that your first job as a data scientist is just the beginning. If you're interested in learning more about what to expect from a data science career path, be sure to check out our comprehensive guide.

Alternative Careers

In this guide, we’ve focused on how to become a data scientist:  what skills to build, what educational opportunities are out there, and what goes into landing a job offer. Someone with computer science, statistical analysis, and data management skills doesn’t need to constrain themselves just to data science, however. There are lots of interesting careers out there where you can leverage these skill sets. To close, we’ll go over a few and suggest some ways you can learn more.

Data analyst

Just like data science, data analytics is a quickly growing field with high demand for qualified professionals. While data scientists focus on developing new methods of data analysis, data analysts are more often concerned with implementing already-developed methods to extract insights that can support decision-making, streamline operations, or improve products.

According to, in the US the average data analyst salary is $81,719. You can learn more about data analytics and data analyst careers through the following articles:

Business analyst

The responsibilities of a business analyst overlap substantially with those of a data analyst: both leverage data analytics — and often big data analytics — to drive value for their companies. Generally, however, a business analyst will work in closer support of business decisions, and so will need stronger business acumen.

According to, in the US the average business analyst salary is $80,757. To learn more about business analytics and the work of a business analyst, see the following articles:

Business intelligence analyst

Business intelligence analysts, like business analysts, require a considerable amount of business acumen. But while a business analyst focuses on analytics that drive business actions, a business intelligence analyst focuses more squarely on producing intelligence: dashboards, reports, and related processes and tools.

According to, in the US the average business intelligence analyst salary is $84,803.

Data architect

A data architect specializes in one aspect of data science: designing efficient systems, pipelines, and protocols for data collection, preparation, and storage to optimize a business’ data operations.

According to, in the US the average data architect salary is $124,771.

Data engineer

A data engineer works in the same area of data science as a data architect, but serves a different function. While a data architect designs data systems and pipelines, a data engineer focus on building them

According to, in the US the average data engineer salary is $112,555. To learn more about the work of a data engineer, see our articles on the difference between data science and data engineering and how to become a data engineer.

Machine learning engineer

A machine learning engineer designs, develops, and ships machine learning models, and then upkeeps them once they are out in the world. Machine learning engineers often work in close collaboration with data scientists, and, because they share an understanding of machine learning, there can be movement between these roles as one continues along a career path.

According to, in the US the average machine learning engineer salary is $121,788. You can learn more about machine learning and machine learning careers over at aifwd, the go-to resource on artificial intelligence and machine learning careers and educational pathways.