How to Become a Data Scientist

A subfield of computer science, data science is the study of large quantities of data.

A relatively new and quickly growing field, data science offers excellent career opportunities. Glassdoor ranks data scientist as the third best job in the U.S. for 2022, citing high job satisfaction, top salaries, and abundant job openings.

This page explains how to become a data scientist. We cover experience and education requirements, look at data science responsibilities and job search strategies, and outline the steps to become a data scientist.

What Does a Data Scientist Do?

A data scientist's primary goal is to use data to answer questions, make predictions, and solve problems. Data science professionals collect, clean, and analyze data. They use computer science techniques and tools to create algorithms, find patterns, ask questions, and launch experiments. Data scientists also write reports and deliver presentations based on their findings.

Organizations of all kinds collect increasingly large sets of data and can face difficulty deciphering its meaning or how to use it effectively. Data scientists use their advanced knowledge and skills to help companies make informed business decisions.

A successful career in data science requires excellent problem-solving, analytical, and communication skills. Data scientists need experience with big data analytics, SQL, R, and data mining. The following section explores the most important soft and hard skills for data scientists.

What Education Do Data Scientists Need?

Education requirements for data science professionals vary by position, employer, and industry. Data scientists typically need at least a bachelor's degree in computer science, data science, or a related field. However, many employers in this field prefer a master's degree in data science or a related discipline.

Data analysts and data engineers usually need a bachelor's degree. Becoming a data scientist or computer and information research scientist usually requires a master's.

Some data science professionals hold a mix of education levels. For example, someone might earn a bachelor's in computer science and complete a data science bootcamp. Or, they might complete a bachelor's in an unrelated field and then earn a master's in data science.

In general, career opportunities and salaries increase as people earn higher degree levels. A graduate degree can also help job applicants stand out from candidates with just a bachelor's.

What Does a Data Scientist Do?

Data-based insights have revolutionized many industries while creating new avenues for profit. To realize these benefits, companies must look at the right data sets and ask the right questions. The specialized training that data scientists undergo develops advanced skills in these areas.

Knowing which data to look at and which questions to ask is one of the data science profession's major challenges. Professionals with strong aptitudes for statistical analysis, logical and mathematical thinking, creativity, and technology tend to succeed in the role.

The following section analyzes major job duties in more detail.

Data Scientist Responsibilities

• Identifying Organizational Challenges: Organizations face internal and external challenges in areas like employee productivity and pressure from competitors. Advanced data analysis can often identify patterns that indicate potential solutions. Data science responsibilities also include identifying organizational challenges that targeted statistical analysis can address.
• Assembling Data: First, data scientists identify an organizational issue to explore through data analysis. Next, they collect relevant statistical information. This process involves harvesting data, usually from many sources. Data scientists then check the collected information for completeness and accuracy.
• Analyzing Data: Data scientists sometimes analyze data by hand. However, in most cases they use software-based tools such as algorithms to filter data strategically. Data scientists may take part in developing these algorithms. They also devise the modeling methods used to generate data-based insights.
• Extracting Insights: Data scientists apply algorithms, mathematical models, and filters to large data sets. Then, they analyze the resulting statistics to extract actionable insights.
• Communicating Findings: Capable data scientists often uncover observations worth reporting to organizational decision-makers. They communicate these findings in writing, with charts and graphs, or through a combination of both. This aspect of the job may also involve giving oral presentations or reports.

Requirements to Become a Data Scientist

There is more than one way to enter the data science field. Data scientist requirements differ by employer, industry, and location. Aspiring professionals can meet data scientist job requirements in various ways.

The typical process to become a data scientist includes at least a four-year college degree in computer science, data science, or a related field. Many data scientists also pursue graduate education, professional certifications, and bootcamps.

Most data scientist jobs require some relevant professional experience. Students can gain experience while still in school through internships, capstone projects, and fellowships.

Below, we outline in detail some of the steps to become a data scientist.

Steps to Become a Data Scientist

Several paths can lead to a career as a data scientist. Below, we list the steps to become a data scientist based on different paths.

Bachelor's Degree Path

1. Earn a bachelor's degree. Most data science jobs require at least a four-year bachelor's degree. Consider majoring in data science, computer science, or mathematics. Take classes in computer science, business, and statistics.
2. Complete an internship. Getting internship experience develops career-relevant skills and can lead to job offers.
3. Pursue professional certifications. Earning a professional certification is not required for becoming a data scientist, but it can help you prove your skills to potential employers.
4. Get entry-level professional experience. Apply for jobs like data analyst, data engineer, and market research analyst. Spending several years developing your skills can lead to better job opportunities in the future.

Data Science Bootcamp Path

1. Earn a bachelor's degree in any field.
2. Complete a data science bootcamp. Data science bootcamps provide intensive career training in less time than most traditional college programs. You may need to complete prerequisites before starting a bootcamp.
3. Complete an internship.
4. Pursue professional certifications.

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Master's Degree Path

1. Earn a bachelor's degree in any field.
2. Earn a master's degree in data science. Data science master's programs usually take 1-2 full-time years to complete.

What Is a Data Scientist?

New technologies help organizations easily collect large amounts of data. But, they often do not know what to do with this information. Data scientists use advanced methods to help bring value to data. They collect, organize, visualize, and analyze data to find patterns, make decisions, and solve problems.

Data scientists need strong skills in programming, data visualization, communication, and mathematics. Typical job responsibilities include gathering data, creating algorithms, cleaning and validating data, and drafting reports. Nearly any organization can benefit from the contributions of a trained data scientist. Potential work sectors include healthcare, logistics, and banking and finance.

How Much Experience Do Data Scientists Need?

Data scientist requirements for professional experience depend on the role and workplace. In general, data scientist positions are not entry-level jobs. Successful candidates need some relevant experience.

Before becoming a data scientist, some people start out in related information technology positions. These stepping-stone roles may include data analyst, market research analyst, or data engineer.

Data science degree programs and bootcamps often include internships, fellowships, and capstone projects. These experiences provide hands-on practice that can help graduates land a job offer. Some employers let job applicants substitute education for experience, or vice versa.

Professional Certifications

Professional certifications are not a requirement for becoming a data scientist. However, a certification can help you stand out and show your expertise to potential employers. Getting certified can help you earn a higher entry-level salary and open the door to more career advancement opportunities.

In some cases, a data science certification may persuade an employer to hire someone who does not meet all of a job's stated education or experience requirements.

Key Soft Skills for Data Scientists

• Analytics: Understanding and interpreting data are at the heart of data science, so business and data analytics skills are essential to data scientists. Some employers seek skills in machine learning, a branch of analytics that creates performance prediction systems.
• Business Knowledge: Many companies seek data scientists who can interpret data and use it to inform business strategies for improving efficiency, productivity, and sales. Data scientists can specialize in the data science field of business analytics, in which old performance data guides present and future business moves.
• Communication: Most data scientists must communicate their findings, interpretations, and ideas to employers and colleagues. Consequently, clear writing and public speaking skills make data scientists more effective in collaborating with team members and superiors.
• Problem-Solving: Most companies, governments, and nonprofits turn to data scientists for their ability to solve problems with data-driven insights. These professionals can help organizations clarify which aspects of issues can be solved using data sets.
• Organization: Data scientists need strong organizational skills to address projects involving complex parts and data sets. Professionals who understand how to organize large repositories of raw data, for example, may know how to improve company outcomes by sourcing more representative amounts of data.

Key Hard Skills for Data Scientists

• Big Data Analytics: Big data analytics involves the analysis and application of large data sets to help companies understand consumer trends and market patterns. The term "big data" refers to data sets so complex that normal data processing software could not work with them.
• Data Mining/Data Warehouse: Data mining is the process of looking within a large set of data for previously unrecognized patterns or insights. A data warehouse is a system created specifically for data analytics.
• SAS: SAS is a suite of software products created specifically for data management and analysis for business insights. Use and mastery of SAS are foundational skills for data science professionals, helping them create value for their employers by addressing real-world problems.
• SQL: Short for Structured Query Language, this language works specifically with database management. All prospective data scientists should maintain a firm grasp on the latest trends and advances in SQL.
• R: R, a programming language, applies to statistics and graphics. Many computer science professionals consider R to be foundational knowledge.

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Data Science Degree Programs

This page explains what to expect from different data science degree programs, including admission requirements, typical courses, and opportunities for graduates.

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Online Data Science Bachelor's Programs

This page explores why to consider earning an online data science bachelor's degree. Get an idea of how to apply, the typical time commitment, and program requirements.

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Data Science Bootcamps

Learn how a data science bootcamp can help you pursue a new career. This page covers prerequisites, cost, and program focus.

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Certifications for Computer Science Professionals

This guide explains how certifications can help computer science professionals and includes details on certification requirements.

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Professional Spotlight: Interview with a Data Scientist

Why did you choose to become a data scientist?

Prior to data science, I was a professor. But I (and many of my fellow young Ph.D.s) gradually realized that the academic job market has serious problems that prevent it from absorbing and properly utilizing all of the talented candidates who are getting their doctorates. Data science offers a way for people with strong mathematical and statistical backgrounds to apply their industry knowledge and research acumen to problems in the private sector in a much livelier job market (also for substantially better pay than is offered in academia). I also felt that data science, as a fast-growing, dynamic field, would allow me to expand my skills and insights faster than in academia.

Can you describe your path to a career in data science?

My first problem once I decided to switch careers was how, exactly, to transition. While I was highly educated, I had no specific certifications or qualifications that many jobs were looking for. That is why I chose to enroll in The Data Incubator. The Data Incubator specializes in taking candidates with strong academic backgrounds and helping them to learn how to conduct and communicate data science effectively in the private sector. They also help to match their students with prospective employers, which enabled me to get my first job in data science at Cova Strategies (which later transitioned to a role at NNData as senior data scientist).

What are some high and low points for this career? What challenges might a data scientist face?

While I am likely not in a good position to comment on career highs and lows (I have not been in data science for that long), I can say that the biggest challenge I faced in data science was believing myself to actually be qualified. Even after getting my first data science role, I felt much of the same imposter syndrome that plagues many people, especially those coming from academia.

What type of person does well in this role?

People who have a strong grasp of mathematics and statistics and can learn and apply new techniques rapidly. Data science is a rapidly evolving field; methods change, new techniques develop, and there is always something relevant to discover, understand, and integrate into new or even existing projects. No one can stay informed on every topic, so there will inevitably be times when you have to learn on the fly to use the latest or best techniques to solve a problem.

What advice do you have for students considering a career in data science?

First, as I mentioned earlier, data science is much more an exercise in mathematical and statistical reasoning than anything else, so don't neglect your mathematics! Second, be prepared to be a pioneer. While many people have attempted to solve almost any problem (Stack Overflow is proof of that), few have likely tried to solve the problems you will be facing with the exact intention that you have. Be prepared to combine solutions together, modify code, or apply technologies in ways they may not have been initially intended. That's what makes data science into a "data art," and that's what makes it fun! Third, especially if the student is coming from a graduate program, know your value. If you have (or are about to have) a Ph.D., you probably know something! You are more qualified than you likely give yourself credit for, and should not let yourself forget that. Finally, getting your foot in the door in any industry can be hard. Try and find some certification program, course, or something that signals to companies that you are serious and able to apply your skills to meet their needs. Beyond that, just remember that, like any career, a career in data science is a journey. Be prepared for the unexpected and to find your ideal niche in a company (and the wider industry) where you may not initially expect.

Andrew Graczyk, Ph.D.

Dr. Andrew Graczyk is a graduate of The Data Incubator. He also earned his Ph.D. in economics from the University of North Carolina at Chapel Hill in December 2017. His research specialties in game theoretic modeling, Bayesian statistics, and time series analysis allowed him to synthesize novel models to capture adverse incentives responsible for behavior that other models struggle to explain. Prior to his career in data science, he developed experience working with a wide variety of data and topics from asset bubble formation to housing markets to environmental regulation and agriculture. As a senior data scientist at NNData, Dr. Graczyk applies his multifaceted experience with data and theory to create robust, flexible, and holistic solutions to problems using cutting-edge machine learning and statistical techniques.

Where Do Data Scientists Work?

The U.S. Bureau of Labor Statistics (BLS) tracks salary, industry, and location information for data scientists. As of May 2021, the BLS reports the following as the five top-employing states for data science professionals:

• California
• New York
• Texas
• North Carolina
• Illinois

Job opportunities tend to cluster in urban areas. However, some data scientists work mainly or exclusively online. The position translates well to remote work.

The private sector dominates the data science employment landscape. As of May 2021, the BLS reports the following as the five leading industries for data scientist employment:

• Computer systems design
• Enterprise management
• Technical consulting
• Scientific research services
• Credit mediation

A day in the life of a data scientist may differ depending on the industry of employment and whether a professional works on-site or remotely.

Career Outlook for Data Scientists

Data scientists might worry that emerging artificial intelligence technologies could reduce the need for their expertise. However, the complexities of modern business require human solutions.

The BLS projects data scientist employment to increase by 31.4% from 2020-2030. In addition, LinkedIn's 2020 Emerging Jobs Report states that data scientists are replacing statisticians in some industries to prepare for a more advanced future in tech.

The Job Hunt

Places to look for data scientist positions include professional organizations, job fairs, and networking opportunities at annual conferences. Ask for job leads from mentors, alumni associations, and former supervisors and colleagues. Current students and recent graduates can apply for paid and unpaid internships to gain professional experience.

You can also search for openings on job boards. Below, we highlight five of the top job boards for the data science industry.

• DataJobs: This job site posts openings in data science, data analysis, and data engineering. It matches companies with big data talent.
• Open Data Science Job Portal: Job-seekers can find thousands of data science jobs here at over 300 companies. Candidates can submit their resumes and get matched automatically with relevant positions.
• Ai-jobs.net: Find artificial intelligence, machine learning, and big data jobs around the world.
• Digital Analytics Association: Browse for analytics openings by industry, job type, location, and experience level.
• icrunchdata: This website posts analytics, technology, and data-related jobs worldwide. It also provides industry insights and career advancement information.

How Much Can You Earn as a Data Scientist?

According to the Bureau of Labor Statistics (BLS), the median data scientist salary for 2021 was $108,660. However, salaries vary significantly in the data science career path based on experience, education, and specialization.

For instance, a data scientist with certifications and training in several tools, like Apache Spark or Python, might earn more than one without the same credentials.

Similarly, some industries pay higher salaries. The BLS lists computer and peripheral equipment manufacturing as the top-paying data scientist career sector. Data compiled by Fortune indicates that data scientists earn higher salaries than in previous years, regardless of industry and other factors.

Data Scientist Salary by Experience

The average data scientist salary increases with experience level, according to Payscale data from July 2022. With less than one year of experience, these professionals earn an average of $85,730. After 20 years or more, their salary increases to an average of $134,980.

This shift in salary potential typically occurs because data scientists become more knowledgeable with experience. Employers may have more interest in highly skilled data scientists and offer them higher pay.

Data Scientist Salary by Education

The tables below list the average salaries for data science professionals based on their education level and degree type.

Statistics, informatics, and mathematics degrees provide foundational information for students unsure about pursuing data science or other related fields. In contrast, a computer science degree provides a more direct pathway.

Although Payscale doesn't report salaries for data science degree-holders, a bachelor's or master's data science degree are additional options. Master's degree graduates could see higher average starting salaries than bachelor's degree-holders due to their advanced skills.

However, a bachelor's degree in computer science offers a similar average wage to a non-computer science master's degree because of its relevant content for data scientists.

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Data Scientist Salary by Location

How much does a data scientist make in different locations? BLS data shows that Washington, California, and Delaware are the highest-paying states for data science. Additionally, Payscale lists three California cities — Mountain View, San Francisco, and Santa Clara — as the top-paying cities.

The demand for data scientists can vary significantly nationally. The areas with the highest salaries tend to have strong technology industries that seek skilled professionals. For instance, California's tech sector is the largest in the country, with close to two million jobs.

A data science career salary may also relate to an area's cost of living. California currently has the third-highest cost of living index. Washington, Delaware, New York, and New Jersey also have a higher-than-average cost of living index.

Questions About Data Science Careers

Is data science a good career?

Yes, data science offers promising career paths. It is a fast-growing and relatively new field with higher-than-average salaries. The Bureau of Labor Statistics projects above-average job growth for many data science careers, including operations research analysts and computer and information research scientists.

Do data science jobs pay well?

Yes. Data scientists can earn significantly more than the average worker. The Bureau of Labor Statistics reports that data scientists earned a mean annual salary of $108,660 as of May 2021.

What are the education requirements for a data scientist?

Minimum education requirements for data science professionals usually include a bachelor's degree. Many data scientist positions require or prefer applicants with a master's degree in data science, computer science, or a relevant field.

How can I become a data scientist?

Various paths can prepare students for a data science career. The steps to become a data scientist may include earning a bachelor's degree, gaining work experience, and completing a professional certification. Many data scientists earn a master's degree or attend a bootcamp.