What Is a Data Scientist and How to Become One?

Data scientists are analytical experts who extract meaning from and interpret data to solve complex problems. They use industry knowledge, contextual understanding and skepticism of existing assumptions to uncover solutions to business challenges.

A data scientist’s role combines computer science, statistics and mathematics to collect and organize data from many different data sources, translate results into actionable plans and communicate their findings to their organizations. Successful data scientists must be effective communicators, leaders, team members and high-level analytical thinkers.

Data scientists work in various industries and environments, including tech startups, government agencies, healthcare, manufacturing and research institutions. In today’s data and technology-driven economy, there are several job prospects for data scientists, and their earning potential often reflects this trend.


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Data Analysis for Management

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Steps to Become a Data Scientist in 2023

Here are six common steps to consider if you’re interested in pursuing a career in data science:

Step 1: Pursue an undergraduate degree in data science or a closely related field;

Step 2: Consider a specialization;

Step 3: Get your first entry-level job as a data scientist;

Step 4: Advance your skills with a data science boot camp (optional);

Step 5: Review additional data scientist certifications and post-graduate learning (optional);

Step 6: Earn a master’s degree in data science;

Step 1. Pursue an undergraduate degree in data science or a closely related field

You will generally need at least a bachelor’s degree in data science or a computer-related field to get your foot in the door as an entry-level data scientist. However, some data science careers require a master’s or doctoral degree. Degrees add structure, internships, networking and recognized academic qualifications to your résumé. However, if you’ve received a bachelor’s degree in a different field, you may need to focus on developing skills required for the job through continued education, like online short courses or boot camps.

Step 2. Consider a specialization

Data scientists may specialize in a particular industry or develop strong skills in areas such as artificial intelligence, machine learning, research or database management. Specialization may be a good way to increase your earning potential and do work that is meaningful to you. 

Step 3. Get your first entry-level job as a data scientist

Once you’ve acquired the necessary skills and specialized knowledge, you can begin the search for your first data science role. Creating an online portfolio is a valuable way to display a few projects and showcase your accomplishments to potential employers. You may want to consider a company with room for growth since your first data science job may not have the title data scientist but could be more of an analytical role. You’ll likely learn how to work within a team and master best practices that can prepare you for more senior positions.

Step 4. Advance your skills with a data science boot camp (optional)

Data science boot camps are short-term, immersive educational programs that teach critical data science skills and programming languages such as Python, R, and SQL. Many boot camps are online, some may take a couple of weeks to complete, while others may take up to a couple of months. Boot camps can help you expand your network. As a student in a boot camp, you can also take advantage of dedicated career services offered through the program to help with job placement after completion. Data science boot camps typically cover various topics such as machine learning, natural language processing, data analytics, data visualization and more.

Step 5. Review additional data scientist certifications and post-graduate learning (optional)

Here are a few certifications that focus on and help improve valuable skills:

Certified Analytics Professional (CAP)

  • CAP was created by the Institute for Operations Research and the Management Sciences (INFORMS) and is targeted towards data scientists. During the certification exam, candidates must demonstrate their expertise in the end-to-end analytics process. This includes framing business and analytics problems, data and methodology, model building, deployment and life cycle management.

SAS Certified Predictive Modeler using SAS Enterprise Miner 14

  • This certification is designed for SAS Enterprise Miner users who perform predictive analytics. Candidates must have a deep, practical understanding of the functionalities for predictive modeling available in SAS Enterprise Miner 14.

Step 6. Earn a master’s degree in data science

Academic qualifications could play a crucial role in the selection process for positions in the field. Is a master’s required for data science jobs? It depends on the job, some professionals have a bachelor’s or graduated from a data science boot camp. However, because some employers prefer that candidates have a graduate degree, a master’s could expand your career prospects.


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Master of Science in Applied Data Science

Syracuse University’s online Master of Science in Data Science can be completed in as few as 18 months.

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Master of Applied Data Science

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  • Complete in as few as 16 months full time.
  • No GRE scores required.

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Master of Information and Data Science

Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.

  • Complete in as few as 12 months
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Master of Science in Data Science

Earn your MS in Data Science at SMU, where you can specialize in Machine Learning or Business Analytics, and complete in as few as 20 months.

  • No GRE required.
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Master of Data Science

In today’s data-driven world, there’s an increased need for data scientists in every industry. Accelerate your career in this rapidly growing field by completing a rigorous master’s degree program that’s 100% online, affordable, and flexible to fit your life. With an MSDS from the top-ranked University of Texas at Austin, you’ll gain in-demand skills in data visualization, data mining, data analysis, machine learning, and more.


Learn about 23 schools with master’s in data science programs.

What Does a Data Scientist Do? 

The day-to-day responsibilities of a data scientist can vary. Some of the different tasks that data scientists are responsible for can include the following:

  • Solve business problems through undirected research and framing open-ended industry questions.
  • Extract huge volumes of structured and unstructured data. They query structured data from relational databases using programming languages such as SQL. They gather unstructured data through web scraping, APIs and surveys.
  • Employ sophisticated analytical methods, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling.
  • Clean data to discard irrelevant information and prepare the data for preprocessing and modeling.
  • Perform exploratory data analysis (EDA) to determine how to handle missing data and to look for trends and/or opportunities.
  • Discovering new algorithms to solve problems and build programs to automate repetitive work.
  • Communicate predictions and findings to management and IT departments through effective data visualizations and reports.
  • Recommend cost-effective changes to existing procedures and strategies.

Every company will have a different take on a data scientist job description. Some treat their data scientists as data analysts or combine their duties with that of data engineers, others need top-level analytics experts skilled in intense machine learning and data visualizations.

As data scientists achieve new levels of experience or change jobs, their responsibilities invariably change. For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging. A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.

Characteristics of a Successful Data Scientist Professional

Data scientists don’t need just to understand programming languages, management of databases and how to transpose data into visualizations–they possess an innate curiosity about their environment, viewing it through an analytical perspective. With personality traits and skills similar to that of quality assurance professionals, data scientists may be meticulous as they review large amounts of data and seek out patterns and answers. They demonstrate creativity in creating new algorithms for data crawling and in designing well-organized database warehouses.

Generally, professionals in the data science field must know how to communicate in several different modes, i.e. to their team, stakeholders and clients. There may be a lot of dead ends, wrong turns or bumpy roads, but data scientists rely on their drive, grit and patience to stay afloat with their research.

Examples of Required Skills for a Data Scientist 

Programming: Python, SQL, Scala, Java, R, MATLAB

Machine Learning: Natural Language Processing, Classification, Clustering,
Ensemble methods, Deep Learning

Data Visualization: Tableau, SAS, D3.js, Python, Java, R libraries

Big data platforms: MongoDB, Oracle, Microsoft Azure, Cloudera


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Data Science Job Outlook and Salary

According to the U.S. Bureau of Labor Statistics (BLS), the employment growth of computer information and research scientists, which include data scientists, from 2022 to 2032 is 22%. The demand for experienced data scientists is high, but you must start somewhere. Some data scientists get their foot in the door working as entry-level data analysts, extracting structured data from MySQL databases or CRM systems, developing basic visualizations in Tableau or analyzing A/B test results. If you’d like to move beyond your analytical role, consider the possibilities that a career in data science can offer:

Companies of varying sizes and in different industries–from Google, LinkedIn and Amazon to the humble retail store–seek experts to help them wrestle big data into submission. In certain companies, “new look” data scientists may be responsible for financial planning, ROI assessment, budgets and a host of other duties related to the management of an organization.

A data scientist’s salary depends on years of experience, skill set, education and location. The BLS reports skilled computer research and information scientists, which include data scientists, are in demand. The below salary data from 2022 is sourced from the U.S. Bureau of Labor Statistics.

Data Scientist

Median Data Scientist Salary: $136,620 per year

Lowest 10%: Earned less than $78,190

Highest 10%: Earned more than $232,010

How Data Science Boot Camps May Help You Become a Data Scientist

Tech boot camps are a quick way to gain hands-on experience in data science and become knowledgeable in programming languages such as Python, R and SQL. Data science boot camps are short programs offered in various formats, including part-time, full-time, online or on-campus. Some boot camps may take a couple of weeks to complete, while others may take a couple of months. Boot camps may give you a chance to expand your professional network and get the support you need for your job search through dedicated career services.

During the boot camp, you’ll work on projects and create a portfolio to demonstrate your abilities to potential employers. Data science boot camps typically cover various topics such as machine learning, natural language processing, different types of data analytics, data visualization and more. Some related boot camp programs are:

When researching boot camps, it is important to consider your career goals and what you’d like to get out of the program. Some boot camps are geared toward beginners, while others are better suited for those with some programming or computer science experience. You may also want to consider the background of the instructors teaching the boot camp and the cost. Can you take time off and commit to a full-time immersive experience? Does the boot camp offer scholarships or discounts? Make sure to ask about all of your financing options.

Data Scientist Career FAQ

How do I become a data scientist?

The first step to becoming a data scientist is typically earning a bachelor’s degree in data science or a related field, but there are other ways to learn data science skills, such as a boot camp. You may also consider pursuing a specialization, or certification or earning a master’s degree in data science before getting your first entry-level data scientist job.

What skills are needed to be a data scientist?

Data scientists use a variety of skills depending on the industry they work in and their job responsibilities. Most data scientists are familiar with programming languages such as R and Python, statistical analysis, data visualization, machine learning techniques, data cleaning, research and data warehouses and structures.

How long does it take to be a data scientist?  

The time it takes to become a data scientist depends on your career goals and the amount of money and time you’d like to spend on your education. There are four-year bachelor’s degrees in data science available, as well as three-month boot camps. If you’ve already earned a bachelor’s degree or completed a boot camp, you may want to consider earning a master’s degree, which can take as little as one year to complete.

How can I become a data scientist without experience?

It is possible to become a data scientist without experience, however, the path you take can depend on if your background is in a related field. You may be able to get your foot in the door through an entry-level data scientist position if you have transferable skills, such as programming, machine learning or data visualization. Another path is learning systematic knowledge through a degree or boot camp program, which offers experience with data science and programming languages such as Python, R and SQL.

Last updated July 2023.