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.

The steps below represent just one pathway towards becoming an expert-level data scientist. However, there are many different degrees, skill-building tools, and advanced learning options available for those interested in the field. One example of how to become a data scientist is:

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

Step 2: Advance your specialized skills.

Step 3: Build an online portfolio of relevant work.

Step 4: Review additional data scientist certifications and post-graduate learning (optional).

Step 5: Prepare for technical interviews.

Step 6: Apply!

You might need a bachelor’s degree in data science or a computer-related field to pursue a position as an entry-level data scientist. However, some data science careers could require a master’s or doctoral degree, too. Degrees add structure, internship opportunities, networking, and recognized academic qualifications to your résumé. However, if you’ve received a bachelor’s degree in a different field, you might instead find it valuable to focus on developing skills required for the job through continued education, like online short courses or boot camps.

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 do work that is meaningful to you and gain skills that set you apart from other candidates.

Creating an online portfolio is a valuable way to display a few projects and showcase your accomplishments to potential employers. Tools like Github and website hosting services make it simple to set up a public page that links to your work.

Here are a few examples of certifications that focus on data science skills:

Certified Analytics Professional (CAP)

SAS Certified Predictive Modeler using SAS Enterprise Miner 14

The interview process for data scientist roles often includes a technical step, when candidates are asked to complete some sort of skills evaluation to measure job-specific knowledge. This could take a few forms, such as a coding exercise, a whiteboarding session, or a take-home assessment. Online study tools like LeetCode and HackerRank offer practice questions and guides to help applicants prepare.

Begin applying to positions that align with your goals. When looking for organizations and companies, consider what matters to you most. For example, are you mission-driven and want to work somewhere that is focused on making a positive impact for a specific community? Perhaps you are driven by stability and work-life balance, and would align better with a company that prioritizes certain benefits and growth opportunities. Don’t be afraid to reach out to current employees via your network to learn more about the culture and application process.

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

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:

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.

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.

Companies of varying sizes and in different industries — from Google, LinkedIn, and Amazon to small local businesses — seek experts to help them wrestle big data into actionable insights.

According to the U.S. Bureau of Labor Statistics (BLS), employment of computer information and research scientists, which includes data scientists, is projected to grow 23% from 2022 to 2032. This demand is considered much higher than the average for all occupations.

A data scientist’s salary depends on a variety of factors, such as years of experience, skill set, education, and location. The below salary data from May 2023 is from the U.S. Bureau of Labor Statistics.

Median annual salary, computer and information research scientists: $145,080

Lowest 10% of earners: Less than $81,450

Highest 10% of earners: More than $233,110

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.

Last updated July 2024.