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.
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Steps to become a data scientist
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!
Step 1: Pursue an undergraduate or graduate degree in data science or a closely related field.
There are no standardized education requirements to become a data scientist. However, professionals in this field typically need a bachelor’s degree in data science, mathematics, statistics, or a computer-related field, according to the U.S. Bureau of Labor Statistics (BLS). Some employers and positions could require a master’s or doctoral degree, too.
A degree helps candidates exhibit both broad and in-depth understanding of the subject material. Structured programs from a university can also provide internship and networking opportunities that help boost a résumé. However, a data science degree is not the only way to gain necessary skills. Those who have already earned a bachelor’s degree in an unrelated field might find it valuable to focus on developing data science skills through continued education, like online short courses or boot camps. These alternative options can help candidates pick up technical skills quickly, and at less cost than a traditional degree program.
Step 2.: Advance your specialized skills.
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.
Step 3: Build an online portfolio of relevant work.
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.
Step 4: Review additional data scientist certifications and post-graduate learning options.
Here are a few examples of certifications that focus on data science 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 5: Prepare for technical interviews.
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.
Step 6: Apply!
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.
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The University of North Carolina at Chapel Hill
Master of Applied Data Science
UNC-Chapel Hill’s online Master of Applied Data Science program gives you a holistic understanding of the data life cycle, preparing you to effectively — and ethically — collect, process, manage and analyze data. Learn to translate your insights into a clear narrative that can be used to drive action. Math and programming skills required.
- Complete in as few as 16 months full time.
- No GRE scores required.
Lancaster University
MSc Data Science
Earn your online MSc Data Science from a globally-recognised university for data science and artificial intelligence. Master in-demand skills that employers value most – AI, Natural Language Processing (NLP), Python, and technologies such as Hadoop, Spark and PyTorch. Whether you’re upskilling existing computer science skills or pivoting into a new career, you’ll emerge ready to transform raw data into actionable strategies that drive growth in today’s data-driven economy.
- 15 hours per week total
- Complete your degree in 28 months, part time
- Learn in a holistic curriculum: Academics from the Data Science Institute, School of Computing and Communications, School of Mathematical Sciences, and the Security Lancaster institute collaborate to deliver coursework that reflects the interconnected nature of data science.
Hawaii Pacific University
Master of Science in Data Science
The online MS in Data Science program from Hawai‘i Pacific University prepares students to interpret both conventional and unconventional data, strengthen creative problem-solving skills, and apply cutting-edge data analysis expertise to nearly every industry.
- 12 months to complete full time and 24 months part time
- Standardized test scores not required (No GRE/GMAT)
- Students learn key skills such as data analysis, data mining, data visualization, programming, machine learning, and more
University of Birmingham
Master of Science Applications of Data Science
With University of Birmingham’s online MSc Applications of Data Science, you will learn the essential programming and data management skills necessary for a successful career in data science. You will study topics online with academic experts, from artificial intelligence and statistical modeling to ethical data collection and legal frameworks.
- Complete the programme in three years, part time.
- No application fees or standardised test scores required.
- Built around the United Nations’ sustainability goals, the programme is designed to equip students with the data science skills to analyse, provide critical insight, and communicate conclusions to drive action.
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Learn about 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 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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University of London
Online BSc Data Science and Business Analytics
The online BSc Data Science and Business Analytics from the University of London, with academic direction from LSE, enables students to build essential technical and critical thinking skills and prepare for careers in data science, analytics and other growing fields – while they work, without relocating.
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Data science job outlook and salary
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
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.
FAQ
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.
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.
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.
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.
This career combines programming and math with problem-solving and creative decision-making, which can make it a challenging but rewarding experience. Also, data scientists employ a variety of technical tools that require study and practice to be able to use. Whether or not an individual finds data science to be difficult will depend on variables like their aptitude for specific types of work or the time they have already spent skill building.
Last updated August 2024.