Your Guide to a Data Science Career (+ How to Get Started)

Data science continues to rise as one of the most in-demand career paths in technology today. Beyond data analysis, mining, and programming, data scientists combine code with statistics to transform data. These insights can help businesses derive a return on investment (ROI) or organizations measure their social impact.

The data science field is interdisciplinary and integral to society’s basic functions, such as restocking grocery stores, tracking political campaigns, and keeping medical records. Participating in this growing field can be a fascinating and fulfilling career.

You can find many career opportunities within data science. Explore what data science is, the skills required, job types, and how to get there.

If you’re ready to start preparing for a data science role, enroll in the IBM Data Science Professional Certificate. You’ll have the opportunity to learn how to clean and visualize data, as well as tools and languages used in data science, such as Python and SQL, in as little as four months. Upon completion, you’ll have earned a career certificate to add to your resume. 

What is data science? Definition, skills, and job outlook

Data science grew out of statistics and data mining. It sits at the intersection of software development, machine learning, research, and data science. In the academic world, it straddles the categories of computer science, business, and statistics. Data professionals create algorithms to translate data patterns into research that informs government agencies, companies, and other organizations.

Data science exists because information technology is evolving rapidly. Businesses, governments, and other organizations need to make sense of all the data they collect.

Data science vs. computer science 

Data science and computer science both deal with computers and algorithms, but the two fields are different. Computer science refers to the study of computer mechanisms, including hardware and software, to understand and advance computation. Data science, on the other hand, refers to studying data. You will use computer systems and algorithms to work with and understand data.

Data science skills

In a field like data science, a number of technical skills will be helpful to have before diving in, such as:

• Deep knowledge and familiarity with statistical analysis

• Machine learning

• Deep learning

• Data visualization

• Mathematics

• Programming

• Ability to manage unstructured data

• Familiarity with SAS, Hadoop, Spark, Python, R, and other data analysis tools

• Big data processes, systems, and networks

• Software engineering

• Statistics

A career in data science requires more than just technical knowledge. You’ll work in teams with other engineers, developers, coders, analysts, and business managers. These workplace skills can help take you further:

• Communication skills

• Storytelling

• Critical thinking and logic

• Business acumen

• Curiosity

• Adaptability and flexibility

• Problem-solving

• Teamwork

Data science job outlook

The future is bright for aspiring data science professionals. The US Bureau of Labor Statistics predicts data scientist jobs will grow 34 percent from 2024 to 2034, representing 23,400 new jobs annually [1]. 

Why is data science a growing career field?

According to the World Economic Forum’s Future of Jobs Report 2025, there has been an 87 percent net increase in the number of employers who consider AI and big data to be increasingly important skills, with AI and big data ranking as the fastest-growing skills for 2025 to 2030 [2]. Additionally, over 90 percent of respondents believe AI and big data skills will increase in demand across the following industries between 2025 and 2030 [2]: 

• Automotive and aerospace

• Telecommunications

• Professional services

• Information and technology services

• Insurance and pensions management

• Financial services

• Supply chain and transportation

• Medical and health care services

• Energy technology

• Government and public sector

Read more: Data Scientist Salary: Your Pay Guide

Data science career path: What can you do with a data science degree?

You can choose from plenty of data science jobs. All of them are integral to making key business decisions. Often, several of the job types below will work together on the same team.

*All salary information represents the median total pay from Glassdoor as of September 2025. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

Data scientist

Data scientists build models using programming languages such as Python, then transform these models into applications. Often working as part of a team, for example, with a business analyst, a data engineer, and a data (or IT) architect, you will help solve complex problems by analyzing data and making predictions. This role is typically considered an advanced version of a data analyst.

• Median total salary in the US: $157,000 [3]

• Skills needed: Statistics, mathematics, machine learning, deep learning, programming skills, data analysis, big data processes, and tools like Hadoop, SQL, and more

• Education: Bachelor’s degree in a related field, although increasingly, data science boot camps, master’s programs, and Professional Certificates are helping career switchers reach their goals 

Data analyst

Unlike data scientists, data analysts use structured data to solve business problems. Using tools such as SQL, Python, and R, statistical analysis, and data visualization, you acquire, clean, and reorganize data for analysis to spot trends that can be turned into business insights. You will bridge the gap between data scientists and business analysts.

• Median total salary in the US: $92,000 [4]

• Skills needed: Programming languages (SQL, Python, R, SAS), statistics and math, data visualization

• Education: Bachelor’s degree in mathematics, computer science, finance, statistics, or a related field

Data architect

Data architects create the blueprints for data management systems, designing plans to integrate and maintain all types of data sources. You will oversee the underlying processes and infrastructure. Your main goal is to enable employees to gain access to information when they need it. 

• Median total salary in the US: $174,000 [5]

• Skills needed: Coding languages such as Python and Java, data mining and management, machine learning, SQL, and data modeling

• Education: A bachelor’s degree in data, computer science, or a related field; if you are switching careers, a boot camp or Professional Certificate can help develop your skills in data management

Data engineer

Data engineers prepare and manage large amounts of data. In this role, you will also develop and optimize data pipelines and infrastructure, getting the data ready for data scientists and business analysts to work with. Data engineers make the data accessible so businesses can optimize their performance.

• Median total salary in the US: $131,000 [6]

• Skills needed: Programming languages such as Java, understanding of NoSQL databases (MongoDB), and frameworks like Apache Hadoop

• Education: A bachelor’s degree in math, science, or a business-related field is helpful. Professional Certificates and boot camps are also options for improving skills.

Machine learning engineer

This role is not entry-level but one you can build toward as a data scientist or engineer. Machine learning uses algorithms that replicate how humans learn and act to interpret data and build accuracy over time. As part of a data science team, machine learning engineers research, build, and design artificial intelligence that facilitates machine learning. You will also serve as a liaison between data scientists, data architects, and more. 

• Median total salary in the US: $157,000 [7]

• Skills needed: Knowledge of tools such as Spark, Hadoop, R, Apache Kafka, TensorFlow, Vertex AI, and more; an understanding of data structures and modeling, quantitative analysis, and computer science basics is also helpful 

• Education: Often, a master’s degree or even a PhD in computer science or related fields is expected. Gain an introduction to this field by enrolling in a popular course on Coursera, Supervised Machine Learning: Regression and Classification.

Business analyst

As a business analyst, you’ll use data to form business insights and make recommendations for companies and organizations to improve their systems and processes. Business analysts identify issues in any part of the organization, including staff development and organizational structures, so businesses can increase efficiency and cut costs.

• Median total salary in the US: $105,000 [8]

• Skills needed: Using SQL and Excel, data visualization, financial modeling, data and financial analysis, business acumen

• Education: Bachelor’s degree in economics, finance, computer science, statistics, business, or a related field

How to get into a career in data science

With so many exciting options in data science, you may be wondering where to begin. Whether you are just starting your career or switching from another one, you can take steps to build toward your future in big data or machine learning.

Education: What should I learn?

Earning a degree or certificate can be a great entry point to any data science role.

Bachelor’s degree: For many, a bachelor’s degree in data science, business, economics, statistics, math, information technology, or a related field can help you gain leverage as an applicant. These programs teach you how to analyze data and use numbers, systems, and tools to solve problems. 

But if your bachelor’s degree is in the arts or humanities, don’t fret. Your ability to think critically and creatively is useful in a data science career. You'll find several options if you don’t have a degree at all.

Online courses and Professional Certificates: Whether or not you have earned a bachelor’s degree, an online course or Professional Certificate can be helpful when applying for data science-related jobs.

You can list these courses on your resume or LinkedIn profile for additional credibility. Typically, these courses take a few months to complete (on a part-time basis) and will set you up for at least an entry-level position.

“It's really about the necessary skills, and being able to demonstrate that you can do the work. That's what I achieved by completing this program and earning my credential.”

— Emma S., on taking the IBM Data Science Professional Certificate

Boot camps: If you are willing to spend a few weeks or months pursuing a boot camp, you have plenty of options to pivot and gain the necessary skills for a data science career. Some boot camps are in person over a few weeks or months with a cohort, while others are completed online or at your own pace. The benefits of an in-person boot camp are the community and network you’ll have access to upon completion. 

Some popular options include:

• General Assembly offers an online data science course, an online data science boot camp, and a data science immersive boot camp in New York and other cities. The community-driven network model could help you land a job more quickly.

• Flatiron School is a similar model that also offers full- and part-time data science boot camps online and in New York City.

• Brainstation offers full- and part-time data science boot camps online or in one of its cities (NYC, Toronto, Miami, London, or Vancouver).

• Clarusway has boot camps for data science, data analytics, and machine learning. 

Experience: How do I get a data science job?

Once you’ve completed a degree, course, or certificate and gained the necessary skills, you’ll want to get some work experience.

Entry-level job or internship: To land your first job or internship, you’ll want to rely on applying to jobs that specifically cater to those starting in the data science field. That way, you can feel supported as you prove your worth, develop your skills, and advance in your career.

Some job seekers report applying for hundreds of jobs before obtaining an interview. But don’t be discouraged because data science roles are also in demand. Your hard work will pay off.

Interviews: Once you’ve secured an interview, practice communicating with a non-technical friend about your process. Pretend your interviewer has no idea about your project, so you can talk through your decisions about which tools you choose and why you coded an algorithm in a certain way. You’ll want to prove that you are familiar with the languages and systems you’ll use on the job.

Explore our free resources for data science careers

Looking for fresh insights? Stay ahead of the curve with the latest trends and career advice by joining our LinkedIn newsletter, Career Chat. Or, keep exploring career paths and skills with our data science resources:

• Take the Quiz: Data Science Career Quiz: Is It Right for You?

• Watch on YouTube: 7 Skills You Need to Become a Data Scientist in 2025, or Data Science for Beginners: Your 3-Minute Crash Course

• Hear from fellow learners: Meet the Learner in Turkey Who Wants to Advance in Data Science

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