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.

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IBM Data Science

Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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Data Science, Generative AI, Predictive Modelling, Data Analysis, Data Pipelines, Scikit Learn (Machine Learning Library), Data Manipulation, Predictive Analytics, Regression Analysis, Machine Learning Methods, Model Selection, NumPy, Data Import/Export, Data Cleansing, Exploratory Data Analysis, Pandas (Python Package), Predictive Modeling, Feature Engineering, Statistical Modeling, Data Transformation, Data Visualization, Statistical Analysis, Data Wrangling, Python Programming, Web Scraping, Computer Programming, Data Processing, Programming Principles, Numpy, Data Collection, Pandas, Scripting, Jupyter, Automation, Object Oriented Programming (OOP), Data Structures, Application Programming Interface (API), Scatter Plots, Box Plots, Plotly, Heat Maps, Histogram, Dashboards and Charts, Seaborn, Matplotlib, Geospatial Information and Technology, Interactive Data Visualization, dash, Dashboard, Data Visualization Software, Transaction Processing, Databases, Cloud Databases, Query Languages, Relational Databases, SQL, Database Design, Database Management, Relational Database Management System (RDBMS), Jupyter notebooks, Stored Procedure, Supervised Learning, Classification And Regression Tree (CART), SciPy and scikit-learn, classification, Machine Learning, Unsupervised Learning, Dimensionality Reduction, regression, Random Forest Algorithm, Statistical Machine Learning, Applied Machine Learning, Clustering, Machine Learning Algorithms, Data Mining, Data Storytelling, Business Analysis, Decision Tree Learning, CRISP-DM, Data Quality, Data Modeling, Peer Review, User Feedback, Methodology, Github, Data-Driven Decision-Making, Jupyter Notebook, Data Presentation, K-Means Clustering, Data Science Methodology, Git (Version Control System), Rstudio, Cloud Computing, Statistical Programming, Big Data, R Programming, Deep Learning, Business Logic, Digital Transformation, Artificial Intelligence, Data Ethics, Data Synthesis, Quering Databases, Data Generation, Interviewing Skills, Professional Networking, Resume Building, Business Writing, Problem Solving, LinkedIn, Career Development, Presentations, Professional Development, Job Preparation, Recruitment, Company, Product, and Service Knowledge, Portfolio Management, Communication

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 hard, refers to studying data. You will use computer systems and algorithms to work with and understand data. 

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IBM Data Science

Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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Data Science, Generative AI, Predictive Modelling, Data Analysis, Data Pipelines, Scikit Learn (Machine Learning Library), Data Manipulation, Predictive Analytics, Regression Analysis, Machine Learning Methods, Model Selection, NumPy, Data Import/Export, Data Cleansing, Exploratory Data Analysis, Pandas (Python Package), Predictive Modeling, Feature Engineering, Statistical Modeling, Data Transformation, Data Visualization, Statistical Analysis, Data Wrangling, Python Programming, Web Scraping, Computer Programming, Data Processing, Programming Principles, Numpy, Data Collection, Pandas, Scripting, Jupyter, Automation, Object Oriented Programming (OOP), Data Structures, Application Programming Interface (API), Scatter Plots, Box Plots, Plotly, Heat Maps, Histogram, Dashboards and Charts, Seaborn, Matplotlib, Geospatial Information and Technology, Interactive Data Visualization, dash, Dashboard, Data Visualization Software, Transaction Processing, Databases, Cloud Databases, Query Languages, Relational Databases, SQL, Database Design, Database Management, Relational Database Management System (RDBMS), Jupyter notebooks, Stored Procedure, Supervised Learning, Classification And Regression Tree (CART), SciPy and scikit-learn, classification, Machine Learning, Unsupervised Learning, Dimensionality Reduction, regression, Random Forest Algorithm, Statistical Machine Learning, Applied Machine Learning, Clustering, Machine Learning Algorithms, Data Mining, Data Storytelling, Business Analysis, Decision Tree Learning, CRISP-DM, Data Quality, Data Modeling, Peer Review, User Feedback, Methodology, Github, Data-Driven Decision-Making, Jupyter Notebook, Data Presentation, K-Means Clustering, Data Science Methodology, Git (Version Control System), Rstudio, Cloud Computing, Statistical Programming, Big Data, R Programming, Deep Learning, Business Logic, Digital Transformation, Artificial Intelligence, Data Ethics, Data Synthesis, Quering Databases, Data Generation, Interviewing Skills, Professional Networking, Resume Building, Business Writing, Problem Solving, LinkedIn, Career Development, Presentations, Professional Development, Job Preparation, Recruitment, Company, Product, and Service Knowledge, Portfolio Management, Communication

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 on 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

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Deep Learning

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Algorithms, Artificial Neural Network, Transformers, Recurrent Neural Network, Computer Programming, Mathematics, Human Learning, Convolutional Neural Network, Calculus, Machine Learning, Organizational Development, Artificial Neural Networks, Deep Learning, Tensorflow, Network Model, Regression, Computer Vision, Mathematical Theory & Analysis, Applied Machine Learning, Strategy, Machine Learning Algorithms, Linear Algebra, Python Programming, Neural Network Architecture, Backpropagation, Object Detection and Segmentation, Facial Recognition System, Mathematical Optimization, hyperparameter tuning, Multi-Task Learning, Decision-Making, Inductive Transfer, Long Short Term Memory (LSTM), Natural Language Processing, Gated Recurrent Unit (GRU), Attention Models

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 36 percent from 2023 to 2033, representing 20,800 new jobs annually [1]. The World Economic Forum reported in their Future of Jobs 2023 report that 59.5 percent of surveyed organizations say that AI and Big Data are increasingly important skills. The following data science careers are expected to see at least a 25 percent increase in jobs created between 2023 and 2027 [2]:  

• AI and machine learning specialists

• Business intelligence analysts

• Information security analysts

• Data analysts and scientists

• Big data specialists

Read more: Data Scientist Salary Guide: What to Expect

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.

Data scientist

Data scientists build models using programming languages such as Python. Then, you will 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.

• Average US salary: $117,634 [3]

• Skills needed: Statistics, mathematics, machine and 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 can help 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, they 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.

• Average US salary: $85,692 [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

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Google Data Analytics

Get on the fast track to a career in Data Analytics. In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.

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Spreadsheet Software, Data Management, Data Analysis, Business Communication, General Statistics, Business Analysis, Data Visualization, SQL, Data Cleansing, Developing a portfolio, Creating case studies, Metadata, Data Ethics, Spreadsheet, Data Collection, Data Calculations, Data Aggregation, Rstudio, R Markdown, R Programming, Presentation, Tableau Software, Sample Size Determination, Data Integrity, Decision-Making, Questioning, Problem Solving

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. 

• Average US salary: $140,912 [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.

• Average US salary: $106,322 [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.

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IBM Data Engineering

Prepare for a career as a Data Engineer. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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Generative AI, Database Security, Database Servers, Database (DBMS), Relational Database, database administration, Star Schemas, Data Lakes, Snowflake Schemas, Cubes, Data Warehousing, Data Marts, Rollups, Cassandra, Cloud Database, Cloudant, Mongodb, NoSQL, Machine Learning Pipelines, Data Engineer, Apache Spark, SparkML, Machine Learning, SparkSQL, Apache Hadoop, Big Data, Data Generation, Querying Databases, Convolutional Neural Networks, Information Engineering, Apache Kafka, Extract Transform and Load (ETL), Data Pipelines, Apache Airflow, Pandas, Data Analysis, Data Science, Numpy, Python Programming, Google Looker Studio, IBM Cognos Analytics, Data Visualization, Dashboards, Business Intelligence, Relational Database Management System (RDBMS), Postgresql, Database (DB) Design, MySQL, Database Architecture, Shell Script, Linux, Linux Commands, Bash (Unix Shell), Leadership and Management, Databases, Data Management, Relational Databases, Data Visualization Software, SQL, Web Scraping, Network Security, Cloud Databases, Jupyter notebooks

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 replicating 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. 

• Average US salary: $122,439 [7]

• Skills needed: Knowledge of tools such as Spark, Hadoop, R, Apache Kafka, Tensorflow, Google Cloud Machine Learning Engine, 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.

• Average US salary: $93,585 [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

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Google Business Intelligence

Learn in-demand skills like data modeling, data visualization, and dashboarding and reporting in less than 2 months.

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Sheets, Data Analysis, Business Process, Data Modeling, Dashboarding and Reporting, Business Analysis, Data Visualization, Bigquery, Tableau Software, Extraction, Transformation And Loading (ETL), Business Intelligence, SQL, Presenting Data Insights, Effective Communication, Database Optimization, Data Management, Data transformation, Google Dataflow/Google BigQuery, Cross-Functional Team Dynamics, Stakeholder Management, Sharing Insights With Stakeholders, Asking Effective Questions, Business Processes and Requirements

How to get into 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 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 data science with Coursera.

Pursuing a data science degree or credential can help you find a job in many different areas of the field. Boost your career in data science by enrolling in IBM’s Data Science Professional Certificate program. You can learn how to analyze data and communicate results to inform data-driven decisions in 11 months or less, all at your own pace.

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IBM Data Science

Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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Skills you'll build:

Data Science, Generative AI, Predictive Modelling, Data Analysis, Data Pipelines, Scikit Learn (Machine Learning Library), Data Manipulation, Predictive Analytics, Regression Analysis, Machine Learning Methods, Model Selection, NumPy, Data Import/Export, Data Cleansing, Exploratory Data Analysis, Pandas (Python Package), Predictive Modeling, Feature Engineering, Statistical Modeling, Data Transformation, Data Visualization, Statistical Analysis, Data Wrangling, Python Programming, Web Scraping, Computer Programming, Data Processing, Programming Principles, Numpy, Data Collection, Pandas, Scripting, Jupyter, Automation, Object Oriented Programming (OOP), Data Structures, Application Programming Interface (API), Scatter Plots, Box Plots, Plotly, Heat Maps, Histogram, Dashboards and Charts, Seaborn, Matplotlib, Geospatial Information and Technology, Interactive Data Visualization, dash, Dashboard, Data Visualization Software, Transaction Processing, Databases, Cloud Databases, Query Languages, Relational Databases, SQL, Database Design, Database Management, Relational Database Management System (RDBMS), Jupyter notebooks, Stored Procedure, Supervised Learning, Classification And Regression Tree (CART), SciPy and scikit-learn, classification, Machine Learning, Unsupervised Learning, Dimensionality Reduction, regression, Random Forest Algorithm, Statistical Machine Learning, Applied Machine Learning, Clustering, Machine Learning Algorithms, Data Mining, Data Storytelling, Business Analysis, Decision Tree Learning, CRISP-DM, Data Quality, Data Modeling, Peer Review, User Feedback, Methodology, Github, Data-Driven Decision-Making, Jupyter Notebook, Data Presentation, K-Means Clustering, Data Science Methodology, Git (Version Control System), Rstudio, Cloud Computing, Statistical Programming, Big Data, R Programming, Deep Learning, Business Logic, Digital Transformation, Artificial Intelligence, Data Ethics, Data Synthesis, Quering Databases, Data Generation, Interviewing Skills, Professional Networking, Resume Building, Business Writing, Problem Solving, LinkedIn, Career Development, Presentations, Professional Development, Job Preparation, Recruitment, Company, Product, and Service Knowledge, Portfolio Management, Communication