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

Written by Coursera • Updated on

Careers in data science are in-demand. Step into the world of big data and machine learning.

A female data scientist presents her findings to the team.

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 program code and combine it with statistics to transform data. These insights can help businesses derive 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. It can be a fascinating and fulfilling career to participate in this growing field.

There are many career opportunities within data science. Here’s a guide to what data science is, the skills required, job types, and how to get there.

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

Data science grew out of statistics and data mining. This is a new specialty, with the Data Science Journal making its debut only in 2002. It sits at the intersection of software development, machine learning, research, and data science while falling under the categories of computer science, business, and statistics combined. 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 at a rapid pace, and there is a need to make sense of it all. 

Skills required in data science

In a field like data science, there are a number of technical skills that are 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

A career in data science is not limited to technical knowledge. You’ll work on teams with other engineers, developers, coders, analysts, and business managers. These workplace skills will help take you farther:

  • Communication skills

  • Storytelling

  • Critical thinking and logic

  • Business acumen

Data science job outlook

The future is bright for aspiring data science professionals. In 2020, IBM predicted that there would be 2.7 million open jobs across data science and related careers and that there would be a 39 percent growth in employer demand for data scientists and data engineers [1].

For data scientists specifically, the US Bureau of Labor Statistics estimates the employment growth rate to grow by 22 percent by 2030 [2]. It is considered the third-best job in the US (as of March 2022), according to Glassdoor [3]. 

Read more: Data Scientist Salary Guide: What to Expect

Data science job roles

There are plenty of data science jobs to choose from. 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. They 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, they help solve complex problems by analyzing data and making predictions about the future. This role is typically considered an advanced version of a data analyst.

Average US salary: $104,235 [4]

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 bootcamps, master’s programs, and professional certificates can help career switchers reach their goals. According to a Burtch Works study of data scientists and salaries, more than 94 percent of data scientists held a master’s or doctorate degree [5].

Read more: ​​What Is a Data Scientist? Salary, Skills, and How to Become One

Data analyst

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

Average US salary: $62,445  [6]

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

Read more: What Does a Data Analyst Do? A Career Guide

Data architect

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

 

Average US salary: $128,855 [7]

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 bootcamp or professional certificate can help develop your skills in data management.

Read more: What Does a Data Architect Do? A Career Guide

Data engineer

Data engineers prepare and manage large amounts of data. They 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: $100,597 [8]

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 bootcamps are also an option to brush up on skills.

 

Read more: What Is a Data Engineer?: A Guide to This In-Demand Career

Machine learning engineer

This role is not an entry-level position, 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. They also serve as a liaison between data scientists, data architects, and more. 

Average US salary: $106,991  [9]

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 Ph.D in computer science or related fields is expected. Gain an introduction to this field by enrolling in one of Coursera’s most popular courses, Machine Learning.

Read more: What is a Machine Learning Engineer and How Can You Get Started?

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 that businesses can increase efficiency and cut costs.

Average US salary: $77,921 [10]

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

Read more: What Is a Business Analyst? 2022 Career Guide

The path to a data science career

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, here are the steps you can take to build toward your future in big data or machine learning.

Education: What should I learn?

To get started in any data science role, earning a degree or certificate can be a great entry point.

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. From these programs, you’ll learn 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 not lost in a data science career. If you don’t have a degree at all, there are several options for you too.

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 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.

If you are interested in diving into a data science career, you might consider IBM's Data Science Professional Certificate or Stanford University’s Machine Learning Course.

Placeholder

professional certificate

IBM Data Science

Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required.

4.6

(54,827 ratings)

102,492 already enrolled

BEGINNER level

Average time: 11 month(s)

Learn at your own pace

Skills you'll build:

Data Science, Deep Learning, Machine Learning, Big Data, Data Mining, Github, Python Programming, Jupyter notebooks, Rstudio, Methodology, Data Analysis, Pandas, Numpy, Ipython, Cloud Databases, Relational Database Management System (RDBMS), SQL, Predictive Modelling, Data Visualization (DataViz), Model Selection, Data Virtualization, Plotly, Matplotlib, Python Libraries, regression, Hierarchical Clustering, K-Means Clustering, Jupyter Notebook

Bootcamps: If you are willing to spend a few weeks or months pursuing a bootcamp, there are plenty of options to pivot and gain the necessary skills for a data science career. Some bootcamps 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 bootcamp are the community and network you’ll have access to upon completion. 

Some popular options include:

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

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

Skills: What do I need?

You’ll need a combination of technical and workplace skills to succeed in a data science career. Here are some of the most common skills employers look for in a data science role:

Technical skills

  • Statistics

  • Mathematics

  • Data visualization

  • Machine and deep learning

  • Data wrangling

  • Programming

  • Data engineering

  • Cloud computing

  • Business acumen

 Workplace skills

  • Curiosity

  • Communication

  • Storytelling

  • Adaptability and flexibility

  • Critical thinking

  • Problem solving

  • Teamwork

Experience: How do I get a 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 out in the data science field. That way, you can feel supported as you prove your worth, develop your skills, and move up 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 that 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 be using on the job.

Explore data science with Coursera

Boost your career in data science by enrolling in IBM’s Data Science professional certificate program. You’ll learn how to analyze data and communicate results to inform data-driven decisions in 11 months or less, all at your own pace.

Placeholder

professional certificate

IBM Data Science

Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required.

4.6

(54,827 ratings)

102,492 already enrolled

BEGINNER level

Average time: 11 month(s)

Learn at your own pace

Skills you'll build:

Data Science, Deep Learning, Machine Learning, Big Data, Data Mining, Github, Python Programming, Jupyter notebooks, Rstudio, Methodology, Data Analysis, Pandas, Numpy, Ipython, Cloud Databases, Relational Database Management System (RDBMS), SQL, Predictive Modelling, Data Visualization (DataViz), Model Selection, Data Virtualization, Plotly, Matplotlib, Python Libraries, regression, Hierarchical Clustering, K-Means Clustering, Jupyter Notebook

 

Related articles

Article sources 

1. IBM. “The Quant Crunch: How the Demand for Data Science Skills is Disrupting the Job Market, https://www.ibm.com/downloads/cas/3RL3VXGA.” Accessed April 13, 2022.

2. US Bureau of Labor Statistics. “Computer and Information Research Scientists, https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm.” Accessed April 13, 2022.

3. Glassdoor. “50 Best Jobs in America for 2022, https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm.” Accessed April 13, 2022.

4. Glassdoor. “How much does a Data Scientist make?, https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm.” Accessed April 13, 2022.

5. Burtch Works. “The Burtch Works Study Salaries of Data Scientists & Predictive Analytics Professionals, https://www.burtchworks.com/wp-content/uploads/2020/08/Burtch-Works-Study_DS-PAP-2020.pdf.” Accessed April 13, 2022.

6. Glassdoor. “How much does a Data Analyst make?,      https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm”  Accessed April 13, 2022. 

7. Glassdoor. “How much does a Data Architect make?, https://www.glassdoor.com/Salaries/data-architect-salary-SRCH_KO0,14.htm.” Accessed April 13, 2022.

8. Glassdoor. “How much does a Data Engineer make?, https://www.glassdoor.com/Salaries/data-engineer-salary-SRCH_KO0,13.htm.” Accessed April 13, 2022.

9. Glassdoor. “How much does a Machine Learning Engineer make?, https://www.glassdoor.com/Salaries/machine-learning-engineer-salary-SRCH_KO0,25.htm.” Accessed April 13, 2022.

10. Glassdoor. “How much does a Business Analyst make?, https://www.glassdoor.com/Salaries/business-analyst-salary-SRCH_KO0,16.htm.” Accessed April 13, 2022.

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Learn without limits