Written by Coursera Staff • Updated on
Data science jobs are increasing in demand as big data and technology industries grow. Find out which jobs are the hottest and how to prepare for your career.
The data science industry is growing at a rapid pace. Technology, big data, and software are advancing every day and there are plenty of jobs to reflect this demand. According to US News and World Report in 2023, information security analyst, software developer, data scientist, and statistician ranked among the top jobs in terms of pay and demand [1].
With more access to data science courses and certificates, anyone who wants to can get a job in data science in 2023. Here is a guide to the most in-demand jobs in data science.
What is data science?
Data science is the study of data. Within the field of data science, which is both a career and academic field, different roles are responsible for preparing, managing, analyzing, and processing data so that it can be used to help companies solve business problems.Data scientistsconstruct questions based on a specific data set and usedata analyticsandadvanced analyticsto find patterns, create predictive models, and develop insights that guide decision-making.
Read more: What Is Data Science? Definition, Examples, Jobs, and More
6 in-demand data science jobs
Data science jobs are becoming commonplace and necessary for companies across the globe to optimize quality and financial growth. Let’s dive into some of these in-demand roles. All salaries listed below reflect the average total pay in the US as of August 2023 and include base salaries and average additional pay such as bonuses.
1. Data scientist
Average annual salary: $152,279 [4]
Data scientists determine the questions their team should be asking. They figure out how to answer those questions with data, often developing predictive models and algorithms to theorize and forecast outcomes.
Read more: What Is a Data Scientist? Salary, Skills, and How to Become One
Data scientist job outlook
Data scientist jobs are predicted to experience 36 percent growth between 2021 and 2031, according to the US Bureau of Labor Statistics [2]. Operations research analyst (or data analyst) jobs are projected to grow 23 percent, another high-growth job title [3].
Due to the high demand and technical skill set, jobs in data science tend to pay well. Data science is penetrating nearly every industry, including health care, retail, and technology, so there are plenty of options to find your niche. If you're interested in this field, you might consider boosting your skills in statistics, mathematics, programming, coding, and software development.
Read more: Data Scientist Salary Guide: What to Expect in 2023
2. Data analyst
Average annual salary: $80,109 [5]
A data analyst collects, analyzes, evaluates, reviews, organizes, and visualizes data. They organize the data and perform statistical calculations to find trends that can solve problems for a client or for their employer and inform important business decisions.
Read more: What Does a Data Analyst Do? Your 2023 Career Guide
3. Data engineer
Average annual salary: $115,472 [6]
Data engineers build systems that can automatically collect, store, manage, and analyze data sets so that other data scientists and mathematicians can spot trends and patterns for interpretation. The systems make data easy to digest so it can help a company or customer.
Read more: What Is a Data Engineer?: A Guide to This In-Demand Career
4. Data architect
Average annual salary: $155,022 [7]
Data architects create plans for systems to manage and organize data. A data architect maps out a company’s plan for solving a particular issue and then constructs systems that data scientists use to spot trends and patterns.
Read more: What Does a Data Architect Do? A Career Guide
5. Machine learning engineer
Average annual salary: $151,961 [8]
Machine learning engineers design the architecture for artificial intelligence (AI) programs to interact with large data sets. These engineers work with other data scientists and programmers to create AI programs that can detect patterns, filter data, and perform algorithmic calculations. The programs use AI to automate processes that help customers make choices and make their lives easier.
Read more: What Is a Machine Learning Engineer? (+ How to Get Started)
6. Business intelligence engineer
Average annual salary: $132,593 [9]
Business intelligence engineers design, install, maintain, and develop data systems that analyze large chunks of data specifically for financial and business purposes. They create interfaces that allow employees to easily access and understand data. BI engineers also work on other systems, such as databases and dashboards, that users interact with to evaluate data across departments.
How to prepare for a data science career
To get started, take a look at the online courses, bootcamps, workshops, and certifications out there. There is no set path to becoming a data scientist. However, you can find courses and degree programs from leading universities on Coursera and refer to this archive of Coursera's data science articles, Data Science Jobs Guide: Resources for a Career in Tech.
1. Boost your skills.
Whether you are just starting your career in data science or switching from another field, you'll want to develop the necessary skills. Start by researching and deciding on a type of data science you'd like to try out. Read blogs, listen to podcasts, and get a better grasp of the field as a whole.
For many data science jobs, a bachelor's degree in a relevant subject such as IT, computer science, or mathematics is a great start. Some employers like to see a master's degree for senior-level roles. However, if you hold a bachelor's in an unrelated major or don't have a university degree at all, don't fret. There are plenty of online courses and certificates you can take while working another job. Plus, employers these days are much more open to hiring candidates from less conventional backgrounds. If you have the time, you might consider enrolling in a bootcamp. The possibilities are endless.
Read more: 7 Skills Every Data Scientist Should Have
2. Earn a certification.
A great way to start pursuing a career in data science is to get certified in specific skills and systems. Data science certifications can demonstrate your expertise. If you have a specific position in mind, you can usually see a list of the types of systems required in the job description, which can help guide you on what certification is best for your career and interests. Some possible certifications include:
Certified Analytics Professional (CAP)
SAS Certified Big Data Professional
Oracle Business Intelligence (BI)
IBM Data Science Professional Certificate
Read more: Your Guide to Data Science Certifications in 2023
3. Gain work experience.
If you are completing a degree in a data science-related subject, some of your classes will be practical projects requiring the completion of hands-on activities and projects. Use these to build a portfolio that you can present to an employer.
Another way to get some relevant work experience is open-source development. Open-source programs allow users access to the source code of their software, so you can apply your programming knowledge to an open-source program and make changes to improve the software. Applying your skills to applications that people use every day is a great way to showcase your knowledge and understanding of the software.
Hackathons are another excellent way to test your skills and collaborate with a team on a project. These competitions can provide industry experience for someone looking to get into the data science field because they replicate the teamwork, pressure, and type of work that data science professionals encounter on a regular basis.
Read more: 5 Data Analytics Projects for Beginners
Start your data science career today
Coursera is a great place to start exploring a certificate that is right for you, with many different Professional Certificate programs from top companies and universities in the industry. Consider elevating your career in data science with an IBM Data Science Professional Certificate or a Google Data Analytics Professional Certificate.
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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.
