Can a data analyst become a data scientist?

Yes, a data analyst can transition into a data scientist role. You should get advanced technical skills like machine learning, a deeper understanding of statistics, and know programming languages, such as Python and SQL. Gaining domain expertise, building a strong portfolio of projects, and networking within the data science community are also key steps in the transition.

How long does it take to become a data scientist?

The typical timeline for becoming a data scientist, from education to employment, generally spans 5-8 years. This timeline begins with foundational education, such as a bachelor's degree in a related field like computer science, statistics, or mathematics, which takes about four years to complete. If you want to pursue a master's degree, you should account for two more years of study. Practical experience through internships, entry-level jobs, or projects can span 1-2 years.

Is data science STEM?

Yes, data science is a part of the broader STEM field. It's a multidisciplinary area that embraces mathematical, statistical, computational, and analytical techniques to extract insights and knowledge from data. Your daily responsibilities may have you doing things such as data capture, storage, processing, analysis, and communication to support decision-making in various industries.

Data Science Careers

Updated on July 12, 2024

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Explore the data science profession in this comprehensive guide, covering the roles, skills, and career opportunities in this high-demand field.

Portions of the following article were drafted using an in-house natural language generation platform. The article was then reviewed, fact-checked, and edited by multiple members of our editorial team prior to publishing.

In the era of big data, data science is revolutionizing industries worldwide. Data science is a multidisciplinary domain that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. The professionals who perform these activities are called data scientists.

In this guide, you’ll learn about the roles and responsibilities of data scientists, the qualifications or requirements to become one, and the career paths available in this exciting field.

Why Is Data Science Important?

Data science is critical to helping organizations make informed decisions and predict future trends. It helps businesses understand their customers while also improving their products and services, marketing efforts, and profitability. Key applications of data science include several industries, including manufacturing and logistics, healthcare, conservation, education, and the financial sector.

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What Data Science Jobs Are Out There?

Individuals with a data science degree have many career options available in various sectors not limited to technology.

Despite tech layoffs and the emergence of AI tools, the job market for data scientists remains robust and is expected to grow: According to one study, only 3% of those laid off by major tech companies were data scientists.

The U.S. Bureau of Labor Statistics projects data scientist positions to be among the fastest-growing jobs, with an increase in job openings by 35% from 2022 to 2032. Furthermore, the World Economic Forum estimates that by 2027, the demand for AI and machine learning specialists will increase by 40%, while other big data and database professionals will grow by 30%-35%. With the rise of AI, the demand for data science skills will likely increase.

Here are some of the in-demand data science careers, along with their average salaries and typical job requirements:

Popular Data Science Careers
Job	Description	Average Pay (as of June 2024)
Data Analyst	Transform and manipulate large datasets for analysis	$67,350
Business Intelligence Developer	Design and develop strategies to assist business users in finding information	$87,120
Statistician	Collect, analyze, and interpret data to inform organizational decision-making	$88,240
Data Engineer	Build and maintain data pipelines	$96,690
Data Scientist	Analyze large amounts of complex data to find patterns that benefit an organization	$101,530
Machine Learning Engineer	Create and maintain machine learning systems	$118,540
Applications Architect	Design the architecture of applications, including user interface and infrastructure	$122,960
Data Architect	Ensure data solutions are built for performance and design analytics applications for multiple platforms	$132,230
Enterprise Architect	Align an organization’s strategy with the technology needed to execute its objectives	$148,310

Source: Payscale

Data Analyst vs. Data Scientist: What’s the Difference?

While both data analysts and data scientists work with data, their roles differ in several ways:

Data analysts focus on interpreting existing data to provide actionable insights. They rely on tools such as SQL, Excel, and statistical software, and their work often involves routine analysis, data visualization, and answering business-related questions.

Data scientists delve into predictive modeling and advanced analytics, using programming languages like Python and R, as well as machine learning techniques. They develop new ways to capture, manipulate, and analyze data, working on complex problems that may include creating new algorithms and models.

What Does a Data Scientist Do on a Daily Basis?

A data scientist’s daily tasks revolve around extracting meaningful information from data. They gather and analyze data, identify patterns or trends, and use various tools to develop predictive models.

One of their key responsibilities is communicating findings to stakeholders. Data scientists work with business analysts and data engineers to conduct analyses and present their findings. They also stay updated on industry changes through blogs, conferences, and networking with other professionals in the field.

Where Can I Work As a Data Science Professional?

As a data science professional, you can work in a variety of industries. Healthcare, retail, finance, and the automotive sectors are some of the top industries employing data scientists. They can work on tasks as varied as diagnosing diseases and preventing illness recurrence to understanding customer preferences, detecting fraud, and improving vehicle safety.

How to Advance Your Career in Data Science

Advancing your career in data science involves a combination of continuous learning, skill-building, networking, and strategic career moves. Here are some ways to help boost your data science career:

Certifications or Licensure

Certifications demonstrate that you’ve met all knowledge and competency requirements set by the certifying organization, and they’re often recognized and valued by employers. You can get these certifications by passing an exam or completing a program, which may be available online or at in-person testing sites. Some top certifications include the following:

Continuing Education

Advancing in data science is not just about learning new skills but also about applying them to real-world problems and pushing your boundaries. With data science’s rapid growth, continuing education is critical for data scientists.

This may involve returning to school for a higher degree, attending workshops or conferences, or taking online courses. Many online platforms offer courses in data science topics, and staying on top of the latest research and trends in data science can help you stay competitive in the job market.

Other Ways to Advance Your Career

There are several other ways to advance your career in data science. For instance, you can gain practical experience by working on real-world projects. Participating in data science competitions can also help you apply your skills and gain recognition in the field. Networking is also crucial. Attend industry events, join online forums, and connect with other data scientists to learn about new opportunities and trends.

Another effective way to advance your career is by contributing to open-source projects or publishing your own work. This can help you demonstrate your skills, gain visibility in the data science community, and make valuable connections.

Don’t forget the importance of soft skills. Developing strong communication, problem-solving, and leadership skills can set you apart and open up new opportunities.

Resources for Data Science Majors

Because data science often relies on new and emerging technologies, students and professionals must stay up-to-date with the latest developments. Professional organizations disseminate news and updates in the field, and publications provide readers with in-depth analyses and research.

Students and professionals can also access open courseware to bolster their credentials and learn about new areas of data science.

American Statistical Association: Founded in 1839, the ASA includes industry, government, and academic members from over 90 countries. The association funds policy initiatives and research projects. Members connect through national and international networking events, including the Women in Statistics and Data Science Conference. Additional resources include internships, fellowships, grants, student award competitions, and a job board.
Association of Data Scientists: ADaSci serves more than 1,000 members who connect through local chapter meetings and national conventions. They can also share knowledge and receive guidance through an integrative online forum. The association delivers continuing education courses, job listings, mentoring programs, and internship opportunities. Professionals can also pursue ADaSci’s chartered data scientist credential.
Computing Technology Industry Association: CompTIA helps technology professionals, educators, and students build their professional networks. The association conducts skill development and leadership programs and hosts local chapter meetings and national events. Data science professionals can also pursue certification in areas like cloud essentials, project management, IT fundamentals, and information security.
International Institute of Business Analysis: As a nonprofit professional organization, IIBA maintains global standards for the emerging field of business intelligence and analytics. Members can access career services and a knowledge center that includes business analysis competency models and best practices. They can also take advantage of certification programs in areas like core business data and agile analysis.
Research Data Alliance: Founded in 2013 as a community-driven initiative, RDA promotes social networks and technology services that enable open data sharing and reuse. Members collaborate in special-interest working groups and regional meetings. They benefit from webinars and career guidance. RDA also operates student groups and early career programs in the U.S. and Europe.

Analytics Magazine: Published by the Institute for Operations Research and the Management Sciences, Analytics Magazine features news, interviews, analysis, and blog posts on data science subjects. Major topics include healthcare, artificial intelligence, business analytics, big data, and predictive analytics. Authors also examine ethical issues, such as bias in algorithms.
Data Science Journal: This open-access, peer-reviewed journal focuses on the use and dissemination of research data. The journal includes articles on STEM, the arts, and humanities. The journal examines how to use data in the most efficient, effective, and ethical ways possible across all disciplines.
CHANCE: This magazine explores how to use data and statistics to improve outcomes in education, science, and society. Although the magazine is dedicated to statistics, it emphasizes innovative, entertaining case studies and applications rather than technical topics. By doing so, CHANCE hopes to reach a wider audience.
Data Mining and Knowledge Discovery: A technical journal for data science professionals, this publication explores techniques, methods, algorithms, and applications for data mining. Prior articles have examined topics like online social networks, space exploration, and decision-making.
Journal of AHIMA: The premier publication of the American Health Information Management Association, this journal features articles on data science and informatics within the healthcare sector. The journal is free for AHIMA members.
International Journal of Business Intelligence Research: This peer-reviewed journal aims to improve the role, scope, and impact of business intelligence on decision-making. Topics include data mining for businesses, business intelligence in small and medium enterprises, organizational culture, and knowledge management. The journal features case studies.
IEEE Transactions on Knowledge and Data Engineering: Published by the Institute of Electrical and Electronics Engineers — the leading professional organization in engineering and computer science — this journal explores data science in artificial intelligence, computer science, electrical engineering, and other technological fields. The journal emphasizes database systems.

Frequently Asked Questions About Data Science Careers

Like any job, a data science career has its stressful moments. Data scientists often have tight deadlines, deal with messy and incomplete data, constantly learn new skills, and effectively communicate complex data insights to non-technical stakeholders.

You can avoid these challenges with continuous learning and maintaining good problem-solving, time management, and communication skills. Ultimately, the satisfaction of solving complex problems and making a tangible impact on business decisions can make a career in data science rewarding.

Note: The insights on this page were reviewed by an independent third party compensated for their time by BestColleges. Page last reviewed June 18, 2024.

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