Making a Career Change to Data Science
Even if you’ve spent years in another field, making a career change to data science is an achievable goal. Many professionals transition into data science by building on existing skills in analytical thinking and problem solving while developing technical expertise.
You may be wondering what potential career paths look like, how to make the transition, and which skills are needed to get started. An online Master of Information and Data Science can help bridge the gap between where you are now and where you want to be. This guide outlines what to expect — from day-to-day responsibilities and earning potential to steps to successfully move into a data science career.
Key Takeaways
- Many pathways for a career change to data science: Professionals from marketing, finance, accounting, supply chain, and other roles can transition by building on existing analytical skills and learning technical tools.
- Growing field with strong earning potential: Data science roles continue to expand across industries, offering competitive salaries and long-term career opportunities.
- Steps to a career in data science: Build foundational skills, create projects, gain real-world experience, and pursue structured learning to move into data science roles.
Is a Career Change to Data Science Right for You?
A common misconception is that you need a degree in computer science or years of programming experience to transition into data science. In reality, many professionals enter the field from non-technical backgrounds by building on skills they already have. Data scientists typically analyze and interpret data to solve problems and inform decisions, making skills like critical thinking, data analysis, and communication just as important as technical expertise.
People transitioning into data science often come from fields like finance, marketing, operations, and supply chain. These roles frequently involve interpreting trends, making data-driven decisions, and improving processes — all of which translate well into data science. While you may need to build technical skills such as programming or statistics, you might already have relevant experience to build on.
These questions can help you evaluate your interest in and readiness for a data science role:
- How could my current experience translate into a data-focused role?
- Where is there overlap between my experience and a data scientist’s? What are the gaps?
- Do I enjoy solving complex problems and identifying data patterns?
- Am I interested in building technical skills like Python, SQL, or data visualization?
Mapping Your Career Change to Data Science
The table below shows how individuals from different professional backgrounds can use their current skills in data science roles across a range of industries.
| Your Background | Key Skills You Bring | If You Enjoy… | Potential Data Science Roles |
|---|---|---|---|
Marketing1 | Customer data analysis, campaign measurement, trend forecasting, data visualization and reporting | Understanding customer behavior, experimenting with strategies, optimizing performance | Business intelligence analyst, marketing data scientist |
Finance2 | Financial data analysis, trend evaluation, mathematical modeling, market analysis, forecasting, reporting and presentations | Building models, analyzing performance, identifying patterns | Business intelligence analyst, financial data analyst, quantitative analyst |
Accounting3 | Structured data analysis, financial reporting, detecting issues or risks, recommending operational or financial changes | Working with structured data, ensuring accuracy, identifying anomalies | Data scientist, data analyst, business intelligence analyst |
Supply chain4 | Operational risk analysis, performance metrics, demand forecasting, process optimization | Improving systems, optimizing performance, forecasting demand | Logistics analyst, supply chain data scientist |
Depending on your background and interests, you may also pursue more specialized roles within the data science ecosystem, such as machine learning specialist or database administrator. These paths typically require deeper expertise in areas like statistical modeling, programming, or data architecture.
Ready to Explore a Data Science Career Path?
Get your master’s in information and data science and earn a certificate
from the UC Berkeley School of Information (I School).
Why Make a Career Change to Data Science?
Data science continues to stand out as a strong career option due to high demand, competitive salaries, and long-term growth potential. According to the U.S. Bureau of Labor Statistics (BLS), employment for data scientists is projected to grow much faster than average (34 percent), reflecting the increasing reliance on data across many industries. In addition to strong job growth, data science roles often offer high earning potential. The median annual wage for data scientists was $112,590 in 2024.
What Does a Career in Data Science Look Like?
What is data science and what does a data scientist do? Data scientists identify meaningful questions, gather and organize data from multiple sources, and turn their analysis into actionable insights. Then, they communicate their findings in ways that inform decisions and help organizations address challenges.
Because data skills are needed across industries — from health care and finance to technology and retail — there are a wide range of careers in data science that allow professionals to apply their skills in different contexts.
A data scientist typically has leadership ability, specialized skills, and business savvy. Types of data scientists include:
- Data scientist: use analytics and statistical methods to shape decision-making.
- Data analyst: focuses on insights and reporting.
- Data engineer: builds and maintains data systems.
- Machine learning engineer: develops predictive models.
While these roles vary in technical depth, they all center on using data to solve problems and support decision making.
How to Transition Into Data Science (Step-by-Step)
Moving into data science doesn’t require starting over. Many career changers build on existing analytical experience while developing new technical skills. The following steps reflect commonly recommended pathways for entering data science roles, including building technical skills, developing a portfolio, and gaining practical experience.
1. Learn foundational data science skills.
Start with core tools such as Python, SQL, and basic statistics. These skills allow you to analyze datasets, build simple models, and understand how data-driven decisions are made.
2. Build projects or a portfolio.
Hands-on projects help demonstrate your skills. Consider analyzing customer data, forecasting trends, or optimizing processes related to your current field.
3. Gain real-world experience.
Look for opportunities to apply data skills in your current role, freelance work, or volunteer projects. Even small initiatives, such as building dashboards or analyzing performance metrics, can build your credibility.
4. Network and apply strategically.
Connect with professionals in data roles, tailor your resume to highlight transferable skills, and target entry points such as analyst or business intelligence positions.
Skills You Need for a Data Science Career Change
A successful transition into data science typically combines technical, analytical, and communication skills. Common skills include:
- Technical skills: Programming in languages such as Python or Java, along with familiarity with data structures, algorithms, and data management concepts.
- Analytical skills: Linear algebra concepts, statistics, quantitative reasoning, and identifying patterns in data.
- Communication skills: Translating technical findings into insights and presenting results to stakeholders.
Many career changers already bring core analytical or domain expertise and focus on building programming and mathematical foundations to support data-driven work.
What Do Data Scientists Make?
Data science roles typically offer competitive salaries across industries. According to the BLS, the median annual wage for data scientists was $112,590 in 2024, with the lowest 10 percent earning under $63,650 and the highest 10 percent earning more than $194,410.
Compensation varies based on experience, specialization, and industry. Entry-level analysts and junior data roles generally earn less, while advanced positions such as machine learning specialists or senior data scientists tend to earn higher salaries. Strong demand across industries also contributes to long-term earning potential and career growth.
Industries Where Data Scientists Work
Data science roles span a wide range of industries, reflecting the growing demand for data-driven decision-making. Organizations use data science to optimize operations, forecast trends, and improve customer experiences across sectors.
Common industries for data scientists include:
- Technology and information services
- Financial services and investment firms
- Healthcare and biotechnology
- Travel and tourism
- Supply chain, transportation, and aviation
- Utilities and infrastructure
- Professional services and consulting
This breadth allows data scientists to apply their skills in industries that match their interests or prior experience.
Next Steps: Start Your Data Science Career Change
If you’re ready to move forward, start by identifying the skills you want to build and roles that align with your background and interests. From there, consider hands-on projects, networking opportunities, and structured learning paths to deepen your technical expertise.
Programs such as UC Berkeley’s online Master of Information and Data Science can help you develop foundational knowledge in programming, statistics, and machine learning while applying those skills to real-world problems.
Created by the online Master of Information and Data Science (MIDS) program from the UC Berkeley School of Information.
- Market Research Analysts and Marketing Specialists. (2026). O*NET OnLine. Retrieved April 24, 2026. ↩︎
- Financial and Investment Analysts. (2026). O*NET OnLine. Retrieved April 24, 2026. ↩︎
- Accountants and Auditors. (2026). O*NET OnLine. Retrieved April 24, 2026. ↩︎
- Supply Chain Managers. (2026). O*NET OnLine. Retrieved April 24, 2026. ↩︎