How to become a data scientist: what you should know

By: The Capella University Editorial Team with Bradly E. Roh, PhD, DBA and Interim Dean and Vice President for the School of Business, Technology and Health Care Administration

Reading Time: 8 minutes

If you’re looking into how to become a data scientist, you’re probably not lacking for sources of information. You’re looking for clarity: what skills and training the role actually requires and a sensible order in which to build toward it.

The field draws on programming ability and statistical thinking, applied to real-world problems across industries. For working professionals, the preparation follows a defined progression. Understanding where you sit within it is the most practical place to start.

Explore the skills that prepare you for data science roles and how to acquire them in a structured way.

Neither Strategic Education, Inc., Capella University, nor any of their affiliates promotes, endorses or has any business relationship with the products or platforms mentioned herein. 

What does a data scientist do?

A data scientist analyzes raw datasets to inform business decisions. They prepare data, build predictive models and identify patterns that businesses can act on.

For example, a data scientist in a retail company might discover that customers who browse a product three times without purchasing are 70% more likely to buy if offered a discount within 24 hours. Based on this information, the business can automatically trigger a discount email the moment a third browse is recorded.

Across retail, healthcare, finance and tech, data scientists are responsible for:

• Sourcing and cleaning datasets so they’re accurate enough to build on. This includes handling missing values and inconsistent formatting.
• Building algorithms that forecast outcomes, such as which loan applicants might default or which social work cases may need urgent intervention.
• Designing A/B tests to measure real-world impact before a change goes live.
• Translating model outputs into reports and recommendations that business stakeholders can understand and use for decision-making.

What is the difference between a data scientist and a data analyst?

A data scientist’s role is forward-looking. They typically predict. A data analyst, on the other hand, explains trends based on historical data.

	Data analyst	Data scientist
Primary focus	Conduct root-cause analysis  to explain what happened and identify trends	Build predictive models to forecast what could happen and solve complex problems
Technical  depth	Strong foundation in SQL, Excel and data visualization tools	Advanced programming (Python, R), machine learning algorithms and statistical modeling
Question   approach	Answer predefined business questions using available data	Formulate new questions and design methods to answer them using data
Data scope	Work primarily with structured, clean datasets	Handle large, unstructured or complex datasets requiring advanced processing
Educational background	Typically a bachelor’s degree	A master’s degree or PhD may be required for specialized roles

If you’re comparing both roles to plan your career, here’s a quick framework to help you decide.

Consider pursuing a data scientist role if you:

• Have a background in mathematics/statistics or computer science and are comfortable working with programming languages and big data
• Are interested in predictive modeling
• Want to focus on experimentation and long-term technical architecture rather than day-to-day business reporting

Consider pursuing a data analyst role if you:

• Have a background in finance or marketing and want to pair that knowledge with technical skills
• Prefer translating data into findings and communicating reports to stakeholders over working with technical models
• Want to build expertise in SQL commands and data visualization tools like Power BI and Tableau

Regardless of which path you choose, both roles demand a strong technical grounding.

With Capella University’s Bachelor of Science (BS) in Information Technology, Data Analytics and Artificial Intelligence program, you study data cleansing and quality measurement. You also learn about ethical and legal policies and how to apply AI to data mining and analytics projects.

If you’re ready for more advanced analytical work, Capella’s Master of Science (MS) in Analytics program can help you build competence in technologies that are highly relevant in the current market, including SAS, Tableau and Python. You also learn to use applied analytics and statistical methods to address business problems. 

Both programs are available online in the GuidedPath learning format. In this format, the schedules are set to help you stay on track but you’re free to access the course room 24/7. You’ll engage with peers over weekly discussions and receive regular feedback from instructors on your assignments.

Learn more about pursuing a career in data analytics.

How to become a data scientist

Becoming a data scientist is a progressive process. You master the fundamentals of modeling and statistics first and then build projects to apply algorithms to real datasets. Once you gain some experience, you can develop a portfolio that demonstrates your work.

Choose your educational pathway

A bachelor’s degree is a common starting point. A BS in Information Technology or a BS in Computer Science degree can formalize the skills you’ve been applying on the job or self-directed projects. And if you already hold a bachelor’s degree, an MS in Information Technology can help you prepare for specialist and leadership roles.

Besides formal degrees, bootcamps such as TripleTen’s offer intensive, skills-focused training in tools like Python libraries and machine learning frameworks.

To validate specific skills and highlight them in your resume, you IT certifications could also be an interesting option.

• The IBM Data Science Professional Certificate and the Certified Data Science Professional (CDSP™) are recognized industry certifications.
• The AWS Certified Machine Learning – Specialty and Certified Analytics Professional (CAP) are typically pursued by information technology professionals at a more advanced level.

Build core technical skills

Three skills anchor most data science work, and you’ll use all of them regularly. Python handles the execution – from cleaning data to writing and testing models. Statistics and probability give you the conceptual grounding to understand what a model is actually doing and where it may break down. Data visualization translates that technical work into outputs that stakeholders can read and use.

To develop and practice these skills, you could take on coding competitions and hackathons on platforms like Kaggle, which let you work with real datasets without needing a formal project structure. You can build personal projects to understand how algorithms work and learn to handle structurally flawed or missing data using statistical techniques.

Develop domain knowledge

Data science skills applied without an industry context might produce models that are technically sound but difficult to use in practical scenarios.

A data scientist without proper industry knowledge might build a credit risk model based on income levels, missing the fact that spending behavior and payment history are stronger indicators of default risk. The result: more high-risk borrowers get approved than reliable ones.

Your target industry shapes the datasets you work with. It determines which questions are worth asking and how your findings get used. The earlier you develop that context, the more purposeful your technical work becomes.

Gain practical experience

While working professionals learn on the job, early-career students often find it difficult to practice their skills.

Consider internships to collaborate with technical teams and learn from experts. Volunteer with organizations such as DataKind or Statistics Without Borders that let you apply data science skills to real-world problems while contributing to meaningful work. You might also contribute to open-source projects on GitHub and join communities where you participate in project work alongside other members.

Create a portfolio

Use platforms like datascienceportfol.io or Notion to create your portfolio. But before that, take some time to set up a strong GitHub profile. Then, list your GitHub personal projects along with their links.

Ensure each project has a clear business question with a documented methodology and a result that a non-tech stakeholder can understand. For long-form articles and in-depth case studies, try Medium.

You might also want to include the experiences where you solved a real problem by applying data or business analytics methodologies. Showcase dashboards you created for your team or an analysis you ran to support business decisions.

Apply for roles

Apply for relevant roles posted on job boards, position yourself and connect with industry experts and companies on LinkedIn. Participate in networking events that can help you build a professional network.

Capella’s Career Development Center supports active students and alumni by helping them connect with professionals. It’s a flexible networking experience where you can discuss opportunities, learn about your industry, explore career paths and gain practical guidance without committing to mentorship programs. 

Is AI replacing data scientists?

Not really. AI is changing the way data scientists work and where they spend their time.

AI tools handle the repetitive parts of data work, like cleaning datasets and running standard queries. They can generate initial visualizations and flag statistical anomalies in larger datasets. What they cannot do is decide which problem is worth solving or validate whether a model’s output makes sense contextually.

The accuracy of AI outputs also requires human supervision, as these systems often hallucinate. They generate factually incorrect information.

Here are a few cases that show what happens when you don’t monitor AI outputs.

• Deloitte used generative AI to produce a $290,000 report for the Australian government. The document contained fabricated citations and references to nonexistent academic research papers, which resulted in Deloitte partially refunding the contract.
• Air Canada’s customer service chatbot gave a passenger incorrect information about bereavement fare eligibility. While the airline argued that the chatbot was a separate legal entity, it resulted in a lawsuit. The British Columbia Civil Resolution Tribunal ordered Air Canada to pay compensation to the passenger.
• In a promotional video, Google’s Bard incorrectly claimed that the James Webb Space Telescope took the first pictures of an exoplanet. As the error was caught by outside observers, Alphabet, Google’s parent company, lost $100 billion in market value.

In each case, the errors could have been prevented with a human expert reviewing the output. This is precisely why the role of a data scientist remains crucial.

They can evaluate whether a model’s output is trustworthy, understand where training data introduces bias and translate findings for non-tech teams, something that AI cannot replicate. So, when you’re building toward this field, make sure you understand how AI tools work and where they fail.

Next steps toward a data science career

The path into data science follows a clear sequence: build your technical foundation, develop domain context, apply your skills through real projects and document what you’ve built. How you approach that sequence depends on where you are now.

If you’re starting out, a formal degree gives you the structured foundation that self-study alone rarely provides. If you’re already working in a technical role, formalizing what you know through an online program may be the most direct next step. Either way, portfolio work and practical experience don’t wait for a credential, so you should start those in parallel.