How to become a data scientist

data scientist programming on computer

Published October 21, 2026

Interested in becoming a data scientist? There’s no single pathway to the career, but many professionals share a strong theoretical foundation and mastery of technical skills.

Here is what it takes to build the skills and credentials that turn that curiosity into a career as a data scientist.

Key takeaways

  • A structured, applied data science program can build the technical skills and business fluency employers look for faster than piecing it together on your own.
  • Portfolio work matters as much as coursework. Employers want proof you can take a messy dataset and turn it into something a business can act on.
  • Employment of data scientists is projected to grow 35% from 2025 to 2035, much faster than the average for all occupations, according to the Bureau of Labor Statistics.

What does a data scientist do?

A data scientist is a technical professional who collects, cleans, and analyzes complex data to build predictive machine learning models and solve business problems. In practice, this role bridges engineering and business through three primary functions:

  • Investigator: Digging into a dataset to figure out what story it is actually telling, maybe even before anyone has asked the right question yet.
  • Builder: Constructing and testing models that turn a pattern or a hunch into something measurable and repeatable.
  • Translator: Taking a technical result and turning it into a recommendation leaders can act on with confidence.

The number of “hats” a data scientist wears is often what makes the role different from a pure engineering or analytics job.

Four steps to become a data scientist

Unlike careers that require licensure or certification, future data scientists have lots of pathways open to them. Different combinations of formal and informal learning, hands-on work, and collaborations can all potentially lead to a successful career.

Step 1: Choose your data science education path

A degree offers an expert-backed, collaborative learning experience designed to help you master both theoretical and practical data science skills. While a formal degree program may not be the best fit for everyone, those who value structure and a recognized credential may find that an online Master of Science in Applied Data Science is the path for them.

Job requirements vary by employer, but a degree from a hands-on program will often close major gaps for aspiring data scientists.

Syracuse University’s online Master of Science in Applied Data Science program offers three paths:

  • Accelerated: Complete the degree in as few as 12 months through full time, intensive enrollment.
  • Standard: Graduate in 18 to 24 months, the typical route for working professionals balancing one to two classes per term.
  • Decelerated: Take up to 36 months, a part-time option for those who need a lighter course load.

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Step 2: Master core data science skills

Breaking the data science skill set into categories can make it easier to see what to prioritize. According to O*NET OnLine, these are the skills associated with data scientists.

Programming and data tools

  • Python and R: The two most common languages for analysis, model building and automation. Python tends to be the starting point for most students given how widely it is used across the field.
  • SQL: Used to pull, filter and manage data directly from databases.
  • GitHub: Used to manage and version control code, especially on team projects. GitHub is the industry standard platform for this.

Statistics and machine learning foundations

  • Probability and statistical inference: The foundation for understanding whether a pattern in the data is meaningful or just noise.
  • Regression and classification: Core techniques for predicting outcomes and sorting data into categories.
  • Clustering: Used to find natural groupings in data without predefined labels.

Data visualization and communication tools

  • Tableau or Power BI: Common platforms for turning a model’s output into something a nontechnical audience can read at a glance.
  • Data storytelling: Less a tool than a skill, this is the ability to sequence a visualization so the audience reaches the right conclusion without extra explanation.

Rather than treat these skills as sequential, from coding to storytelling, developing them alongside one another can be most effective. Solid statistical grounding can inform model development and interpretable results.

The primary coursework in the online MS in Applied Data Science includes both a quantitative reasoning and machine learning course, ensuring students learn these fundamental skills in tandem early.

Step 3: Build core business and communication skills

The next generation of data scientists needs to build skills in framing problems and understanding organizational context, not just technical execution.

  • Business acumen: The ability to understand how an organization makes money and where a project fits into that picture, one of the most transferable skills a professional can build regardless of industry.
  • Stakeholder communication: Translating a technical result into language a marketing director, a hospital administrator or an executive team can act on without needing a background in statistics.
  • Financial literacy: A working understanding of budgets, cost and ROI, which underpins how a project’s value actually gets measured.
  • Project scoping: Defining what a project will and will not cover before work begins, one of the clearest predictors of whether a project stays on track.

Step 4: Build a project portfolio

A resume tells an employer what you studied; A portfolio shows what you can actually build. At minimum, aim for two or three end-to-end projects. Some examples of ways you can showcase your experience include:

  • Cleaning a genuinely raw dataset, such as pulling scattered survey responses into a structured, usable format
  • Building and evaluating a model, such as a churn prediction model that flags which customers are likely to leave
  • Presenting findings clearly to a nontechnical audience, such as turning a model’s output into a one page dashboard a stakeholder can act on without a walkthrough

Explore how Syracuse students turn classroom capstones into employer-ready work by reading our student and alumni profiles.

Examples of data scientist job requirements

Company job postings reveal more about which skills employers prioritize. Let’s take a look at three different job postings for entry level data scientist positions.

The following job postings are examples and represent a point-in-time look at what the kinds of requirements and skills were being requested. It does not represent what new or upcoming job postings will look like and should only be used for contextual purposes.

Data Scientist at a Global Technology Company

A developer at this level would contribute to the design, development and implementation of AI solutions for business problems. This role required a bachelor’s degree with proficiency in Python and its libraries, machine learning algorithms and Git. Experience with deep learning frameworks, model deployments tools and ethical AI principles were preferred.

Data Scientist I at a Bank and Financial Services Company

This role was responsible for analyzing and interpreting large datasets to uncover potential revenue generation opportunities and develop effective risk management strategies. A bachelor’s or master’s degree in data science or a related field was required. Candidates also needed experience in Python and SQL, LLM frameworks and Git.

Data Scientist, Senior at a Government and Military Contractor

This data scientist would work with clients to dig into their data and find solutions to their problems using algorithm development. The role required 5+ years of experience with programming languages like Python or R, experience creating ETL pipelines and knowledge of statistical analysis. “Nice to have” qualifications included experience with deep learning frameworks and generating data visualizations.

Your next step toward a data science career

Becoming a data scientist comes down to building the right foundation: a clear understanding of the role, the right degree, technical skill, business fluency and a portfolio that proves you can do the work. Syracuse University’s MS in Applied Data Science was built around that exact sequence, with faculty who are actively practicing what they teach guiding students through it, preparing graduates for paths that range from building a career in data science to more specialized roles like becoming a machine learning engineer.

You have the roadmap. Start your application and take the first step toward becoming a data scientist.

FAQs

  • How hard it is to become a data scientist will likely depend on your pre-existing skill set—those with experience in programming and math may find it easier to learn data science. However, both the theoretical and technical skills needed to become a data scientist are achievable for students that are motivated and willing to invest the time.

  • Often, employers expect a bachelor’s degree in a related field and a few years of experience to become a data scientist. A portfolio of work can help showcase experience in place of time at a job and an advanced degree can help career changers qualify or more advanced roles become available.

  • No single major is required to become a data scientist. The beauty of the field is that students can come from backgrounds in related fields like statistics or computer science, or other industries like business and the social sciences. What matters more is whether a program’s approach to data scientist schooling accounts for that variety, offering conditional admission or bridge courses for applicants without a strong technical foundation.

  • For someone earning a master’s degree while working, the timeline usually runs 18 to 24 months for the degree itself, with the overall path from career change to a first data science role typically landing somewhere between 18 months and three years depending on prior experience and job search timing. Career changers coming from adjacent fields like finance, healthcare analytics or marketing often move faster than this range suggests, since they already understand how to interpret data in a business context even if the coding itself is new.

  • Beyond a degree, employers typically look for proficiency in Python or R, working knowledge of SQL, some exposure to a cloud platform and evidence of applied project work, often in the form of a portfolio. Communication skills and business context matter nearly as much as the technical qualifications themselves.

Created by Syracuse University’s online Master of Science in Applied Data Science program.