What to Know
Thinking about graduate school in statistics, data analytics, or data science? These fields are growing rapidly and offer exciting opportunities across industries—from tech and healthcare to government and academia. But before applying, it’s essential to understand the differences between these programs, what they typically entail, and how to choose the right path for your goals.
Understanding the Differences
Statistics
A statistics graduate program focuses on the theoretical foundations of data analysis, probability, and inference. Expect rigorous coursework in mathematical statistics, linear models, Bayesian methods, experimental design, and more. If you're interested in developing new statistical or machine learning methodologies, leading research teams, or contributing to scientific knowledge this might be the route for you.
Data Analytics
Analytics programs are typically more applied and business-oriented. These programs focus on tools and methods to process, visualize, and draw actionable insights from data. Topics often include data wrangling, dashboarding, business intelligence, and sometimes lighter exposure to machine learning. These programs are designed to help the student use established tools and methods at a basic level to solve problems in industry.
Data Science
While the term "data science" is used quite broadly, often data science programs are a blend of statistics and computer science, covering predictive modeling, machine learning, programming (Python, R, SQL), and often cloud computing or AI frameworks. These are ideal for students wanting to work in tech, start-ups, or applied research settings.
What Will you Learn?
| Content Area | Statistics | Data Analytics | Data Science |
| Probability and Inference | Strong focus | Basic coverage | Moderate coverage |
| Programming | Extensive (R, Python, SAS) | Limited (Excel, SQL, R) | Extensive (Python, R, SQL) |
| Machine Learning | Some | Basic coverage | Strong focus |
| Depth | Deep theoretical foundation | High-level focus | Broad, technical, and applied |
| Communication and Visualization | Moderate | Strong focus | Moderate |
| Math Requirements | Moderate to high | Low to moderate | Moderate to high |
Questions to Ask Before you Apply
What are your career goals?
Want to be a statistician, data scientist, or data analyst? Your answer will determine the best fit.
Do you prefer theory or application?
Statistics programs tend to lean theoretical, focusing on foundational knowledge to support continued methodological learning even after graduation. Analytics and data science programs lean more tools-based, focusing on using methods used within the industry.
How strong is your math background?
For statistics and data science, calculus and linear algebra are necessary prerequisites. Analytics programs may be more flexible.
What programming experience do you have (or want to gain)?
Some programs expect prior experience in Python or R. Others will teach you from scratch.
Do you want to pursue a Ph.D. later?
A master’s degree in statistics is usually a stronger stepping stone toward a Ph.D. (in any field).
Tips for Finding the Right Program for You
- Review course syllabi: Not all programs use the same terminology. A “data analytics” program at one university may look like a “data science” program at another.
- Talk to current students and alumni: They can offer insight into the culture, workload, and career outcomes.
- Evaluate career support: Look into internship opportunities, industry partnerships, and job placement statistics.
- Don’t skip over statistics fundamentals: Even in applied roles, a solid grasp of probability and inference gives you an edge.
Preparing for Graduate School in Statistics, Data Analytics, or Data Science
This table is meant as a useful guide of content and experiences that will build your preparation for data-focused graduate programs. However, requirements will vary depending on the program and your intended focus. Reach out to your programs of interest to identify the best ways to prepare for that program.
| Preparation Area | Statistics | Data Analytics | Data Science |
| Courses |
| Calculus I—III | | Linear Algebra | | Probability | | Mathematical Statistics | | Intro to Programming (R/Python) |
|
| Intro Statistics | | Business Analytics | | Excel | | SQL | | Intro to R/Python |
|
| Linear Algebra | | Calculus I—III | | Intro Statistics | | Statistical Methods | | Programming (Python/R) | | Machine Learning and/or AI |
|
| Job/Internship Experience |
| Technical internships in e.g., research labs | | Data roles with focus on inference or modeling | | Helpful but not expected |
|
| Business intelligence internships | | Roles using dashboards, KPIsm Excel/SQL reporting | | Helpful but not expected |
|
| Applied data science or software roles | | Machine learning engineering or product analytics internships | | Helpful but not expected |
|
| Teaching Assistant Experience |
| Highly encouraged | | Supports deeper content mastery and communication |
|
| Helpful but not expected | | Good for communication and leadership development |
|
| Encouraged | | A plus for academic communication practice |
|
| Research Assistant Experience |
| Helpful but not expected | | Exposure to long-term and/or deeper statistical analysis problems |
|
| Helpful but not expected | | Consider applied-focused research opportunities |
|
| Encouraged | | Helpful for hands-on machine learning projects or algorithm development |
|
| Programming Skills |
| R is most common | | Some exposure to Python or SAS | | Familiarity with simulation studies is a bonus |
|
| Excel and SQL | | Introductory R or Python | | Data visualization tools (e.g., Tableau) |
|
| Python and R fluency | | SQL | | Git | | Experience with cloud tools and machine learning libraries (e.g., scikit-learn and TensorFlow |
|
| Other Experiences |
| Reading research papers | | Attending academic seminars | | Competing in data analysis competitions |
|
| Working with messy data | | Creating dashboards or BI tools | | Business case study competitions |
|
| Data science competitions (e.g., Kaggle) | | Open-source contributions | | Personal data analysis blog |
|
Career Goals
While job titles and descriptions can vary widely, here is a short list of one definition of different types of jobs and degrees required. This may help you identify the right type of data analysis program for you.
| Job Title | Job Description | Typical Education Required |
| Statistician | Designs experiments, analyzes data, develops analysis models, and interprets results to inform decision-making in business, healthcare, and research. | MS in Statistics |
| Analyst (e.g., Data Analyst or Business Analyst) | Collects, cleans, and summarizes data to create reports or dashboards. Often answers business questions and communicates insights. May use tools like Tableau or Power BI to build dashboards and track KPIs. | BS in Statistics or related field; or, BS/BA in application field with MS in Data Analytics |
| Data Scientist | Builds predictive models, applies machine learning and statistical analyses, and integrates structured and unstructured data to solve complex problems. | MS in Statistics, Data Science, Machine Learning, Computer Science, or related field |
| ML Engineer | Designs and deploys machine learning pipelines into production. Focuses on scalability, model optimization, and systems integration. | MS in Data Science, Machine Learning, Computer Science, or related field |
| Quantitative Researcher | Develops new models or algorithms to support financial, scientific, or strategic research. Heavy on math, coding, and optimization. | PhD (or MS with strong math background) |
| Biostatistician | Applies statistical methods to public health, clinical trials, and biology-related fields. Often collaborates with medical researchers. | MS or PhD in Statistics or Biostatistics |
Conclusion
Graduate school is a major investment—of time, money, and effort. But it can also be a powerful accelerator for your career. Whether you're drawn to theoretical rigor, applied business insights, or cutting-edge computation, there’s a program out there for you.
Take the time to research thoroughly, reflect honestly on your interests and strengths, and seek advice from trusted mentors.
