Data Science MS

2 Years On Campus Masters Program

De Paul University

Program Overview

The Master of Science in Applied Statistics at DePaul University is a STEM-designated program designed to prepare students for the rigorous application of statistical methods in real-world problem-solving across business, healthcare, government, and technology sectors. Offered through the College of Science and Health, the program combines a solid foundation in statistical theory with modern computational tools, making it ideal for those pursuing careers in data analysis, biostatistics, predictive modeling, or risk analytics.

It is well-suited for students from mathematics, engineering, economics, or social science backgrounds who are seeking technical depth and practical application.

Program Format & Duration

  • Delivery: On-campus (Chicago), with select hybrid/online options

  • Start Terms: Fall, Winter, Spring

  • Duration: Typically 2 years (full-time), flexible for part-time students

  • STEM-designated (eligible for OPT extension for international students)

  • No thesis required (capstone or comprehensive exam option available)


Core Curriculum Components

The MS in Applied Statistics focuses on statistical theorycomputational proficiency, and domain-specific applications.

Core Areas of Study

  1. Statistical Foundations

    • Applied Statistics

    • Mathematical Statistics

    • Regression Analysis

    • Experimental Design

    • Statistical Inference

  2. Computational & Analytical Tools

    • Statistical Software (R, SAS, SPSS)

    • Programming for Data Analysis (Python, SQL)

    • Data Visualization

    • Big Data Analytics (select electives)

  3. Specialized Electives
    Students may tailor the program with electives in:

    • Time Series and Forecasting

    • Biostatistics

    • Bayesian Methods

    • Nonparametric Statistics

    • Multivariate Analysis

    • Statistical Consulting

  4. Capstone / Culminating Experience

    • Either a comprehensive exam or a project-based capstone course where students work on real-world statistical applications under faculty supervision.

Experiential Learning (Research, Projects, Internships etc.)

DePaul integrates practical exposure through projects, industry ties, and research-oriented activities, helping students apply classroom concepts to complex data challenges.

Capstone Project

  • Focuses on solving applied statistical problems

  • Projects may involve:

    • Healthcare analytics

    • Market research

    • Government policy analysis

    • Sports analytics or social science data

Internship Opportunities

  • Located in the heart of Chicago, DePaul benefits from strong connections with local industries

  • Internship placements are supported in fields such as:

    • Finance and Insurance

    • Public Health and Biotech

    • Retail and Marketing Analytics

    • Nonprofits and Public Policy

Departmental Engagement

  • Students can collaborate with faculty on research initiatives or participate in the Statistical Consulting Center, providing analytical services to university and external clients.

Progression & Future Opportunities

Graduates of DePaul’s MS in Applied Statistics are equipped with both theoretical understanding and hands-on skills that are highly sought after across industries where data-driven decision-making is critical.

Career Outcomes

Typical job titles include:

  • Statistician
  • Data Analyst / Business Intelligence Analyst
  • Biostatistician
  • Quantitative Analyst
  • Risk Analyst
  • Research Scientist (Social Sciences, Education, or Public Health)

Top Employers

Alumni have secured positions at:

  • Blue Cross Blue Shield
  • Nielsen
  • Abbott Laboratories
  • JPMorgan Chase
  • U.S. Census Bureau
  • City of Chicago Departments and Public Health Agencies

Further Study

Graduates may also pursue:

  • PhD programs in Statistics, Biostatistics, or Data Science
  • Professional certifications in analytics or statistical software
  • MBA or MPA degrees with a data specialization

Salary Overview-

  • Average starting salary is $72241
  • Typical range is $55000 to $100000
  • Nearly half of graduates receive a signing bonus averaging $6350
  • Seventy-nine percent are employed within six months of graduation

Top Employers-

  • Graduates have joined leading employers across the United States
  • Examples include
  • American Express
  • Citi Group
  • Food and Drug Administration
  • Top consulting firms
  • Major tech companies
  • These organizations actively recruit through strong partnerships with the program

Geographic Placement-

  • Most graduates find roles in major US cities
  • New York City sees the highest concentration with 32 percent
  • Boston follows with 11 percent
  • Chicago accounts for 8 percent
  • While some pursue international roles the program strongly supports U S based employment

Industry Sectors-

  • Graduates work across several fields including
  • Finance and Banking
  • Technology and Software
  • Government and Public Health
  • Consulting
  • Healthcare and Biotech
  • Each sector offers a range of analytics and data science opportunities

Career Growth and Progression-

  • Graduates often begin in analyst roles such as
  • Data Analyst
  • Statistician
  • Statistical Programmer
  • Machine Learning Engineer
  • Analytics Consultant
  • Quantitative Analyst
  • Over time, some take on leadership or specialist roles within their industry

What This Means for You-

  • This program gives you a strong launch into the data science field
  • Starting salaries are competitive and often include signing bonuses
  • You can explore a variety of industries and career paths
  • Locations like New York Boston and Chicago offer strong hiring activity
  • Graduates see steady career growth in analytics machine learning and consulting roles

Program Key Stats

$46,776
$ 75



70 %
No
Yes

Eligibility Criteria

3 Year

305
155
3.0
505
7.0
90
2:1

Additional Information & Requirements

Career Options

  • Data Analyst
  • Data Scientist
  • Business Analyst
  • Statistician
  • Statistical Programmer
  • SAS Programmer
  • Machine Learning Engineer
  • Deep Learning Engineer
  • AI Researcher
  • Data Engineer
  • BI Developer
  • Data Architect
  • Analytics Consultant
  • Advisory Consultant
  • Quantitative Analyst
  • Risk Analyst
  • Operations Analyst
  • Marketing Analyst
  • Research Scientist
  • Insights Analyst
  • Forecasting Analyst
  • Healthcare Data Analyst
  • Financial Data Analyst
  • Product Analyst

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