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Big data. Bigger ideas.

The digitization of information—social networks, business transactions, healthcare records and more—has transformed the world, necessitating a demand for people who understand all of the big data being collected and who can take action to help individuals, corporations and organizations become smarter, faster and ultimately more effective.

Through Drew’s Master of Science and graduate certificate programs in Data Science, you will:

  • Learn how to obtain, prepare and manage data from a wide variety of sources.
  • Gain mastery of data analytical techniques.
  • Leave with a portfolio of projects and work experience from experiential learning projects and internships.
  • Focus on statistics, data science, and programming.

Read more about the Data Science degree, and review the Program Requirements.

Programs

Master of Science (30-36 credits, one year full-time)

Foundational courses (6 credits)
Must be taken prior to enrolling in core courses. May be taken during the summer before the program starts.

  • Introductory Statistics (or equivalent)
  • Introduction to Programming (or equivalent)

Fall (12 credits)

  • Statistics Using R (3 credits)
  • Computational Thinking/Programming in Python (3 credits)
  • Applied Regression Analysis (3 credits)
  • Elective (3 credits)

Spring (12 credits)

  • Networks and Text Mining (3 credits)
  • Data Science Using SQL and Relational Databases (3 credits)
  • Statistical Machine Learning (3 credits)
  • Data Visualization and Communication (3 credits)

Summer (6 credits)

  • Data Science Internship (3 credits)
  • Capstone: Case studies in Data Science (3 credits)

VIEW COURSE REQUIREMENTS >

Master of Science (30-36 credits - two years part-time - fall start)

Foundational courses (6 credits)
Must be taken prior to enrolling in core courses. May be taken during the summer before the program starts.

  • Introductory Statistics (or equivalent)
  • Introduction to Programming (or equivalent)

Fall I (6 credits)

  • Statistics Using R (3 credits)
  • Computational Thinking/Programming in Python (3 credits)

Spring I (6 credits)

  • Networks and Text Mining (3 credits)
  • Data Science Using SQL and Relational Databases (3 credits)

Fall II (6 credits)

  • Applied Regression Analysis (3 credits)
  • Elective (3 credits)

Spring II (6 credits)

  • Statistical Machine Learning (3 credits)
  • Data Visualization and Communication (3 credits)

Summer (6 credits)

  • Data Science Internship (3 credits)
  • Capstone: Cases studies in Data Science (3 credits)

VIEW COURSE REQUIREMENTS >

Master of Science (30-36 credits - two years part-time - spring start)

Foundational courses (6 credits)
Must be taken prior to enrolling in core courses. May be taken during the summer before the program starts.

  • Introductory Statistics (or equivalent)
  • Introduction to Programming (or equivalent)

Spring I (6 credits)

  • Data Science Using SQL and Relational Databases (3 credits)
  • Data Visualization and Communication (3 credits)

Fall I (9 credits)

  • Statistics Using R (3 credits)
  • Computational Thinking/Programming in Python (3 credits)
  • Applied Regression Analysis (3 credits)

Spring II (6 credits)

  • Networks and Text Mining (3 credits)
  • Statistical Machine Learning (3 credits)

Summer (6 credits)

  • Data Science Internship (3 credits)
  • Capstone: Case studies in Data Science (3 credits)

Fall II (3 credits)

  • Elective (3 credits)

VIEW COURSE REQUIREMENTS >

Graduate Certificates (12 credits each)

Our graduate certificates enhance core skills required to draw information from data. You may choose from one of three emphases: Statistics, Data Science or Business Analytics. Or, design your own program in consultation with the program director. If you complete one of these certificate programs, you may apply the credits to the Master of Science in Data Science.

Prerequisites (6 credits)
Must be taken prior to enrolling in a Data Science certificate program. May be taken during the summer before the program starts.

  • Introductory Statistics (or equivalent)
  • Introduction to Programming (or equivalent)

Data Science Certificate: Statistics (12 credits)

  • Statistics Using R (3 credits)
  • Applied Regression Analysis (3 credits)
  • Computational Thinking/Programming in Python (3 credits)
  • Statistical Machine Learning (3 credits)

VIEW COURSE REQUIREMENTS >

Data Science Certificate: Data Science (12 credits)

  • Statistics Using R (3 credits)
  • Networks and Text Mining (3 credits)
  • Computational Thinking/Programming in Python (3 credits)
  • Data Analytics Using SQL and Relational Databases (3 credits)

VIEW COURSE REQUIREMENTS >

Data Science Certificate: Business Analytics (12 credits)

  • Statistics Using R (3 credits)
  • Applied Regression Analysis (3 credits)
  • Financial Quantitative Analysis (3 credits)
  • Computational Finance and Large Data Analysis (3 credits)

VIEW COURSE REQUIREMENTS >

Outcomes

With the digital transformation of academia and the workforce, it is not surprising that data science and data analytics jobs are projected to grow at some of the fastest rates in the near future.*

Job title Average salary Projected five-year growth
Analytics managers $105,909 15%
Data scientists & advanced analysts $94,576 28%
Data-driven decision makers $91,467 14%
Data systems developers $78,553 15%
Data analysts $78,553 16%
Functional analysts $69,162 17%

*Burning Glass Technologies