Master of Data Science

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Master of Data Science

Illinois Institute of Technology

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Accredited degree (100% online)

Offered by Illinois Institute of Technology

No application required

Degree admission is entirely performance-based. Bachelor's degree is required.

12–15 months

33 credit hours of graduate coursework

Approximately $455 (USD) per credit

(About $455 x 33 credits = $15,000), with pay-as-you-go tuition for each course.

Save up to $2,730

You may be eligible to have your prior learning on Coursera recognized for credit.

Program Overview

In this program, you’ll find a blend of mathematics, statistics, and computer science. You’ll learn how to analyze data, how to visualize results, and how to articulate discoveries. Work your way through core courses on statistics, machine learning, project management, big data, and more—then round out your data science education with electives that cover subjects such as algorithms and operating systems. It all culminates with a practicum capstone course where you’ll combine what you learn throughout the program.

Hear from Professor Kiah Ong about the pathway courses and the Master of Data Science program

Complete List of Course Offerings

If you're interested in viewing a complete list of the Illinois Tech online course offerings on Coursera visit this page.

Flexible
Start a pathway course and start earning credit towards your degree.
Convenient
Choose from 6 enrollment dates throughout the year and complete the coursework in your own time.
On-the-go
Take your studies on the go with mobile-friendly learning on iOS and Android. Some assignments can’t be completed on a mobile device.

Admissions Information

Important Dates

Spring A - Enrollment deadline is January 28th

  • November 4, 2024: Spring A Registration
  • January 13, 2025: Spring A courses begin
  • January 28, 2025: Last day to register for Spring A Courses
  • February 17, 2025: Last day to upgrade open content for credit for Spring A
  • March 7,2025: Last day of Spring A courses

Spring B - Enrollment deadline is March 31st

  • November 4, 2024: Spring B Registration
  • March 10, 2025: Spring B courses begin
  • March 31, 2025: Last day to register for Spring B Courses
  • April 21, 2025: Last day to upgrade open content for credit for Spring B
  • May 9,2025: Last day of Spring B courses

Upcoming Events

Stay tuned

Additional Resources

Illinois Tech Academic Calendar

Course Offerings by Session

PBA Quick Start Guide

Admissions Information

Important Dates

Spring A - Enrollment deadline is January 28th

  • November 4, 2024: Spring A Registration
  • January 13, 2025: Spring A courses begin
  • January 28, 2025: Last day to register for Spring A Courses
  • February 17, 2025: Last day to upgrade open content for credit for Spring A
  • March 7,2025: Last day of Spring A courses

Spring B - Enrollment deadline is March 31st

  • November 4, 2024: Spring B Registration
  • March 10, 2025: Spring B courses begin
  • March 31, 2025: Last day to register for Spring B Courses
  • April 21, 2025: Last day to upgrade open content for credit for Spring B
  • May 9,2025: Last day of Spring B courses

Upcoming Events

Stay tuned

Additional Resources

Illinois Tech Academic Calendar

Course Offerings by Session

PBA Quick Start Guide

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Build the skills you’ll need in programming, statistics and data analysis

Build a solid foundation for computer science expertise, with key programming concepts and problem-solving techniques using Python, one of the most widely used and versatile programming languages.

A focus on real-life problems and practical exercises will provide you with a strong working knowledge and sought-after computer science skills.

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Curriculum

You’ll build computing-specific academic skills across two distinct areas:

  • Qualitative reasoning skills: Exploring computer ethics, you’ll learn to think critically and communicate effectively about legal, moral and ethical issues related to your field of study.

  • Quantitative reasoning skills: You’ll use data analysis and produce engaging dashboards, apply common descriptive statistics to summarise datasets, and use data visualisation approaches to demonstrate patterns.

  • This module will provide practical experience in applying theoretical programming concepts in creative ways to solve real-world problems. Using Python, one of the most popular and versatile programming languages, you’ll build essential skills in this vital discipline.
  • On completion of this module, you’ll be proficient in implementing and manipulating data structures, managing and analysing data.
  • Topics covered include control flow statements, data structures, object-oriented programming, data wrangling with Pandas, databases and NumPy library.

  • This module provides a comprehensive knowledge base in the mathematics that underpin computer science.
  • You’ll refresh and secure your understanding of arithmetic and algebra, gain an overview of the functions and fundamentals of calculus and trigonometry, and explore a range of other mathematical concepts that will be relevant to your ongoing studies of computer science.
  • Topics covered include quadratic equations and parabolae, number bases and modular arithmetic, sequences and series, and vectors and geometry.

  • You’ll build a strong foundation of statistical knowledge on this module. You’ll grasp the fundamental principles of probability theory, allowing you to understand and apply standard statistical operators and recall essential probability distributions.
  • You’ll perform statistical inference in order to make informed decisions based on data analysis, and construct causal models to meet the requirements of different statistical analysis contexts.
  • Topics covered include discrete and continuous probability distributions, interval estimation, hypothesis testing principles, contingency tables and the chi-squared test, and simple linear regression.