Big Data, Machine Learning, and their Real World Applications

I. July 3–July 14, 2023
II. July 17–July 28, 2023
Day & Time:
Monday–Friday, 8:00–11:00 a.m. or 12:00-3:00p.m. or 5:00–8:00 p.m.
to be announced
Students with a basic understanding of algebra, geometry, statistics and computer programming will be most prepared for this course, but it is not required.

“I have gained invaluable skills in AI and Data Science.” – Rom F. | Glencoe, Illinois

Course Description

The exponential growth of data, advances in cloud computing, and machine learning have transformed every industry from retail and banking to healthcare and education. This introductory-level course enables participants to navigate the new reality of the “data economy,” in which data is the “the new oil”—a ubiquitous and invaluable asset.

We focus on the strategic use of data and innovative technologies to derive actionable business insights. Participants develop a strong foundation in data-driven thinking for solving real-world problems. They are introduced to a variety of popular technologies for data analytics and gain a familiarity with programming in either R, a software environment for statistical computing and graphics, or Python. Much of the in-class work involves working with one of these two languages. Students learn how to import, export, manipulate, transform, and visualize data; use statistical summaries; and run and evaluate machine learning models.

From the start of the course participants are immersed in the world of data: they are introduced to the concepts of big data, artificial intelligence, the internet of things, cloud computing, and data ethics in the context of real-world business scenarios. Through hands-on experience and practice they study data harvesting and exploration, as well as the basics of data visualization. After they get comfortable with data manipulation and transformation, they gain familiarity with statistical frameworks and methods designed to extract practical insights from data. Participants learn and implement common machine-learning techniques and develop and evaluate analytical solutions.

Toward the conclusion of the course, students work in groups on a final project and presentation, thereby (a) solidify their newly acquired analytical and programming skills and (b) practicing storytelling with data.

Participants should expect a dynamic and interactive environment: hands-on exercises, teamwork, continuous in-class dialogue, demonstrations, and interactive presentations. The course features real-world applications of data analytics across industries and challenges students to think in terms of the business value of data and machine learning.


Back to the Course Guide

Specific course details such as topics, activities, hours, and instructors are subject to change at the discretion of the University. Not all instructors listed for a course teach all sections of that course.