Big Data, Machine Learning, and their Real World Applications

Modality:
On-campus
Session:
I. June 27–July 15, 2022 (Course Filled)
II. July 19–August 5, 2022 (Course Filled)
Day & Time:
Monday–Friday, 9:10–11:00 a.m. and 1:10–3:00 p.m.
Instructor(s):
Faith Bradley, Harrison Groll
Prerequisites:
Algebra 1 and geometry. Some background with statistics and with computer programming is recommended but 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 R, a software environment for statistical computing and graphics. Much of the in-class work involves working with R. 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.

Participants are required to bring Mac or PC laptops.

Registration Guidance & Call Number(s)


Please note, this course may have multiple classes being offered in a particular session. Students should only register for one class and with one call number.

To view detailed information on a particular offering, click on the call number to be directed to the Directory of Classes catalogue.

Session 1 Classes

  • BIGD0101 (001) | Call Number: 10902 - class is full
  • BIGD0101 (002) | Call Number: 10903 - class is full

Session 2 Classes

  • BIGD0101 (003) | Call Number: 10904 - class is full
  • BIGD0101 (004) | Call Number: 10905 - class is full

Further guidance on the registration process can be found here.


Instructor(s)

Faith Bradley

Dr. Faith Bradley holds a Ph.D. in Public Policy / Policy Management. She has researched big data policy development in both public and private sectors, including serving as a consultant to the U.S. Department of State in conducting wide-area statistical and data assessments. Dr. Bradley has designed and taught master-level courses in Machine Learning Algorithm Analysis and Data Mining for George Washington University and master-level courses for Data Visualization for Business Intelligence for NYU Tandon School of Engineering. She has also taught courses in Machine Learning I: Algorithm Analysis, Analytics I: Principal & Applications Analytical Methods II, Data Mining, Ethics of Data Science, Modeling, Simulation & Game Theory.

Harrison Groll

Harrison Groll is a Software Engineer who focuses on AI and ML Systems. Harrison has worked previously at Visa, where he was a member of the Visa Research AI Solution Engineering Team. He holds a bachelor's degree from the University of Miami, where he majored in computer science and business, and a master's degree in computer science with a concentration in machine learning from Columbia University. Harrison has a passion for teaching tech to others and worked as a volunteer computer science teacher while in college. He is a certified tutor through the College Reading & Learning Association and has facilitated many college courses through the role of head teaching assistant.

Back to the Course Guide

Specific course detail such as 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.