Big Data, Machine Learning, and their Real World Applications

I. June 26–July 14, 2023
II. July 18–August 4, 2023
Day & Time:
Monday–Friday, 9:10–11:00 a.m. and 1:10–3:00 p.m.
Minwoo Song, Joshua Young, Marcela Mendoza, Kyle Dent
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 advance of data, 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. Students will 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 technology. Students will learn how to import, export, manipulate, transform, and visualize data; use statistical summaries; and run and evaluate machine learning models. Participants will learn and implement common machine-learning techniques and develop and evaluate analytical solutions.

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

Session 2 Classes

Further guidance on the registration process can be found here.


Minwoo Song

Minwoo Song is an accomplished educator and researcher in the field of economics and finance. He currently works as an adjunct professor at several universities, including New York University, Fordham University, and Baruch College, where he teaches a range of subjects including economics, finance, and statistics. His exceptional teaching skills have earned him high praise from both students and colleagues.

Minwoo Song has an impressive educational background, holding a bachelor's degree in electrical engineering and a master's degree in economics. He is currently a Ph.D. candidate in financial economics at the City University of New York, the Graduate Center. His research focuses on the application of machine learning techniques to financial markets. In particular, he has conducted research on the herd instinct in the digital currency market. In his paper, he utilized textual analysis to forecast cryptocurrency returns and constructed a novel data set using machine learning techniques. With this data, he developed a long/short strategy that generated significant profits. Overall, Minwoo Song's expertise in economics and finance, coupled with his innovative use of machine learning, has made him a respected and accomplished academic.

Joshua Young

Joshua Young is an Assistant Professor in the Otto H. York Department of Chemical and Materials Engineering at the New Jersey Institute of Technology. He received his PhD in Materials Science and Engineering at Drexel University and BS in Chemistry from Case Western Reserve University. His research focuses on the use of machine learning and other computational methods to design new materials for cutting edge applications such as batteries, water treatment, catalysis, and novel electronic; this involves the analysis of large databases of material structures and properties using advanced data science techniques. He has also been actively involved in designing, implementing, and teaching data science at all levels from elementary school to post-college, especially as it relates to solving engineering problems.

Marcela Mendoza

Marcela Mendoza is a researcher and educator in the field of Data Science. She is currently an Adjunct Associate Professor of Data Science at Beyond Campus Innovations as well as a Data Science tutor at Practicum. She received her PhD in Bioengineering (2018) and an MA in Applied Mathematics (2017) from the University of California, San Diego where her thesis focused on developing the theory and application of novel machine learning algorithms to biomedical data. She also received a BS in Biomedical Engineering (2012) from the University of Texas at Austin. Her experience in data science expands both academia and industry. She has postdoctoral studies in the field of genomics from Scripps Research Institute and the field of citizen science from El Colegio de la Frontera Sur with a focus on Machine Learning algorithms. She has also worked as a data scientist at several companies and continues to do consulting for industry.

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.