Big Data, Machine Learning, and their Real World Applications

Level:
Open to students entering grades 11 or 12 or freshman year of college in the fall
Session:
I - June 29–July 17, 2020
II - July 21–August 7, 2020
Days & Time:
Monday–Friday, 9:10 –11:00 a.m. and 1:10–3:00 p.m.
Status:
New
Teacher(s):
Elena Dubova
Prerequisites:

Algebra 1, Algebra 2, and Geometry. Some background with statistics is recommended but not required.

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.

Teacher(s)

Elena Dubova

Elena Dubova built her career at Microsoft working with enterprise businesses across different industries – retail, transportation, and professional services. She held leadership positions across a number of business departments; operated in various functions, including sales, marketing, strategy, and operations; and developed and executed transformational projects from bringing to success new business verticals to partner ecosystem transformation. Elena holds an M.S. in applied analytics from Columbia University and M.A.’s in economics and international relations from Ivanovo State University. For several years she was a board member and director for the Model United Nations, an educational program that provides students with opportunities to find solutions for real-world issues. Elena is currently a faculty member at Columbia University’s School of Professional Studies.

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.