II - July 21–August 7, 2020
Algebra 1, Algebra 2, and Geometry. Some background with statistics is recommended but not required.
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.
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.
Rajeev Nair is Vice President for Predictive Modeling and AI/Decision Sciences for CreditOne Bank. Prior to his current role, he was associated with the private equity Comcraft Group. He has served as an advisor to NE Big Data Hub at Columbia University, a think tank created by the US Government, New York City, and Harvard/MIT/Penn/Columbia to apply AI/NLP/Machine Learning technologies to solve business problems in finance and healthcare. Rajeev earned his MBA from Columbia Business School. He has completed executive education from MIT on artificial intelligence (AI) for business strategy and holds a B.Tech. from Indian Institute of Technology (IIT), Kharagpur.
Orthi Rabbane is a data scientist and programming instructor living and working in Washington, DC. She has consulted for startups, investment banks, and corporations, enabling them to leverage their data so as to grow their business, and has worked for the Federal Reserve Board. Orthi holds a masters in applied economics from Johns Hopkins University and undergraduate degrees in economics and mathematical sciences (specialization in statistics, probability, and data analysis) from the University of Texas at Austin.