The global business landscape is increasingly data-driven. Valuable data is generated in every facet of business, and data science techniques help companies find pragmatic solutions to inform strategy, execution, and organizational leadership in finance, management, and marketing.
Data scientists recognize patterns and form insights, contribute to risk management, build models, create viable products, and present and communicate financial data to effectively support company executives in sales, product development, planning, and customer acquisition. Causal techniques, including correlation and multiple regression analysis, are used to prepare business forecasts and long-range plans. Financial analytics are used to audit and optimize financial strategies based on both company and industry data.
This nexus of data science and business analytics and Columbia’s position in the global business hub and ecosystem that is New York City places DSI in a unique position to leverage data science to improve business processes.
DSI researchers study revenue maximization problems and combine labor market data with data science methods to identify factors and environments that shape gender and racial inequality.
Teams of M.S. in data science students have developed and evaluated novel spatio-temporal trajectories clustering methods used for traffic planning, inventory optimization, and understanding movements; used public data, anomaly detection, and natural language processing to create a dashboard and pip-installable package to help executives learn the full news history of a potential client; and predicted the Moody’s rating changes.
Collaboratory courses also prepare Columbia Business School students to succeed in an increasingly data-intensive world with a curriculum focused on programming, databases, and data analytics and the latest techniques for gathering, managing, and interpreting data.
Financial and Business Analytics
Hardeep Johar, Industrial Engineering and Operations Research
With increasing use of asset tracking, large databases of spatio-temporal trajectories (STT) are used for traffic planning, inventory optimization, and understanding movements. This team developed and evaluated novel STT clustering methods with promising results. The key novelty arises from different similarity measures (DTW, Fréchet Distance, Edit Distance, LCSS) and representations (e.g. autoencoders) for STT.
The “Know Your Client” requirement sets a broad mandate that financial institutions must execute adequate background checks on potential clients. This team used public data, anomaly detection, and natural language processing to create a dashboard and pip-installable package to help executives learn the full news history of a potential client.
The announcements of Moody’s credit rating change have impact on the financial market. Predicting them in advance enables more informed investment decisions. The team was interested in predicting the next Moody’s rating change to generate alpha. They recommended to get more data (i.e. Moody’s ratings for more companies) and perform time series classification with a deep model.
Kira Goldner (Ph.D. in computer science and engineering, University of Washington) does research on algorithmic mechanism design: designing algorithms that guarantee that the designer’s objectives are achieved, even when the data they run on is produced by individuals acting in their own self-interest. Her work includes studying revenue maximization problems, and understanding more behavioral and informational complex models.