Sumanta Basu is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. Broadly, his research interests are structure learning and the prediction of large systems from data, with a particular emphasis on developing learning algorithms for time series data. Professor Basu also collaborates with biological and social scientists on a wide range of problems, including genomics, large-scale metabolomics, and systemic risk monitoring in financial markets. His research is supported by multiple awards from the National Science Foundation and the National Institutes of Health. At Cornell, Professor Basu teaches “Introductory Statistics” for graduate students outside the Statistics Department and “Computational Statistics” for Statistics Ph.D. students. He also serves as a faculty consultant at Cornell Statistical Consulting Unit, which assists the broader Cornell community with various aspects of analyzing empirical research. Professor Basu received his Ph.D. from the University of Michigan and was a postdoctoral scholar at the University of California, Berkeley, and Lawrence Berkeley National Laboratory. Before he received his Ph.D, Professor Basu was a business analyst, working with large retail companies on the design and data analysis of their promotional campaigns.
Overview
How It Works
Format
All Online
Time Commitment
2 months with 6 to 8 hours of study per week
Learn From Top Minds
Courses are developed by Cornell faculty.
Power Your Career
Gain today’s most in-demand skills to stand apart.
Flexibility Fits Your Life
Learn on your schedule without stepping out of your job.
Small-class Experience
Participate in facilitated discussions and live sessions with industry peers.
Real-world Projects
Apply learnings and insights to your work to make an impact right away.
Personalized Feedback
Enjoy meaningful feedback on assignments from expert facilitators.
Format
All Online
Time Commitment
2 months with 6 to 8 hours of study per week
Learn From Top Minds
Courses are developed by Cornell faculty.
Power Your Career
Gain today’s most in-demand skills to stand apart.
Flexibility Fits Your Life
Learn on your schedule without stepping out of your job.
Small-class Experience
Participate in facilitated discussions and live sessions with industry peers.
Real-world Projects
Apply learnings and insights to your work to make an impact right away.
Personalized Feedback
Enjoy meaningful feedback on assignments from expert facilitators.
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Faculty Authors
Sumanta Basu
Assistant Professor
Cornell Bowers Computing and Information Science
Assistant Professor, Cornell Bowers CIS; Shayegani Bruno Family Faculty Fellow, Cornell Department of Computational Biology
Sreyoshi Das
Assistant Professor of Practice
Cornell Bowers Computing and Information Science
Assistant Professor of Practice, Department of Statistics and Data Science, Cornell Bowers Computing and Information Science
Sreyoshi Das designs and offers courses on the applications of statistics and data science in the industry, with specific emphasis in the areas of economics and finance. Her courses aim to integrate academic training with hands-on work experience.
Before joining Cornell in 2022, Professor Das worked in economic consulting, where she developed a variety of quantitative and qualitative analyses to support testifying experts, client attorneys, government agencies, and corporations. In 2017, Professor Das received her Ph.D. in Economics from the University of Michigan, where she conducted research on banking and systemic risk, financial markets in emerging economies, and behavioral macroeconomics.
Key Course Takeaways
- Clean and preprocess the textual data contained within a set of documents in preparation for sentiment analysis
- Summarize and visualize the distribution of words within a single document (univariate) and across multiple documents (multivariate)
- Compare word distributions across documents and over time
- Use three different sentiment analysis lexicons (AFINN, Bing, and NRC) to quantify and interpret sentiments associated with words, sentences, and paragraphs
- Compare sentiments across documents and over time

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