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.
Course Overview
With the rapid growth of text data across industries, knowing how to clean and process it is key to extracting valuable insights. This course gives you hands-on experience with text preprocessing, the foundation of any natural language processing (NLP) workflow.
You will start the course by using regular expressions to identify and edit patterns in text before tackling tasks like converting text to lowercase, replacing characters, and removing unwanted elements. As you progress, you will handle more advanced tasks such as tokenizing text into words or n-grams and filtering out irrelevant stop words. Finally, you will clean messy text by standardizing variations and using techniques like stemming.
By the end of the course, you will be equipped to prepare large text datasets for deeper analysis, paving the way for sentiment analysis and other advanced NLP tasks.
Key Course Takeaways
- Use regular expressions to manipulate and search text
- Import text data into R and apply text preprocessing techniques
- Apply advanced preprocessing techniques to standardize complex and messy text
How It Works
Course Authors
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.
Who Should Enroll
- Data scientists
- Computer scientists
- Analysts
- User behavior and UX teams
- Researchers
- Social scientists
100% Online
cornell's Top Minds
career