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.
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Overview and Courses
In today's AI-driven landscape, processing and analyzing textual data is increasingly critical for understanding customer sentiment, market trends, and other key business insights. Natural language processing (NLP) techniques have become essential tools for transforming raw text into meaningful data that underpins many of today’s AI applications.
This certificate program is designed to equip you with foundational skills in NLP, with a focus on text preprocessing, summarization, visualization, and sentiment analysis. In the first course, you will clean and manipulate text data using regular expressions, preprocess complex textual information, and address common challenges in messy datasets. You will also have the opportunity to explore advanced text preprocessing techniques such as stemming and tokenization, which are essential for preparing text for further analysis. In the second course, you will develop the ability to summarize and visualize text distributions across documents, leveraging tools like word clouds and document-term matrices to uncover patterns and trends. Finally, the third course introduces you to sentiment analysis, where you will quantify and interpret emotions in text and compare sentiment across documents and over time.
By the end of this program, you will have the practical knowledge needed to preprocess and analyze textual data, giving you a valuable edge in data science, AI engineering, or any field that requires a deep understanding of textual information.
To succeed in this program, you should have a foundation in R programming. If you do not have this experience, start with the Data Science Essentials certificate program.
The courses in this certificate program are required to be completed in the order that they appear.
Course list
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.
- Jun 24, 2026
- Sep 16, 2026
- Dec 9, 2026
Summarizing and visualizing text data is a key skill for professionals looking to uncover meaningful insights from large volumes of information. In this course, you will master the tools and techniques to condense and display text data, making complex patterns easier to interpret.
Starting with the tidytext package in R, you will tokenize unstructured text data and convert it into structured data for analysis. You will then summarize word distributions within individual documents and bring them to life with visualizations like word clouds. As you progress, you will explore advanced techniques for summarizing and comparing text across multiple documents, using tools such as document-feature matrices.
By the end of the course, you will have the skills to compare word usage across texts and track how language patterns evolve over time, helping you reveal deeper trends in your data.
You are required to have completed the following course or have equivalent experience before taking this course:
- Mastering NLP Fundamentals
- Jul 8, 2026
- Sep 30, 2026
- Dec 23, 2026
In today's data-driven world, being able to quantify and analyze sentiment in text is a powerful skill for understanding customer feedback, social media trends, and more. This course gives you the expertise to transform text into meaningful sentiment scores using key libraries like AFINN, Bing, and NRC.
You will begin by working with these sentiment analysis tools to categorize and quantify emotional tones in documents. From there, you will calculate and visualize sentiment scores using tools like line plots, bar charts, and word clouds. Finally, you will compare sentiment across multiple documents and track changes over time.
By the end of the course, you will be ready to interpret and act on sentiment trends in real-world applications, offering valuable insights for business strategies, customer relations, and market analysis.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Mastering NLP Fundamentals
- Exploring Summarization and Visualization
- Jul 22, 2026
- Oct 14, 2026
eCornell Online Workshops are live, interactive 3-hour learning experiences led by Cornell faculty experts. These premium short-format sessions focus on AI topics and are designed for busy professionals who want to gain immediately applicable skills and strategic perspectives. Workshops include faculty presentations, breakout discussions, and guided hands-on practice.
The AI Workshops All-Access Pass provides you with unlimited participation for 6 months from your date of purchase. Whether you choose to attend one workshop per month, or several per week, the All-Access Pass will allow you to customize your AI journey and stay on top of the latest AI trends.
Workshops cover a range of cutting-edge AI topics applicable across industries, hosted by Cornell faculty at the forefront of their fields. Whether you are just getting started with AI, seeking to build your AI skillset, or exploring advanced applications of AI, Workshops will provide you with an action-oriented learning experience for immediate application in your career. Sample Workshops include:
- Work Smarter with AI Agents: Individual and Team Effectiveness
- Leading AI Transformation: Bigger Than You Imagine, Harder Than You Expect
- Using AI at Work: Practical Choices and Better Results
- Search & Discoverability in the Era of AI
- Don't Just Prompt AI - Govern it
- AI-Powered Product Manager
- Leverage AI and Human Connection to Lead through Uncertainty
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How It Works
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Faculty 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.
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
What You'll Earn
- Text Analysis Certificate from Cornell’s Ann S. Bowers College of Computing and Information Science
- 48 Professional Development Hours (4.8 CEUs)
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Who Should Enroll
- Data scientists
- Computer scientists
- Analysts
- User behavior and UX teams
- Researchers
- Social scientists
{Anytime, anywhere.}
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Text Analysis
| Select Payment Method | Cost |
|---|---|
| $3,750 | |