BCSE206L - Foundations of Data Science
- Instructor
- Dr. Bhargavi R
- Slot And Venue Details
| Slot : B1 + TB1 | Venue: AB3 Block 108 |
| Slot : B2 + TB2 | Venue: AB3 Block 106 |
Course Overview
The "Foundations of Data Science" course provides the essential theoretical framework for data analysis and predictive modeling. It establishes the mathematical language by differentiating qualitative and quantitative data types and covering core descriptive statistics. The curriculum delves into Basic Statistics, focusing on types of statistics, sampling, correlation etc. The basic analysis using SQL is covered in the course. The course includes foundational concepts for the toolsets commonly used, such as Tableau for data visualization principles and Octave for numerical computation and understanding matrix operations. Key concepts in Machine Learning are introduced, and the mechanics of Decision Trees through metrics like Information Gain. The course covers the theoretical necessity of data preparation, including methods for handling missing data and the rationale behind various Feature Selection techniques. More details on the topics covered can be obtained from the syllabus.
Syllabus
You can find the syllabus of this course here
Textbooks
Following are the text books for reference -
- Sanjeev Wagh, Manisha Bhende, Anuradha Thakare, ‘Fundamentals of Data Science, CRC Press, 1st Edition, 2022.
Tentative Schedule
Date  |
Lecture  |
Readings  |
Announcements  |
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| Welcome to Foundations of Data Science! | |||
| Thu, 4th Dec | Lecture 1: Meet and Greet, Introduction to Data Science applications [Slides] |
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| Mon, 8th Dec | Lecture 2: Data Science Introduction continued with different application, Need for DS etc. [Slides] |
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| Tue, 9th Dec | Lecture 3: Data Science Process [Slides] |
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| Thu, 11th Dec | Lecture 4: Data Science Process [Slides] |
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| Mon, 15th Dec | Lecture 5: BI, Data Analysis and Data Analytics, Core componenets of BI [Slides] |
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| Tue, 16th Dec | Lecture 6: Prerequisites for a Data Scientist, Tools and Skills required [Slides] |
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| Thu, 18th Dec | Lecture 7: Data Types, Variable types, Descriptive and inferential statistics, Sampling techniques [Slides] |
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| Welcome New Year 2026! | |||
| Mon, 5th Jan 2026 | Lecture 8: Data Analytics life cycle, Discovery [Slides] |
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| Tue, 6th Jan 2026 | Lecture 9: Dat Preprocessing, handling missing data [Slides] |
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| Mon, 12th Jan 2026 | Lecture 10: Dat Preprocessing, Transformation, etc. [Slides] |
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| Mon, 19th Jan 2026 | Lecture 11: Dat Preprocessing, Feature selection [Slides] |
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| Tue, 20th Jan 2026 | Lecture 12: Model Evaluation,Classification and Regression metrics [Slides] |
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| Thu, 22nd Jan 2026 | Lecture 13: Discussion [Slides] |
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| Mon, 2nd Feb 2026 | Lecture 14: Databases for Data Science - Introduction, pandas for SQL [Slides] |
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| Tue, 3rd Feb 2026 | Lecture 15: Data Munging with SQL [Slides] |
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| Thu, 5th Feb 2026 | Lecture 16: Data Munging, Filtering [Slides] |
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| Mon, 9th Feb 2026 | Lecture 17: Joins [Slides] |
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| Tue, 10th Feb 2026 | Lecture 18: Window functions and ordered data [Slides] |
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| Thu, 12th Feb 2026 | Lecture 19: Window functions and ordered data [Slides] |
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| Mon, 16th Feb 2026 | Lecture 20: Discussion [Slides] |
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| Tue, 16th Feb 2026 | Lecture 21: Aggredation [Slides] |
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| Thu, 17th Feb 2026 | Lecture 22: Preparing data for Analytics tool, NoSQL [Slides] |
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| Mon, 23rd Feb 2026 | Lecture 23: Data analytics on Text - Introduction, Information Retrieval [Slides] |
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| Tue, 24th Feb 2026 | Lecture 24: Term document incidance matrix, Inverted index, Boolean retrieval algorithm, Evaluation metrics [Slides] |
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| Thu, 26th Feb 2026 | Lecture 25: Text mining, stages, preprocessing, tokenization, stemming/lemmetization, text transformation [Slides] |
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| Mon, 2nd March 2026 | Lecture 26: POS taggging, parsing [Slides] |
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| Tue, 3rd March 2026 | Lecture 27: Multi nomial Naive Bayes [Slides] |
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| Thu, 5th March 2026 | Lecture 28: NLP [Slides] |
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| Mon, 9th March 2026 | Lecture 29: Practice Session SQL |
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| Tue, 10th March 2026 | Lecture 30: Practice Session - Text preprocessing |
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| Thu, 12th March 2026 | Lecture 31: Practice Session |
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| Mon, 23rd March 2026 | Lecture 32: Pandas [Pandas-Part1] |
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| Tue, 24th March 2026 | Lecture 33: Pandas [Pandas-Part2] |
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| Thu, 26th March 2026 | Lecture 34: Pandas [Pandas-Part2] |
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| Mon, 30th March 2026 | Lecture 35: Clustering - Kmeans[Slides] [KMeansClustering application] [KMeansClustering - Selecting the number of clusters] |
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| Tue, 31st March 2026 | Lecture 36: Hierarchical clustering[Slides] |
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