Mathematical Foundations of Data Science
UG Course Scheme
M.Tech (Artificial Intelligence) R2019 [ Scheme ]
MEAIC103 Mathematical Foundations of Data Science [ Syllabus ]
Question Bank Booklet QBB - Jan 2024 [ PDF ] [ SpreaSpreadsheet ]
Course Outcomes
Upon completion of the course, the learners will be able to:
Understand the importance of linear algebra, statistics and probability from data science perspective.
Understand the elements of structured data and data distribution for binary as well as categorical data.
Apply the knowledge of sampling and distribution algorithms to evaluate the real distribution of sampling data.
Apply the knowledge of significance testing, use of null value hypothesis to outline the conditions for a particular test.
Evaluate and analyze the results of confusion matrix.
Apply optimization techniques for improvising performance.
Course Materials (PPTs/PDFs/Handouts)
Module 1: Basics of Data Science
Introduction [ PDF ]
Module 2: Linear Algebra
Module 3: Exploratory Data Analysis
Module 4: Data and Sampling Distributions
Data and Sampling Distributions [ PDF ]
Population (Probability) Distributions [ PDF ]
Module 5: Statistics and Significance Testing
Module 6: Evaluation and Optimization
Confusion Matrix [ PDF ]
Confusion Matrix [ Confusion Matrix_Lab.ipynb ]
Confusion Matrix Example [ Spreadsheet.xls ]
Confusion Matrix Example [ PDF ]
Example: Multiclass Confusion Matrix [ PDF ]
Optimization [ PDF ]
Course Examination and Evaluation
2023-24
Internal Assessment #1: [ PDF ] [ Solution PDF ]
Internal Assessment #2: [ IA1 QP PDF ] [ IA1 Solution PDF ]
Assignment #1 : [ Assign1 Question PDF ] [ Assign1 Solution PDF ]
Assignment #2: [ Assign2 Question ] [ Assign2 Solution ]
Practice Quiz #1 [ Google Form ]
Theory Examination [ QP PDF ] [ TH QP Solution PDF ]
2022-23
Lab Experiments
Guidelines for writing Lab Experiments [ PDF ]
Module 1: Basics of Data Science
Lab 1: [ PDF ]
Lab 2: [ PDF ]
Lab 3: [ PDF ]
Module 2: Linear Algebra
Lab 4: [ PDF ]
Lab 5: [ PDF ]
Module 3: Exploratory Data Analysis
Lab 6: [ PDF ]
Lab 7: [ PDF ]
Module 4: Data and Sampling Distributions
Lab 8: [ Lab8.ipynb ]
Lab 9: [ Lab9.ipynb ]
Module 5: Statistics and Significance Testing
Lab 10:
Module 6: Evaluation and Optimization
Lab 11:
Lab Manual
Lab Manual [ PDF ]