Mathematical Foundations of Data Science

UG Course Scheme

1. M.Tech (Artificial Intelligence) R2019 [ Scheme ]

2. MEAIC103 Mathematical Foundations of Data Science [ Syllabus ]

3. Question Bank Booklet QBB - Jan 2024 [ PDF ] [ SpreaSpreadsheet ]

Course Outcomes 

Upon completion of the course, the learners will be able to:

1. Understand the importance of linear algebra, statistics and probability from data science perspective.

2. Understand the elements of structured data and data distribution for binary as well as categorical data.

3. Apply the knowledge of sampling and distribution algorithms to evaluate the real distribution of sampling data.

4. Apply the knowledge of significance testing, use of null value hypothesis to outline the conditions for a particular test.

5. Evaluate and analyze the results of confusion matrix.

6. Apply optimization techniques for improvising performance.

Course Materials (PPTs/PDFs/Handouts)

• • Module 1: Basics of Data Science

    • Introduction [ PDF ]

  • Module 2: Linear Algebra

    • Linear Algebra [ PDF ]

    • Matrices, Eigenvalues & Vectors, Orthogonality [ PDF ]

    • Eigenvalues and Eigenvectors [ PDF ]

    • Eigenvalues for PCA [ PDF ]

  • Module 3: Exploratory Data Analysis

    • Introduction [ PDF ]

    • Exploratory Data Analysis using R [ PDF ]

    • Statistical Description (Parameters) of Data [ PDF ]

    • VStatistics - Moments of a Distribution [ PDF ]

  • Module 4: Data and Sampling Distributions

    • Data and Sampling Distributions [ PDF ]

    • Population (Probability) Distributions [ PDF ]

  • Module 5: Statistics and Significance Testing

    • Hypothesis Testing [ PDF ]

    • Probability Distribution and Hypothesis Testing [ PDF ]

    • Hypothesis Testing (Parametric) [ PDF ]

    • Hypothesis Testing (Non-Parametric) [ PDF ]

  • 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 ]

      • Optimization Techniques: Genetic Algorithm [ PDF ]

      • ROC Curve Example [ 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 ]