Course curriculum

  • 1

    DS_E-Learning Content

    • Chapter 1 - Data Science - Introduction to Data Science Part - 1

    • Introduction to Data Science Part - 2

    • Data Science Pipeline

    • Data Science Career

    • Chapter 2 - Python - Introduction to Python

    • Software Installation

    • First Program

    • Comments

    • Data Types Part - 1

    • Data Types Part - 2

    • Operators Part - 1

    • Operators Part - 2

    • Variables Part - 1

    • Variables Part - 2

    • Syntax

    • Working with Numbers

    • Working with Strings Part - 1

    • Working with Strings Part - 2

    • Date & Time

    • Conditional Statements Part - 1

    • Conditional Statements Part - 2

    • For Loop Part - 1

    • While Loop

    • For Loop Part - 2

    • Lists Tuples Sets Part - 1

    • Lists Tuples Sets Part - 2

    • Lists Tuples Sets Part - 3

    • Dictionaries Part - 1

    • Dictionaries Part - 2

    • Functions Part - 1

    • Functions Part - 2

    • Numpy Part - 1

    • Numpy Part - 2

    • Numpy Part - 3

    • MatPlotLib Part - 1

    • MatPlotLib Part - 2

    • Pandas Part - 1

    • Pandas Part - 2

    • Pandas Part - 3

    • Chapter 3 - Maths required for Data Science

    • Introduction to Statistics

    • Descriptive Statistics

    • Measure of Spread

    • Probability

    • Conditional Probability

    • Chapter 4 - Introduction to Machine Learning (ML)

    • Types of ML Algorithms

    • Steps in building ML Model

    • Linear Regression

    • Logistic Regression

    • KNN - K Nearest Neighbours

    • Naive Bayes

    • SVM - Support Vector Machines

    • Decision Trees

    • Clustering

    • HC - Hierarchical Clustering

    • K-means Clustering

    • DBSCAN

    • Dimensionality Reduction

    • Principle Component Analysis

    • Linear Discriminant Analysis

    • Recommendation Systems

    • Collaborative Filtering System

    • Content Based System

    • Hybrid Recommendation System

    • Difference between Supervised and Unsupervised

    • Semi-Supervised Learning

    • Reinforcement Learning Part - 1

    • Reinforcement Learning Part - 2

    • Introduction to TensorFlow and Keras

    • Data Mining

    • Natural Language Processing Part - 1

    • Natural Language Processing Part - 2
  • 2

    DS_Hierarchical Clustering from Personifwy_Project 1

    • Hierarchical Clustering - Theory

    • Hierarchical Clustering - Code

    • Hierarchical Clustering IPYNB File

    • Mall Customers CSV File

  • 3

    DS_Linear Discriminant Analysis from Personifwy_Project 2

    • Wine Classification using LDA

    • Wine Classification IPYNB File

    • Wine CSV File