Overview

  • Key Learnings

    Learn essential mathematics and Python programming skills required to excel in the field of Data Science.

  • Capstone Projects

    Complete an end-to-end Capstone Project in which you will solve a real-world problem and engineer relevant features.

  • Job Guidance

    Become job ready with dedicated lectures on resume development, case studies, puzzles and one-on-one Mock Interviews.

Methodology

  • All classes will be one-to-one instructor-led live web sessions

  • Lecture materials and Jupyter notebooks will be shared with lectures on same day

  • Post completion of the course, you will receive a certificate of completion

  • This course will take 4 weeks to complete and end with a final assessment

Course curriculum

  • 1

    Data Science in Industrial Setup - Why, What, How

    • Data Science in Industrial Setup - Why, What, How

  • 2

    Python - Essential Toolkit for a Data Scientist

    • Setting Up the Work Environment

    • Installation Guide - Windows

    • Installation Guide - macOS

    • Installation Guide - Linux

    • Your First Jupyter Notebook

  • 3

    Fiddling Around with Variables

    • Exploring Data Types in Python

    • Strings

    • Boolean Variables

    • Reference Files (Jupyter Notebook & Data)

    • Practice Assignment

  • 4

    Basic Units of the Python Universe

    • Introduction to Lists

    • List Indexing & Slicing

    • List Functions & Methods

    • Tuples

    • Sets

    • Dictionary Objects

    • Indentation in Python

    • If elif else statements

    • For Loop

    • While Loop

    • Reference Files (Jupyter Notebook & Data)

    • Practice Assignment

  • 5

    Introduction to Methods & Functions

    • Methods

    • Functions

    • Scope

    • Reference Files (Jupyter Notebook & Data)

    • Practice Assignment

  • 6

    Writing Production Grade Code

    • What is Production Environment?

    • Object Oriented Programming

    • Methods

    • Error & Exception Handling

    • Real World Example

    • Reference Files (Jupyter Notebook & Data)

    • Practice Assignment

  • 7

    Facing off with Linear Algebra

    • Linear Algebra & Its Applications

    • Vectors

    • Matrices

    • Reference files (PPT)

    • Linear Algebra - Python Implementation

    • Practice Assignment

    • Additional Reading Material

  • 8

    Calculus

    • Introduction to Calculus

    • Reference files (PDF)

    • Additional Reading Material

  • 9

    Probability Theory

    • Concepts in Probability

    • Random Variables and its Types

    • Probability Mass Function

    • Probability Density Function

    • Additional Reading Material

  • 10

    Statistics - Measures of Central Tendency & Spread

    • Measures of Central Tendency

    • Measures of Spread

    • Symmetry and Skewness

    • Reference files(ppt)

    • Mean, Median, Mode, Variance & IQR - Python Implementation

    • Additional Reading Material

  • 11

    Statistics - Hanging in with Statistical Distributions

    • Discrete Statistical Distributions

    • Continuous Statistical Distributions

    • Additional Reading Material

  • 12

    Statistics - Covariance, Correlation & Chi-Squared

    • Explanatory vs Response Variable

    • Covariance & Correlation

    • Chi - Squared

    • Reference files (PPT & Chi - Squared table)

  • 13

    Numpy - Operations on Arrays

    • Why Numpy

    • List vs Array

    • Array Inspection

    • Placeholders

    • Array Manipulation

    • Basic Operations

    • Real World problem: Operations on Image

    • Reference Files (Jupyter Notebook & Data)

    • Practice Assignment

  • 14

    Pandas - Chug Data, Spit Frames

    • Pandas package - Introduction

    • Introduction to Series and Data Frame

    • Load csv, xlsx and json format

    • Saving file to location

    • Reference Files (Jupyter Notebook & Data)

  • 15

    Pandas - Operations on Dataframes

    • Data Frame Inspection

    • Indexing and Slicing

    • Manipulating Columns

    • Merging dataframes

    • Unique and missing values

    • Groupby

    • Reference Files (Jupyter Notebook & Data)

    • Practice Assignment

  • 16

    Introduction to Data Visualization

    • Matplotlib & Seaborn : Worth 1000 words

  • 17

    Matplotlib - Data Visualization

    • Line Plot

    • Bar Plot

    • Box Plot

    • Reference Files (Jupyter Notebook & Data)

  • 18

    Seaborn - Data Visualization

    • Histogram

    • Understanding Correlation

    • Correlation Heatmap

    • Two-way plots

    • Reference Files (Jupyter Notebook & Data)

  • 19

    Capstone Project

    • Defining the Problem Statement

    • Capstone Project

  • 20

    Solution - Capstone Project

    • Capstone Project - Solution

  • 21

    Final Assessment

    • Quiz