Course curriculum

  • 1

    Course Introduction

    • Introduction to Course
  • 2

    Introduction to Data Science & Analytics

    • Introduction to Data Science 1

    • Types of Analytics

    • Basic Requirement for an Analyst
  • 3

    Python

    • Introduction to Python & Installation

    • Anaconda Installation

    • First Python Program

    • Input & Output

    • Strings

    • Lists

    • Tuples

    • Dictionary

    • Loops (nested, break & continue)

    • Loops (while & for)

    • Functions
  • 4

    Numpy

    • Introduction to NumPy

    • NumPy Array

    • NumPy Functions

    • Array Indexing
  • 5

    Pandas

    • Introduction to Pandas

    • Pandas Series

    • Pandas Dataframes
  • 6

    Visualization using Matplotlib

    • Introduction to Visualization

    • Matplotlib Part 1 (line plot)

    • Matplotlib Part 2 (scatter, bar & pie plots)

    • Sub plot and hist.ENCODING
  • 7

    Visualization using Seaborn

    • Introduction to Seaborn & Functions

    • Seaborn Bar Plot

    • Seaborn Countplot
  • 8

    Data Analysis Case Studies

    • Projecting Appraisals by Evaluating Performance

    • Wine Quality Exploratory Data Analysis
  • 9

    Welcome to machine Learning

    • Introduction to machine learning ,History & Applications
  • 10

    Supervised Ml-(Regression Algorithms)

    • supervised learning

    • Introduction to regression(Linear -Regression)

    • Simple Linear regression Without Library

    • perfomance analysis

    • Linear regression sklearn
  • 11

    Supervised-Classification Algorithms

    • logistic regression sklearn
  • 12

    Aws Machine Learning Certification

    • Recording__10

    • Recording__11

    • Recording__15

    • Recording__17

    • Recording__18

    • Recording__20

    • Recording__21

    • Recording__23

    • Recording__24

    • Recording__25