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Learn Data Analysis From Scratch

person icon Mukesh Ranjan

3.9

Learn Data Analysis From Scratch

Step By Step Learn Data Analysis

updated on icon Updated on Jun, 2024

language icon Language - English

person icon Mukesh Ranjan

English [CC]

category icon Business,Data Analytics

Lectures -80

Duration -11 hours

3.9

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Course Description

In this course you will learn about Data Analysis in a step by step manner. This course is divided into 4 parts. Following are the course Structure

LEARN DATA ANALYSIS FROM SCRATCH 

   Part I : Tools For Data Analysis

      Python Refresher

  •  01 Course Pre-Requisite
    •   Learn Coding From Scratch With Python3
  •  02 Ipython Interpreter
  •  03 Jupyter Notebook
    • Running Jupyter Notebook
    •  Object introspection
    • %Run Command
    •  %load Command
    •   Executing Code from Clipboard
    •  Shortcut of Jupyter Notebook
    •  Magic Command
    •   Matplotlib Integration
  • 04 Python Refresher - Basic DataTypes
  • 05 Python Refresher - Collection Types - Lists
  • 06 Python Refresher - Collection Types - Dictionaries
  • 07 Python Refresher - Collection Types - Sets
  • 08 Python Refresher - Collection Types - Tuples
  •  09 Python Refresher - Functions
  • 10 Python Refresher - Classes And Objects

      Numpy Core Concept For Data Analysis

  • Step 1 : Concept : Numpy Introduction
    •  What is Numpy?
    • Why Use Numpy?
  • Step 2 : Concept : Arrays Revisited
    •  Types Of Arrays
  • Step 3 : Lab : Ways to Create Arrays
    • 1. Create Arrays Using Python List
    • 2. Using Numpy's Methods 
  • Step 4 : Concept + Lab : Numpy Array Internals
    • Dimensions
    • Shape
    • Strides
  • Step 5 : Concept + Lab : Data Types and Casting
  • Step 6 : Concept + Lab : Slicing And Indexing
    • 1. Understand Slicing and Indexing 1-D Array
    • 2. Understand Slicing and Indexing Multidimensional Array
  • Step 7 : Concept + Lab : Array Operations
    • 1. Common Operations On Arrays
    • 2. Commonly Used Functions for Numpy Array Operations
  • Step 8 : Concept + Lab : Broadcasting 
    • Array Broadcasting Principle
    • Understand Usage of Broadcasting
  • Step 9 : Concept + Lab : Understand Vectorization 

      Pandas Core Concept For Data Analysis

  • Step 1 : What is Pandas
  • Step 2 : DataFrames
  •  Step 3 :  DataFrames Basics
  • Step 4 : Handling Missing Data
  •  Step 5 : GroupBy
  •  Step 6 : Aggregation
  • Step 7 : Transform
  •  Step 8 : Window Functions
  • Step 9 : Filter
  •  Step 10 : Join Merge And Concat
  • Step 11 : Apply Method
  •  Step 12 :  DataFrame Reshape
  • Step 13 :  Calculate Frequency Distribution

   Part II : Data Analysis Core Concepts

  • What is Data
  •  What is DataSet      
  • Types of Variables   
    • Types of Data Types    
    • Why Data Types are important?
  •  How do you collect Information for Different Data Types
    • For Nominal Data Type
    • Ordinal Data
    • Continuous Data
  • Descriptive Statistics Concepts
    • Types Of Statistics
      • Descriptive statistics
      •  Inferential Statistics
    • What it is?       
    • Concept 1 :  Understand Normal Distribution
    • Concept 2 : Central Tendency
    • Concept 3 : Measures of Variability
      • Range
      • Interquartile Range(IQR)    
    • Concept 4 : Variance and Standard Deviation   
    • Concept 5 : Z-score or Standardized Score
    • Concept 6 : Modality    
    • Concept 7 : Skewness  
    • Concept 8 : Kurtosis
      •  How  it look like            
      • Mesokurtic
      • platykurtic
      •  Leptokurtic 

   Part III : Tools For Data Visualization

  • Matplotlib Introduction
  •  Matplotlib Architecture
  • Seaborn Plot Overview
  • Parameters Of Plot
  • Types Of Plot By Purpose
    • 1. Correlation
      •  What It Is?
        • Type Of Graphs In Correlation Category
        • Scatter plot
        • Steps To Draw this graph
        • Step 1: Prepare Data
        • Step 2 : Plot By Each Category
        • Step 3 : Decorate the plot
        • Scatter plot with line of best fit
      •  When To Use
        •  Counts Plot           
        • Marginal Boxplot
        •  Correlogram          
        •   Pairwise Plot                
    •  2. Deviation
      • Diverging Bars             
      •   Diverging Dot Plot      
    • 3. Ranking
      • Ordered Bar Chart     
      • Dot Plot             
    •  4. Distribution
      •  Histogram for Continuous Variable   
      •  Histogram for Categorical Variable         
      • Density Curves with Histogram 
      •  Box Plot               
      • Dot + Box Plot        
      • Categorical Plots         
    • 5. Composition
      •  Pie Chart
      • Treemap
      •  Bar Chart      
    • 6. Change
      • Time Series Plot
      •  Time Series Decomposition Plot     

   Part IV : Step By Step Exploratory Data Analysis and Data Preparation Workflow With Project

  • What is Exploratory Data Analysis (EDA)?
  • Value of Exploratory Data Analysis
  • Steps of Data Exploration and Preparation
    • Step 1 :  Variable Identification
    • Step 2 :  Univariate Analysis
    •  Step 3 :  Bi-variate Analysis
    •  Step 4 :  Missing values treatment
    • Step 5 :  Outlier Detection and Treatment
      • What is an outlier?
      •  What are the types of outliers ?
      • What are the causes of outliers ?
      • What is the impact of outliers on dataset ?
      • How to detect outlier ?
      • How to remove outlier ?
    • Step 6 :  Variable transformation
    • Step 7 :  Variable creation

Goals

  • Python Important Concept For Data Analysis
  • Numpy Concept For Data Analysis
  • Python Pandas For Data Analysis
  • Matplot lib for Data Visualization in Data Analysis
  • Exploratory Data Analysis Workflow

Prerequisites

  • A computer installed with Windows/Linux /OS X
  • Internet Connection
Learn Data Analysis From Scratch

Curriculum

Check out the detailed breakdown of what’s inside the course

PART I : TOOLS FOR DATA ANALYSIS
33 Lectures
  • play icon Course Introduction 12:06 12:06
  • play icon Course Pre-requisite 04:28 04:28
  • play icon Ipython Interpreter 06:15 06:15
  • play icon Jupyter Notebook 12:24 12:24
  • play icon Python Refresher - Basic DataTypes 13:33 13:33
  • play icon Python Refresher - Collection Types - Lists 15:18 15:18
  • play icon Python Refresher - Collection Types - Dictionaries 06:23 06:23
  • play icon Python Refresher - Collection Types - Sets 06:35 06:35
  • play icon Python Refresher - Collection Types - Tuples 07:31 07:31
  • play icon Python Refresher - Collection Types - Functions 13:57 13:57
  • play icon Python Refresher - Classes And Objects 12:43 12:43
  • play icon What Is Numpy And Why To Use Numpy 03:39 03:39
  • play icon Numpy - Array Revisited 14:55 14:55
  • play icon Numpy - Ways To Create Arrays In Numpy 18:05 18:05
  • play icon Numpy Array Internals 12:46 12:46
  • play icon Numpy - DataTypes And Casting 08:29 08:29
  • play icon Numpy - Slicing And Indexing Numpy Arrays 11:45 11:45
  • play icon Numpy Array Operations 10:39 10:39
  • play icon Numpy - Broadcasting 06:50 06:50
  • play icon Numpy - Vectorization 06:29 06:29
  • play icon What is Pandas 02:56 02:56
  • play icon Pandas - Creating DataFrame in Pandas 09:14 09:14
  • play icon Pandas - DataFrames Basics 17:12 17:12
  • play icon Pandas - Handling Missing Data 14:00 14:00
  • play icon Pandas - GroupBy 14:28 14:28
  • play icon Pandas - Aggregation 05:45 05:45
  • play icon Pandas - Transform 08:53 08:53
  • play icon Pandas - Window Functions 08:32 08:32
  • play icon Pandas - Filter 03:58 03:58
  • play icon Pandas - Join Merge And Concat 15:57 15:57
  • play icon Pandas - Apply Method 03:54 03:54
  • play icon Pandas - DataFrame Reshape 06:09 06:09
  • play icon Pandas - Calculating Frequency Distribution 02:54 02:54
PART II - DATA ANALYSIS CORE CONCEPTS
10 Lectures
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PART III - TOOLS FOR DATA VISUALIZATION
24 Lectures
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PART IV : STEP BY STEP EXPLORATORY DATA ANALYSIS
12 Lectures
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Instructor Details

Mukesh Ranjan

Mukesh Ranjan


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