Tutorialspoint

Learn Data Analysis From Scratch

Step By Step Learn Data Analysis

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
Show More

Curriculum

  • Course Introduction
    12:06
    Preview
  • Course Pre-requisite
    04:28
    Preview
  • Ipython Interpreter
    06:15
    Preview
  • Jupyter Notebook
    12:24
  • Python Refresher - Basic DataTypes
    13:33
  • Python Refresher - Collection Types - Lists
    15:18
  • Python Refresher - Collection Types - Dictionaries
    06:23
  • Python Refresher - Collection Types - Sets
    06:35
  • Python Refresher - Collection Types - Tuples
    07:31
  • Python Refresher - Collection Types - Functions
    13:57
  • Python Refresher - Classes And Objects
    12:43
  • What Is Numpy And Why To Use Numpy
    03:39
  • Numpy - Array Revisited
    14:55
  • Numpy - Ways To Create Arrays In Numpy
    18:05
  • Numpy Array Internals
    12:46
  • Numpy - DataTypes And Casting
    08:29
  • Numpy - Slicing And Indexing Numpy Arrays
    11:45
  • Numpy Array Operations
    10:39
  • Numpy - Broadcasting
    06:50
  • Numpy - Vectorization
    06:29
  • What is Pandas
    02:56
  • Pandas - Creating DataFrame in Pandas
    09:14
  • Pandas - DataFrames Basics
    17:12
  • Pandas - Handling Missing Data
    14:00
  • Pandas - GroupBy
    14:28
  • Pandas - Aggregation
    05:45
  • Pandas - Transform
    08:53
  • Pandas - Window Functions
    08:32
  • Pandas - Filter
    03:58
  • Pandas - Join Merge And Concat
    15:57
  • Pandas - Apply Method
    03:54
  • Pandas - DataFrame Reshape
    06:09
  • Pandas - Calculating Frequency Distribution
    02:54
Tutorialspoint
Tutorialspoint
Tutorialspoint
Feedbacks
4.7
Course Rating
78%
11%
11%
0%
0%

    Feedbacks (9)

  • Manivannan M
    Manivannan M

  • Yaminiprasad Kollipara
    Yaminiprasad Kollipara

  • Vipin Ramachandran
    Vipin Ramachandran

  • Jitendra Acchelal Yadav
    Jitendra Acchelal Yadav

  • Sakshi Lad
    Sakshi Lad

  • Enoch Onwuka
    Enoch Onwuka

  • Pranay Pawar
    Pranay Pawar

  • NAGANANDA S,B
    NAGANANDA S,B

  • Shailesh Sanjeeva Billava
    Shailesh Sanjeeva Billava

Learn Data Analysis From Scratch
This Course Includes
  • 11 hours
  • 80 Lectures
  • Completion Certificate Sample Certificate
  • Lifetime Access Yes
  • Language English
  • 30-Days Money Back Guarantee

Sample Certificate

sample certificate

Use your certification to make a career change or to advance in your current career. Salaries are among the highest in the world.

We have 30 Million registered users and counting who have advanced their careers with us.

X

Sample Certificate

Talk to us

1800-202-0515