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Learn Data Analysis From Scratch

Step By Step Learn Data Analysis
  • 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

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

What Will I Get ?

  • 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

Requirements

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

79 Lectures

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