Learn Data Analysis From Scratch

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