Master 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 is the Course Structure:

   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 does 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 a 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 the dataset?
    • How to detect outliers?
    • How to remove outliers?
  • Step 6:  Variable transformation
  • Step 7:  Variable creation