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

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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