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
P
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
