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3.9

# Learn Data Analysis From Scratch

Step By Step Learn Data Analysis

Updated on Jun, 2024

Language - English

English [CC]

Lectures -80

Duration -11 hours

3.9

30-days Money-Back Guarantee

Training 5 or more people ?

## Course 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
•   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
• 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

### Goals

• 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

### Prerequisites

• A computer installed with Windows/Linux /OS X
• Internet Connection

## Curriculum

Check out the detailed breakdown of whatâ€™s inside the course

PART I : TOOLS FOR DATA ANALYSIS
33 Lectures
• Course Introduction 12:06 12:06
• Course Pre-requisite 04:28 04:28
• Ipython Interpreter 06:15 06:15
• Jupyter Notebook 12:24 12:24
• Python Refresher - Basic DataTypes 13:33 13:33
• Python Refresher - Collection Types - Lists 15:18 15:18
• Python Refresher - Collection Types - Dictionaries 06:23 06:23
• Python Refresher - Collection Types - Sets 06:35 06:35
• Python Refresher - Collection Types - Tuples 07:31 07:31
• Python Refresher - Collection Types - Functions 13:57 13:57
• Python Refresher - Classes And Objects 12:43 12:43
• What Is Numpy And Why To Use Numpy 03:39 03:39
• Numpy - Array Revisited 14:55 14:55
• Numpy - Ways To Create Arrays In Numpy 18:05 18:05
• Numpy Array Internals 12:46 12:46
• Numpy - DataTypes And Casting 08:29 08:29
• Numpy - Slicing And Indexing Numpy Arrays 11:45 11:45
• Numpy Array Operations 10:39 10:39
• Numpy - Broadcasting 06:50 06:50
• Numpy - Vectorization 06:29 06:29
• What is Pandas 02:56 02:56
• Pandas - Creating DataFrame in Pandas 09:14 09:14
• Pandas - DataFrames Basics 17:12 17:12
• Pandas - Handling Missing Data 14:00 14:00
• Pandas - GroupBy 14:28 14:28
• Pandas - Aggregation 05:45 05:45
• Pandas - Transform 08:53 08:53
• Pandas - Window Functions 08:32 08:32
• Pandas - Filter 03:58 03:58
• Pandas - Join Merge And Concat 15:57 15:57
• Pandas - Apply Method 03:54 03:54
• Pandas - DataFrame Reshape 06:09 06:09
• Pandas - Calculating Frequency Distribution 02:54 02:54
PART II - DATA ANALYSIS CORE CONCEPTS
10 Lectures
PART III - TOOLS FOR DATA VISUALIZATION
24 Lectures
PART IV : STEP BY STEP EXPLORATORY DATA ANALYSIS
12 Lectures

Mukesh Ranjan