Learn Data Analysis From Scratch Using Python

Learn Data Analysis From Scratch Using Python, available at $39.99, has an average rating of 3.8, with 79 lectures, based on 12 reviews, and has 84 subscribers.

You will learn about Python Important Concepts 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 This course is ideal for individuals who are Beginners Python Developer who want to learn Data Analysis or Student who want to learn Numpy or Student who want to learn Pandas for Data Analysis or Student who want to learn Matplot lib package for data visualization or Student who want to learn Exploratory Data Analysis or Student who want to learn workflow of Data Analysis It is particularly useful for Beginners Python Developer who want to learn Data Analysis or Student who want to learn Numpy or Student who want to learn Pandas for Data Analysis or Student who want to learn Matplot lib package for data visualization or Student who want to learn Exploratory Data Analysis or Student who want to learn workflow of Data Analysis.

Enroll now: Learn Data Analysis From Scratch Using Python

Summary

Title: Learn Data Analysis From Scratch Using Python

Price: $39.99

Average Rating: 3.8

Number of Lectures: 79

Number of Published Lectures: 79

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: $174.99

Quality Status: approved

Status: Live

What You Will Learn

Python Important Concepts 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

Who Should Attend

Beginners Python Developer who want to learn Data Analysis

Student who want to learn Numpy

Student who want to learn Pandas for Data Analysis

Student who want to learn Matplot lib package for data visualization

Student who want to learn Exploratory Data Analysis

Student who want to learn workflow of Data Analysis

Target Audiences

Beginners Python Developer who want to learn Data Analysis

Student who want to learn Numpy

Student who want to learn Pandas for Data Analysis

Student who want to learn Matplot lib package for data visualization

Student who want to learn Exploratory Data Analysis

Student who want to learn workflow of Data Analysis

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

Course Curriculum

Chapter 1: PART I : TOOLS FOR DATA ANALYSIS

Lecture 1: Course Pre-requisite

Lecture 2: Ipython Interpreter

Lecture 3: Jupyter Notebook

Lecture 4: Python Refresher – Basic DataTypes

Lecture 5: Python Refresher – Collection Types – Lists

Lecture 6: Python Refresher – Collection Types – Dictionaries

Lecture 7: Python Refresher – Collection Types – Sets

Lecture 8: Python Refresher – Collection Types – Tuples

Lecture 9: Python Refresher – Functions

Lecture 10: Python Refresher – Classes And Objects

Lecture 11: Numpy – What Is Numpy And Why To Use Numpy

Lecture 12: Numpy – Array Revisited

Lecture 13: Numpy – Ways To Create Arrays In Numpy

Lecture 14: Numpy – Numpy Array Internal

Lecture 15: Numpy – DataTypes And Casting

Lecture 16: Numpy – Slicing And Indexing Numpy Arrays

Lecture 17: Numpy – Numpy Array Operations

Lecture 18: Numpy – Broadcasting Concept

Lecture 19: Numpy – Vectorization Concept

Lecture 20: Pandas – What is Pandas

Lecture 21: Pandas – Creating DataFrame in Pandas

Lecture 22: Pandas – DataFrames Basics

Lecture 23: Pandas – Handling Missing Data

Lecture 24: Pandas – GroupBy

Lecture 25: Pandas – Aggregation

Lecture 26: Pandas – Transform

Lecture 27: Pandas – Window Functions

Lecture 28: Pandas – Filter

Lecture 29: Pandas – Join Merge And Concat

Lecture 30: Pandas – Apply Method

Lecture 31: Pandas – DataFrame Reshape

Lecture 32: Pandas – Calculate Frequency Distribution

Chapter 2: Part II : Data Analysis Core Concepts

Lecture 1: Data Analysis Core Concepts Introduction

Lecture 2: What is Data

Lecture 3: All About DataSet

Lecture 4: Types Of Variables / DataTypes

Lecture 5: Descriptive Statistics Concepts – Types Of Statistics

Lecture 6: Understand Normal Distribution

Lecture 7: Central Tendency (Mean Median Mode)

Lecture 8: Measure Of Variablility

Lecture 9: Z-Score (Standardized Score)

Lecture 10: Modality – Skewness – Kurtosis

Chapter 3: PART III : Tools For Data Visualization

Lecture 1: Matplotlib Introduction

Lecture 2: Matplotlib Architecture

Lecture 3: Seaborn Plot Overview

Lecture 4: Parameters Of Plot

Lecture 5: Types Of Plot By Purpose – Introduction

Lecture 6: Plot Type – Correlation – Scatter Plot

Lecture 7: Plot Type – Correlation – Scatter Plot With Best Fit Line

Lecture 8: Plot Type – Correlation – Counts Plot

Lecture 9: Plot Type – Correlation-Distribution – Marginal Boxplot

Lecture 10: Plot Type – Correlation – Correlogram-Heatmap

Lecture 11: Plot Type – Correlation – Pairwise Plot

Lecture 12: Plot Type – Deviation Plot – Diverging Bars

Lecture 13: Plot Type – Deviation Plot – Diverging Dot Plot

Lecture 14: Plot Type – Ranking Plot – Ordered Bar Plot

Lecture 15: Plot Type – Ranking Plot – Dot Plot

Lecture 16: Plot Type – Distribution Plot – Histogram for Continuous Variable

Lecture 17: Plot Type – Distribution Plot – Histogram for Categorical Variable

Lecture 18: Plot Type – Distribution Plot – Density Plot

Lecture 19: Plot Type – Distribution Plot – Box Plot

Lecture 20: Plot Type – Composition Plot – Pie Chart

Lecture 21: Plot Type – Composition Plot – Tree Map

Lecture 22: Plot Type – Composition Plot – Bar Chart

Lecture 23: Plot Type – Change Plot – Time Series Plot

Lecture 24: Plot Type – Change Plot – Time Series Decomposition Plot

Chapter 4: PART IV : STEP BY STEP EXPLORATORY DATA ANALYSIS

Lecture 1: Exploratory Data Analysis Workflow – Introduction

Lecture 2: Lets Understand The Big Picture

Lecture 3: What is Exploratory Data Analysis ?

Lecture 4: EDA – Step 1 – Variable Identification

Lecture 5: EDA – Step 2 -Univariate Analysis

Lecture 6: EDA – Step 3 -Concept – BiVariate Analysis

Lecture 7: EDA – Step 3 -LAB – BiVariate Analysis

Lecture 8: EDA – Step 4 – Missing values treatment

Lecture 9: EDA – Step 5 – Outlier Detection and Treatment

Lecture 10: EDA – Step 6 – Variable transformation

Lecture 11: EDA – Step 7 – Variable creation

Lecture 12: What Next ?

Chapter 5: Resources For Course

Lecture 1: Resource Location

Instructors

Mukesh Ranjan
Technical Consultant

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 3 votes

4 stars: 2 votes

5 stars: 5 votes

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