Data Visualization Data Wrangling Masterclass with Python

Data Visualization Data Wrangling Masterclass with Python

What you’ll learn

Learn about python variables and data types.
Learn how to apply loops and conditionals in python.
Learn how to work with python strings.
Learn about Regular expressions and date time objects in Python.
Learn about the Numpy and the Pandas library.
Learn about Univariate, Bivariate and Multivariate Analysis.
Learn about advanced visualization such as facet grids, polar charts, waffle charts, maps, statistical charts etc.
Learn about animated visualizations such as bubble plot, facets, scatter maps and choropleth maps.
Learn about some more miscellaneous charts such as sunburst charts, parallel-coordinate charts, gantt charts etc.
Learn exploring data using Dabl and Sweetviz library.
Learn about working on real-world projects.
Create a variety of charts.

Requirements

Students Must be having a Laptop/PC.
Students Must have an Active Internet Connection.
Students Must have adequate Knowledge of English Language.
Students Must have Basics of Programming.
Students Must be willing to Learn Data Visualization.
Determination and Desire to Learn new things.
A positive attitude to success.

Description

Welcome to the online course on Data Visualization.Data Visualization is a graphical representation of information and data.Data Visualization tools provide an accessible way to see and understand data because with visualizations it becomes easier for the human brain to understand and pull insights out of the data.The primary goal of a data analyst is to increase efficiency and improve performance by discovering patterns in data.In this course, you will get advanced knowledge on Data Visualization.This course begins with providing you the complete knowledge on Python programming language.You will learn all the concepts of python programming required.This course will cover:-Python variables.Python data types.Loops and Conditionals.Strings.Regular Expressions.Data Time Objects.Numpy Library.Pandas Library.Data Visualization using PythonQuery Analysis.Along with python programming, this course will cover the concepts of query analysis as well.That will help you become an expert in analyzing data using different libraries such as Dabl and sweetviz.Not only this, this course will provide advanced knowledge on Data Visualization.This course will cover:-Basic data visualizationAdvanced-Data Visualization such as facet grids, polar charts, waffle charts, maps, statistical charts etc.Animated Data Visualization such as bubble plot, facets, scatter maps, and choropleth maps.Some miscellaneous charts such as sunburst charts, parallel-coordinate charts, Gantt charts, etc.All this in one course!!!!Along with all these, we have three bonus projects for you!Startup Case study and Analysis.Player performance Reviewer, andIPL Data Science Analyzer.Where you will apply all the concepts learned in this course.This course is a complete package. Lots and lots of quizzes and exercises are waiting for you.You will also have access to all the resources used in this course.Instructor Support – Quick Instructor Support for any queries.I’m looking forward to see you in the course!Enroll now and become an expert in data visualizations.

Overview

Section 1: Python Fundamentals

Lecture 1 Why should you learn Python?

Lecture 2 Installing Python and Jupyter Notebook

Lecture 3 Understanding the Interface of Jupyter Notebook

Lecture 4 Q and A

Lecture 5 Naming Convention for variables

Lecture 6 Built in Data Types and Type Casting

Lecture 7 Scope of Variables

Lecture 8 Quiz Solution

Lecture 9 Exercise on Variables and Data Types

Lecture 10 Solution on Variables and Data Types

Lecture 11 Exercise on Scope of Variables

Lecture 12 Solution on Scope of Variables

Lecture 13 Arithmetic and Assignment Operators

Lecture 14 Comparison, Logical, and Bitwise Operators

Lecture 15 Identity and Membership Operators

Lecture 16 Quiz Solution

Lecture 17 Exercise on Arithmetic and Assignment Operators

Lecture 18 Solution on Arithmetic and Assignment Operators

Lecture 19 Exercise on Comparison, Logical, and Bitwise Operators

Lecture 20 Solution on Comparison, Logical, and Bitwise Operators

Lecture 21 Exercise on Identity and Membership Operators

Lecture 22 Solution on Identity and Membership Operators

Lecture 23 String Formatting

Lecture 24 String Methods

Lecture 25 User Input

Lecture 26 Quiz Solution

Lecture 27 Exercise on Strings

Lecture 28 Solution on Strings

Lecture 29 If, elif, and else

Lecture 30 For and While

Lecture 31 Break and Continue

Lecture 32 Quiz Solution

Lecture 33 Exercise on Loops and Conditionals

Lecture 34 Solution on Loops and Conditionals

Section 2: Python for Data Science

Lecture 35 Introduction to datetime

Lecture 36 Date and Time Class

Lecture 37 Datetime Class

Lecture 38 Timedelta Class

Lecture 39 Quiz Solution

Lecture 40 Exercise on Date and Time

Lecture 41 Solution on Date and Time

Lecture 42 Meta Characters for Regular Expressions

Lecture 43 Built-in Functions for Regular Expressions

Lecture 44 Special Characters for Regular Expressions

Lecture 45 Sets for Regular Expressions

Lecture 46 Quiz Solution

Lecture 47 Exercise on Regular Expressions

Lecture 48 Solution on Regular Expressions

Lecture 49 Array Creation using Numpy

Lecture 50 Mathematical Operations using Numpy

Lecture 51 Built-in Functions in Numpy

Lecture 52 Quiz Solution

Lecture 53 Exercise on Built-in Functions in Numpy

Lecture 54 Solution on Built-in Functions in Numpy

Lecture 55 Reading Datasets using Pandas

Lecture 56 Plotting Data in Pandas

Lecture 57 Indexing, Selecting, and Filtering Data using Pandas

Lecture 58 Merging and Concatenating DataFrames

Lecture 59 Lambda, Map, and Apply Functions

Lecture 60 Quiz Solution

Lecture 61 Exercise on Pandas Plotting

Lecture 62 Solution on Pandas Plotting

Lecture 63 Exercise on Indexing and Selecting

Lecture 64 Solution on Indexing and Selecting

Lecture 65 Exercise on Apply Functions

Lecture 66 Solution on Apply Functions

Section 3: Data Visualization

Lecture 67 Univariate Analysis

Lecture 68 Bivariate Analysis

Lecture 69 Multivariate Analysis

Lecture 70 Quiz Solution

Lecture 71 Exercise on Univariate Analysis

Lecture 72 Exercise Solution on Univariate Analysis

Lecture 73 Exercise on Bivariate Analysis

Lecture 74 Exercise Solution on Bivariate Analysis

Lecture 75 Exercise on Multivariate Analysis

Lecture 76 Exercise Solution on Multivariate Analysis

Lecture 77 Scatter Plots

Lecture 78 Charts with Colorscale

Lecture 79 Bar, Line, and Area Charts

Lecture 80 Facet Grids

Lecture 81 Statistical Charts

Lecture 82 Polar Charts

Lecture 83 Subplots

Lecture 84 3D Charts

Lecture 85 Waffle Charts

Lecture 86 Maps

Lecture 87 Quiz Solution

Lecture 88 Exercise on Facet Grids

Lecture 89 Exercise Solution on Facet Grids

Lecture 90 Exercise on Statistical Charts

Lecture 91 Exercise Solution on Statistical Charts

Lecture 92 Exercise on Polar Charts

Lecture 93 Exercise Solution on Polar Charts

Lecture 94 Exercise on 3D Charts

Lecture 95 Exercise Solution on 3D Charts

Lecture 96 Animation with Bubbleplot

Lecture 97 Animation with Facets

Lecture 98 Animation with Scatter Maps

Lecture 99 Animation with Choropleth Maps

Lecture 100 Quiz Solution

Lecture 101 Introduction to Ipywidgets

Lecture 102 Interactive Univariate Analysis

Lecture 103 Interactive Bivariate Analysis

Lecture 104 Interactive Multivariate Analysis

Lecture 105 Quiz Solution

Lecture 106 Sunburst Charts

Lecture 107 Parallel Co-ordinate Charts

Lecture 108 Funnel Charts

Lecture 109 Gantt Charts

Lecture 110 Ternary Charts

Lecture 111 Tree Maps

Lecture 112 Network Charts

Lecture 113 Quiz Solution

Section 4: Query Analysis

Lecture 114 Aggregate functions used for Grouping

Lecture 115 Using Groupby for Grouping Operations

Lecture 116 Groupby with Idxmax and Idxmin functions

Lecture 117 Using Color scales for better visualization

Lecture 118 Visualizing the Groupby Operations

Lecture 119 Using Pivot Tables for Grouping Operations

Lecture 120 Difference between Groupby and Pivot tables

Lecture 121 Performing Cross Tabulation

Lecture 122 Visualizing Cross tabulated Data

Lecture 123 Interactive Grouping Operations

Lecture 124 Quiz Solution

Lecture 125 Exercise on Groupby Operations

Lecture 126 Exercise Solution on Groupby Operations

Lecture 127 Exercise on Pivot Table

Lecture 128 Exercise Solution on Pivot Table

Lecture 129 Exercise on Cross Tabulations

Lecture 130 Exercise Solution on Cross Tabulations

Lecture 131 When to perform Filtering Operations

Lecture 132 Introduction to Simple Filtering Operations

Lecture 133 Advanced Filtering Operations

Lecture 134 Filtering and Grouping Operations

Lecture 135 Interactive Filtering Operations

Lecture 136 Quiz Solution

Lecture 137 Exercise on Filtering Operations

Lecture 138 Exercise Solution on Filtering Operations

Section 5: Startups Case Study and Analysis

Lecture 139 Understanding the Problem Statement

Lecture 140 Setting up the Environment

Lecture 141 Data Cleaning

Lecture 142 Querying the data using Visualizations Part – 1

Lecture 143 Querying the data using Visualizations Part – 2

Lecture 144 Major Learning from Data

Section 6: Player’s Performance Reviewer

Lecture 145 Understanding the problem statement

Lecture 146 Setting up the Environment

Lecture 147 Data Cleaning

Lecture 148 Feature Engineering

Lecture 149 Data Visualization

Lecture 150 Query Analysis

Lecture 151 Major Learnings from the project

Section 7: IPL Data Science Analyzer

Lecture 152 Setting up the Environment

Lecture 153 Understanding the Dataset

Lecture 154 Understanding the Problem Statement

Lecture 155 Summarizing Interesting Facts from the Data

Lecture 156 Exploring the Best Players from IPL

Lecture 157 Discovering the Biggest Matches in IPL

Lecture 158 Understanding the Match Results

Lecture 159 Uncovering the Most Popular IPL Seasons and Teams

Lecture 160 Realizing the Locations for all the IPL Seasons

Lecture 161 Comparing Toss Winners and Winners

Lecture 162 Checking the Winning Locations for all the Teams

Lecture 163 Analyzing Toss Decisions in IPL Matches

Lecture 164 What is DL in an IPL Match?

Lecture 165 Key Insights from this Project

Section 8: Outro Section

Lecture 166 Conclusion

Lecture 167 How to Get Your Certificate of Completion

Section 9: Bonus Section

Lecture 168 Bonus Lecture

Beginner Software Developers willing to Upskill.,Data Scientists willing to Learn Advanced Data Visualizations.,Business Analysts curious to Learn Data Visualizations with Python.,Beginners and Freshers willing to Learn something new.,Beginner Data Analyst willing to Learn Visualization.,Beginner Python Developers Curious to Upskill.,Anyone interested about the rapidly expanding world of data science!,Job-seekers.

Course Information:

Udemy | English | 9h 33m | 7.80 GB
Created by: Data Is Good Academy

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