Data Visualization In Python Masterclass™: Beginners To Pro

Visualisation in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data.

What you'll learn
Learn Complete Exploratory Data Analysis on the Latest Covid-19 Dataset
Learn EDA on Kaggle's Boston Housing and Titanic Datasets
Learn IPL Cricket Matches and FIFA World Cup Matches Analysis and Visualization
Learn Data Visualization by Plotly and Cufflinks, Seaborn, matplotlib, and Pandas
Learn Interactive plots and visualization
Installation of python and related libraries.
Covid-19 Data Visualization
Covid-19 Dataset Analysis and Visualization in Python
Data Science Visualization with Covid-19
Use the Numpy and Pandas in data manipulation
Learn Complete Text Data EDA
Create a variety of charts, Bar Charts, Line Charts, Stacked Charts, Pie Charts, Histograms, KDE plots, Violinplots, Boxplots, Auto Correlation plots, Scatter Plots, Heatmaps
Learn Data Analysis by Pandas.
Use the Pandas module with Python to create and structure data.
Customize graphs, modifying colors, lines, fonts, and more
Requirements
No introductory skill level of Python programming required
Have a computer (either Mac, Windows, or Linux)
Desire to learn!
Description
Are you ready to start your path to becoming a Data Scientist!KGP Talkie brings you all in one course. Learn all kinds of Data Visualization with practical datasets. This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations! This is a very unique course where you will learn EDA on Kaggle's Boston Housing, Titanic and Latest Covid-19 Datasets, Text Dataset, IPL Cricket Matches of all seasons, and FIFA world cup matches with real and practical examples.Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $110,000 in the United States and all over the World according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 200+ Full HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive courses on Complete Data Visualization in Python.We'll teach you how to program with Python, how to analyze and create amazing data visualizations with Python! You can use this course as your ready-to-go reference for your own project.Here just a few of the topics we will be learning:Programming with PythonNumPy with PythonUsing Pandas Data Frames to solve complex tasksUse Pandas to FilesUse matplotlib and Seaborn for data visualizationsUse Plotly and Cufflinks for interactive visualizationsExploratory Data Analysis (EDA) of Boston Housing DatasetExploratory Data Analysis (EDA) of Titanic DatasetExploratory Data Analysis (EDA) of Latest Covid-19 Datasetand much, much more!By the end of this course you will:Have an understanding of how to program in Python.Know how to create and manipulate arrays using numpy and Python.Know how to use pandas to create and analyze data sets.Know how to use matplotlib and seaborn libraries to create beautiful data visualization.Have an amazing portfolio of python data analysis skills!Have experience of creating a visualization of real-life projectsEnroll in the course and become a data scientist today!

Overview

Section 1: Introduction

Lecture 1 Welcome

Lecture 2 DO NOT SKIP IT | Free Resources!

Lecture 3 DO NOT SKIP IT | Introduction

Lecture 4 Q&A Support

Lecture 5 Course Overview

Lecture 6 Anaconda Installation for Windows OS

Lecture 7 Anaconda Installation for Mac OS

Lecture 8 Anaconda Installation on Ubuntu OS

Lecture 9 Jupyter Notebook Keyboard Shortcuts

Lecture 10 Jupyter Notebook Shortcuts Article

Section 2: Python Crash Course

Lecture 11 Introduction

Lecture 12 Data Types: Numbers

Lecture 13 Variable Assignment

Lecture 14 String

Lecture 15 List

Lecture 16 Set

Lecture 17 Tuple

Lecture 18 Dictionary

Lecture 19 Boolean and Comparison Operator

Lecture 20 Logical Operator

Lecture 21 Conditional Statements: If Else and Elif

Lecture 22 For and While Loops in Python

Lecture 23 Methods and Lambda Functions

Section 3: NumPy Crash Course

Lecture 24 Introduction

Lecture 25 Array

Lecture 26 NaN and INF

Lecture 27 Statistical Operations

Lecture 28 Shape, Reshape, Ravel, Flatten

Lecture 29 Sequence, Repetitions, and Random Numbers

Lecture 30 Where

Lecture 31 File Read and Write

Lecture 32 Concatenate and Sorting

Lecture 33 Working with Dates

Section 4: Pandas Crash Course

Lecture 34 Introduction

Lecture 35 DataFrame and Series

Lecture 36 File Reading and Writing

Lecture 37 Info, Shape, Duplicated, and Drop

Lecture 38 Columns

Lecture 39 NaN and Null Values

Lecture 40 Imputation

Lecture 41 Lambda Function

Section 5: Data Visualization with Pandas

Lecture 42 Introduction

Lecture 43 Data Generation

Lecture 44 Line Plot

Lecture 45 More on Line Plot

Lecture 46 Bar Plot

Lecture 47 Stacked Plot

Lecture 48 Histogram

Lecture 49 Box Plot

Lecture 50 Area and Scatter Plot

Lecture 51 Hex and Pie Plot

Lecture 52 Scatter Matrix and Subplots

Section 6: Matplotlib

Lecture 53 Introduction

Lecture 54 Line Plot

Lecture 55 Label

Lecture 56 Scatter, Bar, and Hist Plots

Lecture 57 Box Plot

Lecture 58 Subplot

Lecture 59 xlim, ylim, xticks, and yticks

Lecture 60 Pie Plot

Lecture 61 Pie Plot Text Color

Lecture 62 Nested Pie Plot

Lecture 63 Labeling a Pie Plot

Lecture 64 Bar Chart on Polar Axis

Lecture 65 Line Plot on a Polar Axis

Lecture 66 Scatter Plot on a Polar Axis

Lecture 67 Integral in Calculus Plot as Area Under the Curve

Lecture 68 Animation Plot Part 1

Lecture 69 Animation Plot Part 2

Section 7: Time Series Plots

Lecture 70 Dataset Loading

Lecture 71 Line and Scatter Plots

Lecture 72 Subplots

Lecture 73 Heatmap

Lecture 74 Histogram and KDE Plots

Section 8: Seaborn

Lecture 75 Introduction

Lecture 76 Scatter Plot

Lecture 77 Hue, Style and Size Part 1

Lecture 78 Hue, Style and Size Part 2

Lecture 79 Line Plot Part 1

Lecture 80 Line Plot Part 2

Lecture 81 Line Plot Part 3

Lecture 82 Subplot

Lecture 83 sns.lineplot(), sns.scatterplot()

Lecture 84 Cat Plot

Lecture 85 Box Plot

Lecture 86 Boxen Plot

Lecture 87 Violin Plot

Lecture 88 Bar Plot

Lecture 89 Point Plot

Lecture 90 Joint Plot

Lecture 91 Pair Plot

Lecture 92 Regression Plot

Lecture 93 Controlling Plotted Figure Aesthetics

Section 9: Plotly and Cufflinks

Lecture 94 Introduction

Lecture 95 Installation and Setup

Lecture 96 Line Plot

Lecture 97 Scatter Plot

Lecture 98 Bar Plot

Lecture 99 Box Plot and Area Plot

Lecture 100 3D Plot

Lecture 101 Spread Plot and Hist Plot

Lecture 102 Bubble Plot and Heatmap

Section 10: Analysis and Visualization of Boston Housing Data

Lecture 103 Introduction

Lecture 104 Data Preparation [Update]

Lecture 105 Data Preparation

Lecture 106 Data Deep Dive

Lecture 107 pd.describe()

Lecture 108 Bar Plot

Lecture 109 Plot Styling

Lecture 110 Pair Plot

Lecture 111 Distribution Plot

Lecture 112 Scatter Plot

Lecture 113 Heatmap

Lecture 114 Correlated Feature Selection

Lecture 115 Heatmap and Pair Plot of Correlated Data

Lecture 116 Box and Rel Plot

Lecture 117 Joint Plot Part 1

Lecture 118 Joint Plot Part 2

Lecture 119 Linear Regression without ML Part 1

Lecture 120 Linear Regression without ML Part 2

Section 11: Analysis and Visualization of Titanic Dataset

Lecture 121 Introduction

Lecture 122 Data Understanding

Lecture 123 Load Dataset

Lecture 124 Heatmap

Lecture 125 Univariate Analysis

Lecture 126 Survived

Lecture 127 Pclass Part 1

Lecture 128 Pclass Part 2

Lecture 129 Sex Part 1

Lecture 130 Sex Part 2

Lecture 131 Sex Part 3

Lecture 132 Sex Part 4

Lecture 133 Sex Part 5

Lecture 134 Age Part 1

Lecture 135 Age Part 2

Lecture 136 Age Part 3

Lecture 137 Age Part 4

Lecture 138 Fare Part 1

Lecture 139 Fare Part 2

Lecture 140 Fare Part 3

Lecture 141 Fare Part 4

Lecture 142 Sibsp Part 1

Lecture 143 Sibsp Part 2

Lecture 144 Sibsp Part 3

Lecture 145 Sibsp Part 4

Lecture 146 Parch Part 1

Lecture 147 Parch Part 2

Lecture 148 Embarked

Lecture 149 Who

Section 12: Analysis and Visualization of Covid-19 Data

Lecture 150 Introduction

Lecture 151 Data Understanding

Lecture 152 Import Packages

Lecture 153 Clone Latest Covid-19 Dataset

Lecture 154 Import Cleaned Covid-19 Dataset

Lecture 155 Import Preprocessed Data

Lecture 156 Scatter Plot for Confirmed Cases

Lecture 157 Cases Timelaps on Worldmap

Lecture 158 Total Cases on Ships

Lecture 159 Cases Over the Time with Area Plot Part 1

Lecture 160 Cases Over the Time with Area Plot Part 2

Lecture 161 Covid-19 Cases on Folium Map

Lecture 162 Confirmed Cases with Animation

Lecture 163 Confirmed and Death Cases with Bar Plot

Lecture 164 Confirmed and Death Cases with Colormap

Lecture 165 Deaths per 100 Cases

Lecture 166 New Cases and Countries per Day

Lecture 167 Correction in Top 15 Countries Case Analysis Part 1

Lecture 168 Top 15 Countries Case Analysis Part 1

Lecture 169 Top 15 Countries Case Analysis Part 2

Lecture 170 Top 15 Countries Case Analysis Part 3

Lecture 171 Top 15 Countries Case Analysis Part 4

Lecture 172 Top 15 Countries Case Analysis Part 5

Lecture 173 Save Figures in PNG, JPEG, and PDF

Lecture 174 Scatter Plot for Deaths vs Confirmed Cases

Lecture 175 Stacked Bar Plot

Lecture 176 Stacked Line Plot

Lecture 177 Growth Rate After 100 Cases

Lecture 178 Growth Rate After 1000 Cases

Lecture 179 Growth Rate After 10000 Cases

Lecture 180 Growth Rate After 100k Cases

Lecture 181 Tree Map Analysis

Lecture 182 First and Last Case Report Time Part 1

Lecture 183 First and Last Case Report Time Part 2

Lecture 184 First and Last Case Report Time Part 3

Lecture 185 Confirmed Cases by Country and Daywise

Lecture 186 Covid-19 vs Other Epidemics

Section 13: Analysis and Visualization of Reviews Text Data

Lecture 187 Introduction

Lecture 188 Getting Started

Lecture 189 Data Import

Lecture 190 Data Cleaning

Lecture 191 Feature Engineering

Lecture 192 Distribution of Sentiment Polarity

Lecture 193 Distribution of Reviews Rating and Reviewers Age

Lecture 194 Distribution of Review Text Length and Word Length

Lecture 195 Distribution of Department, Division, and Class

Lecture 196 Distribution of Unigram, Bigram and Trigram Part 1

Lecture 197 Distribution of Unigram, Bigram and Trigram Part 2

Lecture 198 Distribution of Unigram, Bigram and Trigram without STOP WORDS

Lecture 199 Distribution of Top 20 Parts-of-Speech POS tags

Lecture 200 Bivariate Analysis Part 1

Lecture 201 Bivariate Analysis Part 2

Lecture 202 Bivariate Analysis Part 3

Section 14: Analysis and Visualization of IPL Cricket Matches

Lecture 203 Introduction

Lecture 204 About Cricket Matches and Package Import

Lecture 205 Data Understanding

Lecture 206 Wins and Lost Matches Analysis

Lecture 207 MoM, City and Venue wise Analysis

Lecture 208 MI vs CSK Head to Head Matches

Lecture 209 Seasonwise Analysis

Lecture 210 Ball by Ball Analysis

Section 15: Analysis and Visualization of FIFA World Cup Matches

Lecture 211 Introduction

Lecture 212 FIFA World Cup Data Import

Lecture 213 Data Cleaning

Lecture 214 Most Number of World Cup Winning Title

Lecture 215 Number of Goal Per Country

Lecture 216 Attendance, Number of Teams, Goals, and Matches per Cup

Lecture 217 Goals Per Team Per Word Cup

Lecture 218 Matches with Highest Number of Attendance

Lecture 219 Stadiums with Highest Average Attendance

Lecture 220 Match Outcomes by Home and Away Teams

Section 16: Python Coding in Mobile

Lecture 221 Introduction

Lecture 222 Python in Mobile

Lecture 223 Matplotlib Plot in Mobile

Lecture 224 Pandas Coding in Mobile

Lecture 225 Seaborn Coding in Mobile

Beginners python programmers.,Beginners Data Science programmers.,Students of Data Science and Machine Learning.,Anyone interested in learning more about python, data science, or data visualizations.,Anyone interested about the rapidly expanding world of data science!,Developers who want to work in analytics and visualization project.,Anyone who wants to explore and understand data before applying machine learning.