Data Visualization In Python Masterclass™: Beginners To Pro
Last updated 6/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.53 GB | Duration: 21h 52m
Last updated 6/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.53 GB | Duration: 21h 52m
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.