Visualisation in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data.
What you will 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
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 Python
NumPy with Python
Using Pandas Data Frames to solve complex tasks
Use Pandas to Files
Use matplotlib and Seaborn for data visualizations
Use Plotly and Cufflinks for interactive visualizations
Exploratory Data Analysis (EDA) of Boston Housing Dataset
Exploratory Data Analysis (EDA) of Titanic Dataset
Exploratory Data Analysis (EDA) of Latest Covid-19 Dataset
and 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 projects
Enroll in the course and become a data scientist today!
English
language
Content
Introduction
Welcome!!!
Introduction
Q&A Support
Free Coupons for the Next Course
Anaconda Installation for Windows OS
Anaconda Installation for Mac OS
Anaconda Installation on Ubuntu OS
Jupyter Notebook Keyboard Shortcuts
Jupyter Notebook Shortcuts Article
Test Yourself
Python Crash Course
Introduction
Data Types: Numbers
Variable Assignment
String
Test Yourself
List
Set
Tuple
Dictionary
Test Yourself
Boolean and Comparison Operator
Logical Operator
Conditional Statements: If Else and Elif
For and While Loops in Python
Methods and Lambda Functions
Test Yourself
Do you know?
NumPy Crash Course
Introduction
Array
NaN and INF
Statistical Operations
Shape, Reshape, Ravel, Flatten
Test Yourself
Sequence, Repetitions, and Random Numbers
Where
File Read and Write
Concatenate and Sorting
Working with Dates
Do you Know?
Pandas Crash Course
Introduction
DataFrame and Series
File Reading and Writing
Info, Shape, Duplicated, and Drop
Columns
NaN and Null Values
Imputation
Lambda Function
Test Yourself
Data Visualization with Pandas
Introduction
Data Generation
Line Plot
More on Line Plot
Bar Plot
Stacked Plot
Histogram
Box Plot
Area and Scatter Plot
Hex and Pie Plot
Scatter Matrix and Subplots
Matplotlib
Introduction
Line Plot
Label
Scatter, Bar, and Hist Plots
Box Plot
Subplot
xlim, ylim, xticks, and yticks
Pie Plot
Pie Plot Text Color
Nested Pie Plot
Labeling a Pie Plot
Bar Chart on Polar Axis
Line Plot on a Polar Axis
Scatter Plot on a Polar Axis
Integral in Calculas Plot as Area Under the Curve
Animation Plot Part 1
Animation Plot Part 2
Time Series Plots
Dataset Loading
Line and Scatter Plots
Subplots
Heatmap
Histogram and KDE Plots
Seaborn
Introduction
Scatter Plot
Hue, Style and Size Part 1
Hue, Style and Size Part 2
Line Plot Part 1
Line Plot Part 2
Line Plot Part 3
Subplot
sns.lineplot(), sns.scatterplot()
Cat Plot
Box Plot
Boxen Plot
Violin Plot
Bar Plot
Point Plot
Joint Plot
Pair Plot
Regression Plot
Controlling Plotted Figure Aesthetics
Plotly and Cufflinks
Introduction
Installation and Setup
Line Plot
Scatter Plot
Bar Plot
Box Plot and Area Plot
3D Plot
Spread Plot and Hist Plot
Bubble Plot and Heatmap
Analysis and Visualization of Boston Housing Data
Introduction
Data Preparation
Data Deep Dive
pd.describe()
Bar Plot
Plot Styling
Pair Plot
Distribution Plot
Scatter Plot
Heatmap
Correlated Feature Selection
Heatmap and Pair Plot of Correlated Data
Box and Rel Plot
Joint Plot Part 1
Joint Plot Part 2
Linear Regression without ML Part 1
Linear Regression without ML Part 2
Analysis and Visualization of Titanic Dataset
Introduction
Data Understanding
Load Dataset
Heatmap
Univariate Analysis
Survived
Pclass Part 1
Pclass Part 2
Sex Part 1
Sex Part 2
Sex Part 3
Sex Part 4
Sex Part 5
Age Part 1
Age Part 2
Age Part 3
Age Part 4
Fare Part 1
Fare Part 2
Fare Part 3
Fare Part 4
Sibsp Part 1
Sibsp Part 2
Sibsp Part 3
Sibsp Part 4
Parch Part 1
Parch Part 2
Embarked
Who
Analysis and Visualization of Covid-19 Data
Introduction
Data Understanding
Import Packages
Clone Latest Covid-19 Dataset
Import Cleaned Covid-19 Dataset
Import Preprocessed Data
Scatter Plot for Confirmed Cases
Cases Timelaps on Worldmap
Total Cases on Ships
Cases Over the Time with Area Plot Part 1
Cases Over the Time with Area Plot Part 2
Covid-19 Cases on Folium Map
Confirmed Cases with Animation
Confirmed and Death Cases with Bar Plot
Confirmed and Death Cases with Colormap
Deaths per 100 Cases
New Cases and Countries per Day
Correction in Top 15 Countries Case Analysis Part 1
Top 15 Countries Case Analysis Part 1
Top 15 Countries Case Analysis Part 2
Top 15 Countries Case Analysis Part 3
Top 15 Countries Case Analysis Part 4
Top 15 Countries Case Analysis Part 5
Save Figures in PNG, JPEG, and PDF
Scatter Plot for Deaths vs Confirmed Cases
Stacked Bar Plot
Stacked Line Plot
Growth Rate After 100 Cases
Growth Rate After 1000 Cases
Growth Rate After 10000 Cases
Growth Rate After 100k Cases
Tree Map Analysis
First and Last Case Report Time Part 1
First and Last Case Report Time Part 2
First and Last Case Report Time Part 3
Confirmed Cases by Country and Daywise
Covid-19 vs Other Epidemics
Analysis and Visualization of Reviews Text Data
Introduction
Getting Started
Data Import
Data Cleaning
Feature Engineering
Distribution of Sentiment Polarity
Distribution of Reviews Rating and Reviewers Age
Distribution of Review Text Length and Word Length
Distribution of Department, Division, and Class
Distribution of Unigram, Bigram and Trigram Part 1
Distribution of Unigram, Bigram and Trigram Part 2
Distribution of Unigram, Bigram and Trigram without STOP WORDS
Distribution of Top 20 Parts-of-Speech POS tags
Bivariate Analysis Part 1
Bivariate Analysis Part 2
Bivariate Analysis Part 3
Analysis and Visualization of IPL Cricket Matches
Introduction
About Cricket Matches and Package Import
Data Understanding
Wins and Lost Matches Analysis
MoM, City and Venue wise Analysis
MI vs CSK Head to Head Matches
Seasonwise Analysis
Ball by Ball Analysis
Analysis and Visualization of FIFA World Cup Matches
Introduction
FIFA World Cup Data Import
Data Cleaning
Most Number of World Cup Winning Title
Number of Goal Per Country
Attendance, Number of Teams, Goals, and Matches per Cup
Goals Per Team Per Word Cup
Matches with Highest Number of Attendance
Stadiums with Highest Average Attendance
Match Outcomes by Home and Away Teams
Python Coding in Mobile
Introduction
Python in Mobile
Matplotlib Plot in Mobile
Pandas Coding in Mobile
Seaborn Coding in Mobile
