Udemy - Data Visualization in Python Masterclass: Beginners to Pro
Are you ready to start your path to becoming a Data Scientist!
Description
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 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 167 Full HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive courses!
We'll teach you how to program with Python, how to create amazing data visualizations with Python!
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 an experience of creating visualization of real life projects
Enroll in the course and become a data scientist today!
Who this course is for:
Anyone interested in learning more about python, data science, or data visualizations.
Anyone interested in the rapidly expanding world of data science!
Developers who want to work in analytics and visualization projects.
Anyone interested in real life and practical cases data visualization
Who this course is for:
- 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.
Course content
- Introduction
- Welcome!!!
- Introduction
- Anaconda Installation for Windows OS
- Anaconda Installation for Mac OS
- Anaconda Installation on Ubuntu OS
- Jupyter Notebook Keyboard Shortcuts
- Test Yourself
- Python Crash Course
- Introduction
- Data Types: Numbers
- Variable Assignment
- String
- Test Yourself
- Free Coupon Form for the Next Course
- 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
- 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, Hist, and Box Plots
- Subplot
- xlim, ylim, xticks, and yticks
- Animation Plot Part 1
- Animation Plot Part 2
- Free Coupon for the Next Course
- 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
- Exploratory Data Analysis (EDA) of Boston Housing Dataset
- 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
- Exploratory Data Analysis (EDA) 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
- Exploratory Data Analysis (EDA) of Covid-19 Cases
- 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
- Exploratory Data Analysis (EDA) of 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
- Exploratory Data Analysis (EDA) 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
- Exploratory Data Analysis (EDA) 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
Data_Visualization_in_Python_Masterclass_Beginners_to_Pro.part1.rar
Data_Visualization_in_Python_Masterclass_Beginners_to_Pro.part2.rar
Data_Visualization_in_Python_Masterclass_Beginners_to_Pro.part3.rar
