Video description
Data visualization has gained a lot of traction resulting from an increased focus on data analytics. To be a successful data scientist, data manipulation and wrangling are not enough. Visualizing data to garner insights is an equally important tool in the data science toolkit. Given the myriad types of data that exist, visualization has become an increasingly important topic.
This course will equip you with all the skills you need to successfully create insightful visualizations. The course first starts with the fundamentals of Python. Then, the course teaches you how to use libraries such as NumPy, Pandas, Matplotlib, Seaborn, Bokeh, and so on. Additionally, you will learn data manipulation, which is the step prior to visualization. You will also learn how to plot geographical data using Folium. Each module in the course has practical handson mini projects, and with the mammoth content, this is one of the most comprehensive courses you will be doing on data visualization in Python.
By the end of this course, you have not only learned the theoretical fundamentals of visualizations but also gained essential practical skills to explore this growing domain.
What You Will Learn
 Study fundamentals of data manipulation and visualization
 Use Pandas and NumPy for data manipulation
 Handle text data with string
 Learn CRUD (Create, Retrieve, Update, Delete) operations on data
 Look at 3D visualizations with Matplotlib and Plotly
 Learn to create surface and scatter 3D interactive plots in Plotly
Audience
This course is for anyone who seeks to perform data manipulations and create insightful visualizations. Even if you are an absolute beginner, you will benefit from this course as it begins with the fundamentals of Python and teaches you the necessary skills in using Pythonbased visualization libraries.
About The Author
AI Sciences: AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.
AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.
Their courses have successfully helped more than 100,000 students master AI and data science.
Table of contents
 Chapter 1 : Introduction to the Course
 Chapter 2 : Strings in Python

Chapter 3 : Python Data Structure
 Introduction to Data Structure
 Data Structures  Defining and Indexing
 Data Structures  Insertion and Deletion
 Data StructuresInsertion and Deletion Quiz
 Data StructuresInsertion and Deletion Solution
 Data Structures  Insertion and Deletion Python Practice
 Data StructuresInsertion and Deletion Python Practice Quiz
 Data Structures insertion And Deletion python Practice Solution
 Data Structures  Deep Copy or Reference and Slicing
 Data StructuresDeep Copy or Reference and Slicing Quiz
 Data StructuresDeep Copy or Reference and Slicing Solution
 Data Structures  Exploring Methods Using TAB Completion
 Data Structures  Abstract Ways
 Data Structures  Problem Solving Practice
 Data Structures Problem Solving Practice Quiz
 Data StructuresProblem Solving Practice Solution

Chapter 4 : NumPy for Numerical Data Processing
 Introduction to NumPy
 NumPy Dimensions
 NumPy Shape, Size, and Bytes
 NumPy Arange and Random Package
 NumPy Arange and Random Package Quiz
 NumPy Arange and Random Package Solution
 NumPy Random and Reshape
 NumPy Slicing Combined
 NumPy Slicing Combined Quiz
 NumPy Slicing Combined Solution
 NumPy Masking
 NumPy Masking Quiz
 NumPy Masking Solution
 NumPy Broadcasting and Concatenation
 NumPy Ufuncs and SpeedTest
 Ufuncs Add, Sum, and Plus Operators
 Ufuncs Subtract Power Mod
 Ufuncs Comparisons Logical Operators
 Ufuncs Comparisons Logical Operators Quiz
 Ufuncs Comparisons Logical Operators Solution
 Ufuncs Output Argument
 NumPy Playing with Images
 NumPy Playing With Images Quiz
 NumPy Playing With Images Solution
 NumPy KNN Classifier from Scratch
 NumPy Structured Arrays
 NumPy Structured Arrays Quiz
 NumPy Structured Arrays Solution

Chapter 5 : Pandas for Data Manipulation and Understanding
 Introduction to Pandas
 Pandas Series
 Pandas DataFrame
 Pandas DataFrame Quiz
 Pandas DataFrame Solution
 Pandas Missing Values
 Pandas loc and Iloc
 Pandas in Practice
 Pandas Group By
 Pandas Group By Quiz
 Pandas Group By Solution
 Hierarchical Indexing
 Pandas Rolling
 Pandas Rolling Quiz
 Pandas Rolling Solution
 Pandas Where
 Pandas Clip
 Pandas Clip Quiz
 Pandas Clip Solution
 Pandas Merge
 Pandas Merge Quiz
 Pandas Merge Solution
 Pandas Pivot Table
 Pandas Strings
 Pandas DateTime
 Pandas HandsOn COVID19 Data
 Pandas HandsOn COVID19 Data Bug Fixed

Chapter 6 : Matplotlib for Data Visualization
 Introduction to Matplotlib
 Matplotlib Multiple Plots
 Matplotlib Colors and Styles
 Matplotlib Colors and Styles Quiz
 Matplotlib Colors and Styles Solution
 Matplotlib Colors and Styles Shortcuts
 Matplotlib Axis Limits
 Matplotlib Axis Limits Quiz
 Matplotlib Axis Limits Solution
 Matplotlib Legends Labels
 Matplotlib Set Function
 Matplotlib Set Function Quiz
 Matplotlib Set Function Solution
 Matplotlib Markers
 Matplotlib Markers Random Plots
 Matplotlib Scatter Plot
 Matplotlib Contour Plot
 Matplotlib Contour Plot Quiz
 Matplotlib Contour Plot Solution
 Matplotlib Histograms
 Matplotlib Subplots
 Matplotlib Subplots Quiz
 Matplotlib Subplots Solution
 Matplotlib 3D Introduction
 Matplotlib 3D Scatter Plots
 Matplotlib 3D Scatter Plot Quiz
 Matplotlib 3D Scatter Plot Solution
 Matplotlib 3D Surface Plots
 Chapter 7 : Seaborn for Data Visualization
 Chapter 8 : Bokeh for Interactive Plotting
 Chapter 9 : Plotly for 3D Interactive Plotting
 Chapter 10 : Geographic Maps with Folium
 Chapter 11 : Pandas for Plotting
Product information
 Title: Data Understanding and Data Visualization with Python
 Author(s):
 Release date: March 2021
 Publisher(s): Packt Publishing
 ISBN: 9781801078795
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