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 hands-on 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 Python-based 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 Structures-Insertion and Deletion Quiz
- Data Structures-Insertion and Deletion Solution
- Data Structures - Insertion and Deletion Python Practice
- Data Structures-Insertion and Deletion Python Practice Quiz
- Data Structures insertion And Deletion python Practice Solution
- Data Structures - Deep Copy or Reference and Slicing
- Data Structures-Deep Copy or Reference and Slicing Quiz
- Data Structures-Deep 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 Structures-Problem 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 Hands-On COVID-19 Data
- Pandas Hands-On COVID-19 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
You might also like
book
Python for Geospatial Data Analysis
In spatial data science, things in closer proximity to one another likely have more in common …
book
Hands-On Exploratory Data Analysis with Python
Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key …
book
Python for Data Science
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. …
book
Data Analysis with Python and PySpark
Think big about your data! PySpark brings the powerful Spark big data processing engine to the …