Data Understanding and Data Visualization with Python

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


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

  1. Chapter 1 : Introduction to the Course
    1. About the Tutor and AI Sciences
    2. Introduction to Instructor
    3. Focus of the Course
    4. Content of the Course
  2. Chapter 2 : Strings in Python
    1. Introduction to Strings
    2. Multi-Line Strings
    3. Indexing Strings
    4. Indexing Strings Quiz
    5. Indexing Strings Solution
    6. String Methods
    7. String Methods Quiz
    8. String Methods Solution
    9. String Escape Sequences
    10. String Escape Sequences Quiz
    11. String Escape Sequences Solution
  3. Chapter 3 : Python Data Structure
    1. Introduction to Data Structure
    2. Data Structures - Defining and Indexing
    3. Data Structures - Insertion and Deletion
    4. Data Structures-Insertion and Deletion Quiz
    5. Data Structures-Insertion and Deletion Solution
    6. Data Structures - Insertion and Deletion Python Practice
    7. Data Structures-Insertion and Deletion Python Practice Quiz
    8. Data Structures insertion And Deletion python Practice Solution
    9. Data Structures - Deep Copy or Reference and Slicing
    10. Data Structures-Deep Copy or Reference and Slicing Quiz
    11. Data Structures-Deep Copy or Reference and Slicing Solution
    12. Data Structures - Exploring Methods Using TAB Completion
    13. Data Structures - Abstract Ways
    14. Data Structures - Problem Solving Practice
    15. Data Structures Problem Solving Practice Quiz
    16. Data Structures-Problem Solving Practice Solution
  4. Chapter 4 : NumPy for Numerical Data Processing
    1. Introduction to NumPy
    2. NumPy Dimensions
    3. NumPy Shape, Size, and Bytes
    4. NumPy Arange and Random Package
    5. NumPy Arange and Random Package Quiz
    6. NumPy Arange and Random Package Solution
    7. NumPy Random and Reshape
    8. NumPy Slicing Combined
    9. NumPy Slicing Combined Quiz
    10. NumPy Slicing Combined Solution
    11. NumPy Masking
    12. NumPy Masking Quiz
    13. NumPy Masking Solution
    14. NumPy Broadcasting and Concatenation
    15. NumPy Ufuncs and SpeedTest
    16. Ufuncs Add, Sum, and Plus Operators
    17. Ufuncs Subtract Power Mod
    18. Ufuncs Comparisons Logical Operators
    19. Ufuncs Comparisons Logical Operators Quiz
    20. Ufuncs Comparisons Logical Operators Solution
    21. Ufuncs Output Argument
    22. NumPy Playing with Images
    23. NumPy Playing With Images Quiz
    24. NumPy Playing With Images Solution
    25. NumPy KNN Classifier from Scratch
    26. NumPy Structured Arrays
    27. NumPy Structured Arrays Quiz
    28. NumPy Structured Arrays Solution
  5. Chapter 5 : Pandas for Data Manipulation and Understanding
    1. Introduction to Pandas
    2. Pandas Series
    3. Pandas DataFrame
    4. Pandas DataFrame Quiz
    5. Pandas DataFrame Solution
    6. Pandas Missing Values
    7. Pandas loc and Iloc
    8. Pandas in Practice
    9. Pandas Group By
    10. Pandas Group By Quiz
    11. Pandas Group By Solution
    12. Hierarchical Indexing
    13. Pandas Rolling
    14. Pandas Rolling Quiz
    15. Pandas Rolling Solution
    16. Pandas Where
    17. Pandas Clip
    18. Pandas Clip Quiz
    19. Pandas Clip Solution
    20. Pandas Merge
    21. Pandas Merge Quiz
    22. Pandas Merge Solution
    23. Pandas Pivot Table
    24. Pandas Strings
    25. Pandas DateTime
    26. Pandas Hands-On COVID-19 Data
    27. Pandas Hands-On COVID-19 Data Bug Fixed
  6. Chapter 6 : Matplotlib for Data Visualization
    1. Introduction to Matplotlib
    2. Matplotlib Multiple Plots
    3. Matplotlib Colors and Styles
    4. Matplotlib Colors and Styles Quiz
    5. Matplotlib Colors and Styles Solution
    6. Matplotlib Colors and Styles Shortcuts
    7. Matplotlib Axis Limits
    8. Matplotlib Axis Limits Quiz
    9. Matplotlib Axis Limits Solution
    10. Matplotlib Legends Labels
    11. Matplotlib Set Function
    12. Matplotlib Set Function Quiz
    13. Matplotlib Set Function Solution
    14. Matplotlib Markers
    15. Matplotlib Markers Random Plots
    16. Matplotlib Scatter Plot
    17. Matplotlib Contour Plot
    18. Matplotlib Contour Plot Quiz
    19. Matplotlib Contour Plot Solution
    20. Matplotlib Histograms
    21. Matplotlib Subplots
    22. Matplotlib Subplots Quiz
    23. Matplotlib Subplots Solution
    24. Matplotlib 3D Introduction
    25. Matplotlib 3D Scatter Plots
    26. Matplotlib 3D Scatter Plot Quiz
    27. Matplotlib 3D Scatter Plot Solution
    28. Matplotlib 3D Surface Plots
  7. Chapter 7 : Seaborn for Data Visualization
    1. Introduction to Seaborn
    2. Seaborn Relplot
    3. Seaborn Relplot Quiz
    4. Seaborn Relplot Solution
    5. Seaborn Relplot Kind Line
    6. Seaborn Relplot Facets
    7. Seaborn Relplot Facets Quiz
    8. Seaborn Relplot Facets Solution
    9. Seaborn Catplot
    10. Seaborn Heatmaps
  8. Chapter 8 : Bokeh for Interactive Plotting
    1. Introduction to Bokeh
    2. Bokeh Multiplots Markers
    3. Bokeh Multiplots Grid Plot
    4. Bokeh Multiplots Grid Plot Quiz
    5. Bokeh Multiplots Grid Plot Solution
  9. Chapter 9 : Plotly for 3D Interactive Plotting
    1. Plotly 3D Interactive Scatter Plot
    2. Plotly 3D Interactive Scatter Plot Quiz
    3. Plotly 3D Interactive Scatter Plot Solution
    4. Plotly 3D Interactive Surface Plot
    5. Plotly 3D Interactive Surface Plot Quiz
    6. Plotly 3D Interactive Surface Plot Solution
  10. Chapter 10 : Geographic Maps with Folium
    1. Geographic Maps with Folium Using COVID-19 Data
    2. Geographic Maps with Folium Using COVID-19 Data Quiz
    3. Geographic Maps with Folium Using COVID-19 Data Solution
  11. Chapter 11 : Pandas for Plotting
    1. Pandas for Plotting
    2. Thank You

Product information

  • Title: Data Understanding and Data Visualization with Python
  • Author(s): AI Sciences
  • Release date: March 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781801078795