Data Understanding and Data Visualization with Python

Video description

Learn data visualization from scratch

About This Video

  • Learn data manipulation and visualization from scratch
  • Master various Python libraries such as NumPy, Pandas, Matplotlib, and so on
  • Create interactive, insightful visualizations

In Detail

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 is 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. Hence, you not only learn the theoretical fundamentals of visualizations but also gain essential practical skills. With over 12 hours of content, this is one of the most comprehensive courses you will be doing on data visualization in Python.

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Table of contents

  1. Chapter 1 : Introduction to the Course
    1. About the Tutor and AI Sciences
    2. Focus of the Course
    3. Content of the Course
  2. Chapter 2 : Strings in Python
    1. Introduction to Strings
    2. Multi-Line Strings
    3. Indexing Strings
    4. String Methods
    5. String Escape Sequences
  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 Python Practice
    5. Data Structures - Deep Copy or Reference and Slicing
    6. Data Structures - Exploring Methods Using TAB Completion
    7. Data Structures - Abstract Ways
    8. Data Structures - Problem Solving Practice
  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 Random and Reshape
    6. NumPy Slicing Combined
    7. NumPy Masking
    8. NumPy BroadCasting and Concatenation
    9. NumPy Ufuncs and SpeedTest
    10. Ufuncs Add, Sum, and Plus Operators
    11. Ufuncs Subtract Power Mod
    12. Ufuncs Comparisons Logical Operators
    13. Ufuncs Output Argument
    14. NumPy Playing with Images
    15. NumPy KNN Classifier from Scratch
    16. NumPy Structured Arrays
  5. Chapter 5 : Pandas for Data Manipulation and Understanding
    1. Introduction to Pandas
    2. Pandas Series
    3. Pandas DataFrame
    4. Pandas Missing Values
    5. Pandas loc and Iloc
    6. Pandas in Practice
    7. Pandas Group by
    8. Hierarchical Indexing
    9. Pandas Rolling
    10. Pandas Where
    11. Pandas Clip
    12. Pandas Merge
    13. Pandas Pivot Table
    14. Pandas Strings
    15. Pandas DateTime
    16. Pandas Hands-On COVID-19 Data
    17. 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 Shortcuts
    5. Matplotlib Axis Limits
    6. Matplotlib Legends Labels
    7. Matplotlib Set Function
    8. Matplotlib Markers
    9. Matplotlib Markers Random Plots
    10. Matplotlib Scatter Plot
    11. Matplotlib Contour Plot
    12. Matplotlib Histograms
    13. Matplotlib Subplots
    14. Matplotlib 3D Introduction
    15. Matplotlib 3D Scatter Plots
    16. Matplotlib 3D Surface Plots
  7. Chapter 7 : Seaborn for Data Visualization
    1. Introduction to Seaborn
    2. Seaborn Relplot
    3. Seaborn Relplot Kind Line
    4. Seaborn Relplot Facets
    5. Seaborn Catplot
    6. Seaborn Heatmaps
  8. Chapter 8 : Bokeh for Interactive Plotting
    1. Introduction to Bokeh
    2. Bokeh Multiplots Markers
    3. Bokeh Multiplots Grid Plot
  9. Chapter 9 : Plotly for 3D Interactive Plotting
    1. Plotly 3D Interactive Scatter Plot
    2. Plotly 3D Interactive Surface Plot
  10. Chapter 10 : Geographic Maps with Folium
    1. Geographic Maps with Folium using COVID-19 Data
  11. Chapter 11 : Pandas for Plotting
    1. Pandas for Plotting

Product information

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