Data Analytics Using Python Visualizations

Data Analytics Using Python Visualizations

by Manas Dasgupta
Released June 2022
Publisher(s): Packt Publishing
ISBN: 9781804614839

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Video description

If you are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course takes a holistic approach to teach visualization techniques.

We will be taking real-life business scenarios and raw data to go through detailed Exploratory Data Analysis (EDA) techniques to prepare the raw data to suit the appropriate visualization needs. You will learn about data analytics and exploratory data analysis techniques using multiple different data structures with NumPy and Pandas libraries. You will also learn various chart/graph types, customization/configuration, and vectorization techniques.

We will look at advanced visualizations using business applications such as single and multiple bar charts, pie charts, and bubble charts with the vectorization of properties. We will further explore Seaborn Boxplot, Violin plot, Categorical Scatterplot, and how to create heat maps.

By the end of the course, you will learn the foundational techniques of data analytics and deeper customizations on visualizations. You will be able to confidently use Python visualization libraries such as Matplotlib, Seaborn, and Bokeh in your future projects.

What You Will Learn

  • Learn about the various visualization concepts
  • Learn to create simple plots using Matplotlib
  • Learn about marginal histograms and marginal boxplots
  • Learn handling images using pixel metrics
  • Learn about categorical variables and histograms (with EDA)
  • Learn various data generation techniques

Audience

This course is for Python and machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial to leaders, managers, and anyone whose job involves presenting data in the form of visuals, which include developers, architects, and system analysts.

A basic understanding of Python will be helpful, but not mandatory.

About The Author

Manas Dasgupta: Manas Dasgupta holds a master’s degree (MSc) from the Liverpool John Moore’s University (LJMU), the UK in Artificial Intelligence and Machine Learning (AI/ML). My specialization and research areas are Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, and areas such as Supervised Learning on Semantic Similarity and so on.

His expertise area also encompasses an array of Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised, and Clustering methods.

He has almost 20 Years of experience in the IT Industry, mostly in the Financial Services domain. Starting as a Developer to being an Architect for several years to a leadership position. His key focus and passion are to increase technical breadth and innovation.

Table of contents

  1. Chapter 1 : Matplotlib and Seaborn – Libraries and Techniques
    1. Promotional Video
    2. Author Introduction
    3. What You Will Learn
    4. Visualization Concepts
    5. Introduction to Matplotlib
    6. Creating Simple Plots Using Matplotlib
    7. Creating Scatter Plots
    8. Creating Axis Limits
    9. Parameterizing Plots
    10. Creating Error Bars
    11. Plotting Histograms and Box Plots
    12. Plotting 2D Histograms
    13. Marginal Histograms and Marginal Boxplots
    14. Working with Subplots
    15. Stock Trend / Time Series Plot and Annotations
    16. Plotting Images and Clustering
    17. Creating 2D Contour plots for 3D Data
    18. Creating 3D Plots Including 3D Contours
    19. Stylesheets, rcParam, and Custom Stylesheets
  2. Chapter 2 : Advanced Visualizations Using Business Applications
    1. Single and Multiple Bar Charts
    2. Area and Stacked-Area Charts
    3. Drawing Pie Charts
    4. Bubble Charts with Vectorization of Properties
    5. Plotting Regression Lines with OLS (ML)
    6. Categorical Variables and Histograms (with EDA)
    7. Seaborn Boxplot, Violin plot, Categorical Scatterplot
    8. Seaborn Slopeplots for Comparing Distributions
    9. Dumbbell Plot for Category-Wise Value Movement
    10. Creating Heatmaps
    11. Working with Pairplots
    12. Seasonal Trendcharts
    13. Yearplot and Calendarplot for Color-Scaled Trends
    14. Radarplot to Compare Scores of Multiple Parameters
  3. Chapter 3 : Working with the Beautiful and Powerful Bokeh Library
    1. Introduction to Bokeh
    2. Creating Simple and Multiple Line Plots
    3. Customizing Your Plots
    4. Creating Bubble Plots – Vectorizing Your Plot
    5. Working with Layouts – Row / Column / Grid
    6. Using the ColumnDataSource Object
    7. Applying Filters – IndexFilter, BooleanFilter, GroupFilter
    8. Widgets – Dynamic Plot Controls
    9. Plotting on a Google Map Using Google Map API
    10. Closing Notes

Product information

  • Title: Data Analytics Using Python Visualizations
  • Author(s): Manas Dasgupta
  • Release date: June 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781804614839