Description

If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or 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 exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.

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. All resources and code files are placed here: https://github.com/PacktPublishing/Data-Analytics-using-Python-Visualizations

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.

Approach

This practical hands-on course has easy, step-by-step explanations with code to draw over 20 diverse kinds of charts and graphs using Python. Extensive quizzes are infused at logical points to validate the learning effectiveness.

Key Features

The art of presenting data in the form of powerful, innovative, and intuitive visualizations * In-depth coverage of Matplotlib, Seaborn, and Bokeh visualization libraries * Use of data analytics techniques/Exploratory Data Analysis (EDA) using several data generations and manipulation methods

Github Repo

https://github.com/PacktPublishing/Data-Analytics-using-Python-Visualizations

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.

Course Outline

1. Matplotlib and Seaborn - Libraries and Techniques

1. Promotional Video

This video explains the course.

2. Author Introduction

This video introduces you to the author.

3. What You Will Learn

This video explains what the course outcomes are.

4. Visualization Concepts

This video explains visualization concepts.

5. Introduction to Matplotlib

This video explains Matplotlib.

6. Creating Simple Plots Using Matplotlib

This video explains how to create simple plots using Matplotlib.

7. Creating Scatter Plots

This video explains creating scatter plots.

8. Creating Axis Limits

This video explains how to create axis limits.

9. Parameterizing Plots

This video explains parameterizing plots.

10. Creating Error Bars

This video explains creating error bars.

11. Plotting Histograms and Box Plots

This video explains plotting histograms and box plots.

12. Plotting 2D Histograms

This video explains plotting 2D histograms.

13. Marginal Histograms and Marginal Boxplots

This video explains marginal histograms and marginal boxplots.

14. Working with Subplots

This video explains working with subplots.

15. Stock Trend / Time Series Plot and Annotations

This video explains stock trend / time series plot and annotations.

16. Plotting Images and Clustering

This video explains plotting images and clustering.

17. Creating 2D Contour plots for 3D Data

This video explains creating 2D contour plots for 3D data.

18. Creating 3D Plots Including 3D Contours

This video explains creating 3D plots including 3D contours.

19. Stylesheets, rcParam, and Custom Stylesheets

This video explains stylesheets, rcParam, and custom stylesheets.

2. Advanced Visualizations Using Business Applications

1. Single and Multiple Bar Charts

This video explains single and multiple bar charts.

2. Area and Stacked-Area Charts

This video explains area and stacked-area charts.

3. Drawing Pie Charts

This video explains drawing pie charts.

4. Bubble Charts with Vectorization of Properties

This video explains bubble charts with vectorization of properties.

5. Plotting Regression Lines with OLS (ML)

This video explains plotting regression lines with OLS (ML).

6. Categorical Variables and Histograms (with EDA)

This video explains categorical variables and histograms.

7. Seaborn Boxplot, Violin plot, Categorical Scatterplot

This video explains Seaborn Boxplot, Violin plot, and Categorical Scatterplot.

8. Seaborn Slopeplots for Comparing Distributions

This video explains Seaborn Slopeplots for comparing distributions.

9. Dumbbell Plot for Category-Wise Value Movement

This video explains Dumbbell plot for category-wise value movement.

10. Creating Heatmaps

This video explains creating heatmaps.

11. Working with Pairplots

This video explains working with Pairplots.

12. Seasonal Trendcharts

This video explains seasonal Trendcharts.

13. Yearplot and Calendarplot for Color-Scaled Trends

This video explains Yearplot and Calendarplot for color-scaled trends.

14. Radarplot to Compare Scores of Multiple Parameters

This video explains Radarplot to compare scores of multiple parameters.

3. Working with the Beautiful and Powerful Bokeh Library

1. Introduction to Bokeh

This video introduces you to Bokeh.

2. Creating Simple and Multiple Line Plots

This video explains how to create simple and multiple line plots.

3. Customizing Your Plots

This video explains how to customize your plots.

4. Creating Bubble Plots - Vectorizing Your Plot

This video explains how to create bubble plots - vectorizing your plot.

5. Working with Layouts - Row / Column / Grid

This video explains working with layouts - row / column / grid.

6. Using the ColumnDataSource Object

This video explains using the ColumnDataSource object.

7. Applying Filters - IndexFilter, BooleanFilter, GroupFilter

This video explains applying filters - IndexFilter, BooleanFilter, GroupFilter.

8. Widgets - Dynamic Plot Controls

This video explains widgets - dynamic plot controls.

9. Plotting on a Google Map Using Google Map API

This video explains plotting on a Google Map using Google Map API.

10. Closing Notes

This video wraps up the course.

Course Images

Data Analytics Using Python Visualizations

By Packt

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