Data Analytics: Python Visualizations

Name: Data Analytics: Python Visualizations
Rating: 4.5 (36 reviews)

Enrich Data Science, ML and Analytics with powerful Visualisations using Matplatlib, Seaborn and Bokeh

Created byManas Dasgupta

Last updated 7/2021

English

What you'll learn

In depth coverage of Matplotlib, Seaborn and Bokeh Visualization Libraries.
Easy, step by step explanations with code to draw over 20 different kinds of Charts and Graphs using Python.
Extensive amount of Python - Matplotlib/Seaborn/Bokeh code used in the course are attached as downloadable resources for you to try out while you learn.
Use of Data Analytics Techniques / Exploratory Data Analysis (EDA) using several Data Generation and Manipulation Methods.
Extensive coverage of NumPy and Pandas Data capabilities using Python.
Learn the art of presenting Data in the form of Powerful, Innovative and Intuitive Visualisations that your stakeholders will love.
Application of Business and real-life Scenatios to create Visualisations.

Course content

3 sections • 42 lectures • 6h 24m total length

Author Introduction1:13
What will you Learn3:15
Visualization Concepts7:17
Introduction to Matplotlib20:03
Creating Simple Plots using Matplotlib20:21
You may download the attached Notebook (.ipynb) file. This contain all the Matplotlib and Seaborn code used in the lectures.
Creating Scatter Plots6:28
Creating Axis Limits7:34
Parameterizing Plots9:05
Creating Error Bars7:14
Check your Understanding 1
Plotting Histograms and Box Plots23:23
Plotting 2D Histograms10:42
Marginal Histograms and Marginal Boxplots14:21
Working with Subplots9:44
Stock Trend / Time Series Plot and Annotations8:18
Plotting Images and Clustering18:51
Creating 2D Contourplots for 3D Data7:19
Creating 3D Plots including 3D Contours6:58
Stylesheets, rcParam and Custom Stylesheets6:24
Check your Understanding 2

Single and multiple Bar charts15:02
Area and Stacked-Area Charts8:16
Drawing Pie Charts10:20
Bubble Charts with Vectorisation of Properties8:29
Plotting Regression Lines with OLS (ML)11:50
Categorical Variables and Histograms (with EDA)11:53
Check your Understanding 3
Seaborn Boxplot, Violineplot, Categorical Scatterplot10:16
Seaborn Slopeplots for Comparing Distributions9:14
Dumbellplot for Category-wise Value Movement7:57
Creating Heatmaps8:01
Working with Pairplots6:56
Seasonal Trendcharts6:04
Yearplot and Calendarplot for Color-Scaled Trends8:36
Radarplot to Compare Scores of Multiple Parameters8:12
Check your Understanding 4

Introduction to Bokeh8:32
Creating Simple and Multiple Line Plots6:16
You may download the attached Notebook (.ipynb) file. This contain all the Bokeh code used in the lectures.
Customising your Plots2:47
Creating Bubble Plots – Vectorising your Plot4:56
Working with Layouts – Row/Column/Grid6:27
Using the ColumnDataSource Object3:56
Applying Filters – IndexFilter, BooleanFilter, GroupFilter13:55
Widgets – Dynamic Plot Controls9:56
Plotting on a Google Map using Google Map API7:06
Check your Understanding 5
Closing Notes0:44

Requirements

Basic understanding of Python will be helpful, however not mandatory.

Description

If you are working on Data Science projects and want to create powerful Visualization and Insights as outcome from your projects, this course is for you!!!

If you are working on Machine Learning Projects and want to find patterns and insights from your Data on your way to building Models, this course is for you!!!

If you are a Business Analyst or Functional Analyst and want to build powerful Visualizations for your stakeholders, this course is for you!!

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This course is exclusively focused on explaining how to build fantastic Visualizations using Python. Covers more than 20 types of Visualizations using the most popular Python Visualization Libraries Matplotlib, Seaborn and Bokeh. It covers Data Analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.

This course takes a holistic approach towards teaching Visualizations:

- Take real-life, business scenarios and raw data to go through detailed Exploratory Data Analysis (EDA) techniques to prepare your raw data to suit the appropriate Visualization needs.

- Data Analytics and Exploratory Data Analysis Techniques using multiple different data structures using Numpy and Pandas libraries.

- Explain Chart/Graph types, customization/configuration and vectorization techniques.

- Throughout the course, extensive amount of Code demo along with concepts as a balanced approach to teaching.

- Every concept is taught by going deeper into foundational techniques and deeper customizations on Visualizations.

Extensive Quizzes are infused at logical points to validate the learning effectiveness.

[NOTE: All the code used in the Lectures are attached as downloadable resources. You may download them and try out while going through the lectures.]

Who this course is for:

Python, Machine Learning Developers
Data Scientists
Data Analysts
Big Data Professionals
Business Analysts
Leaders, Managers, anyone whose job involves presenting Data in the form of Visuals
Techno-Functional Analysts
Developers
Architects
Systems Analysts
Anyone who is in a role in handling and managing Data

Data Analytics: Python Visualizations

What you'll learn

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Course content

Matplotlib and Seaborn – Libraries and Techniques18 lectures • 3hr 9min

Advanced Visualisations using Business Applications14 lectures • 2hr 11min

Working with the Beautiful and Powerful Bokeh Library10 lectures • 1hr 5min

Requirements

Description

Who this course is for: