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Data Analytics: Python Visualizations
Rating: 4.5 out of 5(36 ratings)
607 students

Data Analytics: Python Visualizations

Enrich Data Science, ML and Analytics with powerful Visualisations using Matplatlib, Seaborn and Bokeh
Created byManas Dasgupta
Last updated 7/2021
English
English [Auto],

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

    Explore the fundamentals of data visualization, from choosing appropriate chart types to reveal patterns and insights, and using Matplotlib, Seaborn, and Bokeh for interactive, map-based visualizations.

  • 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

    Learn how to set x and y axis limits in Matplotlib, apply axis and tight options, and label plots with legends for multiple data series in a sinusoidal example.

  • Parameterizing Plots9:05
  • Creating Error Bars7:14

    Plot error bars in matplotlib to show ideal values and their variability, using 30 x values, y = 5x^2 + 5, with amplified random deviations.

  • Check your Understanding 1
  • Plotting Histograms and Box Plots23:23
  • Plotting 2D Histograms10:42
  • Marginal Histograms and Marginal Boxplots14:21

    Learn to create marginal histograms and marginal box plots beside a scatterplot using matplotlib, gridspec, and twin axes to reveal x and y distributions and their cumulative distributions.

  • Working with Subplots9:44
  • Stock Trend / Time Series Plot and Annotations8:18

    Visualize stock price trends with a line plot of dates and closing values and annotate key events using simple and advanced annotations to communicate insights clearly.

  • Plotting Images and Clustering18:51

    Learn to print handwritten digits from raw grayscale eight-by-eight pixel matrices and apply dimensionality reduction to two dimensions, then plot a scatterplot colored by target digits to reveal clustering.

  • Creating 2D Contourplots for 3D Data7:19
  • Creating 3D Plots including 3D Contours6:58
  • Stylesheets, rcParam and Custom Stylesheets6:24
  • Check your Understanding 2

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

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