Applied Plotting, Charting & Data Representation in Python

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library

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Key Points About This Course

Duration: X Days
Time: 9.00am-5.00pm
Public Class Fee: RM X,XXX.XX
Virtual Class Fee: RM X,XXX.XX
HRDF Claimable

Course Overview

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data.

This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

What You Will Learn

  • Describe what makes a good or bad visualization
  • Identify the functions that are best for particular problems
  • Understand best practices for creating basic charts
  • Create a visualization using matplotlb

Skills You Will Gain

  • Python Programming
  • Data Virtualization
  • Data Visualization (DataViz)
  • Matplotlib

Course Content

Part 1: Principles of Information Visualization

In this module, you will get an introduction to principles of information visualization.

  • Tools for Thinking about Design (Alberto Cairo)
  • Graphical heuristics: Data-ink ratio (Edward Tufte)
  • Graphical heuristics: Chart junk (Edward Tufte)
  • Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)
  • The Truthful Art (Alberto Cairo)

Part 2: Basic Charting

In this module, you will delve into basic charting.

  • Matplotlib Architecture
  • Basic Plotting with Matplotlib
  • Scatterplots
  • Line Plots
  • Bar Charts
  • Dejunkifying a Plot

Part 3: Charting Fundamentals

In this module you will explore charting fundamentals.

  • Subplots
  • Histograms
  • Box Plots
  • Heatmaps
  • Animation
  • Interactivity

Part 4: Applied Visualizations

In this module, then everything starts to come together.

  • Plotting with Pandas
  • Seaborn
  • Becoming an Independent Data Scientist

Training Schedule

15 – 17 Feb 2021
12 – 14 Apr 2021
8 – 10 Jun 2021
2 – 5 Aug 2021
25 – 27 Oct 2021
13 – 15 Dec 2021

  • Public Class Training

  • Date Format: DD slash MM slash YYYY
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  • Participant List

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  • Examination (Optional)

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Call Us : 03-21165778