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
- Classroom
- Virtual Class
- On-Demand Video
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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 |
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