Data Visualisation and Visual Analytics

Duration

11 - 20 hours

Level

Learner
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Course overview

This course in data exploration and visualization covers cleaning and structuring datasets, and choosing suitable methods for visualizing them. It also provides theoretical knowledge of the underpinning descriptive statistics and the basics of human perception for cognition.

Learners will acquire skills in data exploration and visualization. By the end of the course they will be able to take raw data sets, clean them, structure them and choose suitable methods for visualizing them. They will also acquire theoretical knowledge of the underpinning descriptive statistics and the basics of human perception for cognition.

Who is this course for?

This is an introductory course on Data Visualization using Python, suitable for anyone with basic experience of Python programming.

Learning outcomes

By the end of this course you will be able to:

  • Wrangle data (cleaning, integration) throughout a practical data pipeline (extract, transform, load phases)
  • Aggregate data from large data sets
  • Generate descriptive statistics
  • Create numerical, categorical, geographic and hierarchical data visualizations
  • Critically compare visualization techniques for their appropriateness to real data sets
  • Create an applicable visualization pipeline, which takes into account human perception and cognition

License

This course is released under a CC BY 4.0 license.
It was designed by Professor Nick Holliman at Newcastle University.

Details

1. Introduction

Module Name

Topic

Section 1.1 What is data visualisation?
Section 1.2 Pandas: quick start Series and Dataframe
Section 1.3 Data Wrangling: quick start example
Section 1.4 Datetime: manipulating dates and times in python
2. Data types: Nominal and ordinal categorical data

Module Name

Topic

Section 2.1 Categorical data
Section 2.2 Categorical data and bar charts
Section 2.3 Categorical ordinal data and bar charts
Section 2.4 Grouping and facets to compare across categories
3. Data types: Numerical data

Module Name

Topic

Section 3.1 Data types: Numerical data
Section 3.2 Numerical data: Scatter plots
Section 3.3 Numerical data: Line graphs
Section 3.4 Numerical data: Correlation and regression
4. Information theory and human vision

Module Name

Topic

Section 4.1 Information theory and visualisation
Section 4.1 The human visual system
5. Colour and design principles

Module Name

Topic

Section 5.1 Colour in visualization
Section 5.2 Principles of graphic design
6. Visual decision making

Module Name

Topic

Section 6.1 Decision making: Human cognition and decisions
Section 6.2 Decision making: Four panels method
Section 6.3 Decision making: Visualization quality judgements
7. Financial data visualisation

Module Name

Topic

Section 7.1 Financial data: Markets and data sources
Section 7.2 Financial data: Basic visualization of findata
Section 7.3 Financial data: Analytics that help classify trends
8. Connected data: Networks

Module Name

Topic

Section 8.1 Connected data: trees, graphs / networks
Section 8.2 Connected data: visualizing networks
Section 8.3 Connected data: graph layout for visualization
9. Geographic maps

Module Name

Topic

Section 9.1 Coordinate systems and projections of the globe
Section 9.2 Mapping using Cartopy
Section 9.3 Mapping using Folium

Instructors

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