This course equips a learner with skills and knowledge on analyzing data and being able to perform Visual Analytics.

By the end of the course the Learner should be able to:

1. Identify the key terminologies, steps, and frameworks used in the data analytics process.
2. Explain the theories of human visual perception underpinning information visualization and visual analytics.
3. Apply visualization design principles to construct appropriate information visualizations from existing datasets.
4. Evaluate the suitability of visualization tools and techniques for analyzing large and fast-moving datasets in a big data context.

Course Content

Essentials of Data Analytics

Essentials of data analytics and the corresponding terminologies.
Define data analytics, data mining, business intelligence, and big data,
and describe developments in computing enabling organizations to adopt a data-driven approach to decisions and operations.

Steps Involved in the Analytics Process

Data analytics process, Cross-Industry Standardized Process for Data Mining,
pitfalls of managing data analytics projects, and ways to improve business performance
and inform decisions for management, marketing, and other business application areas.

Data Analytics in Big Data

Fundamental principles of data science and business analytics that form the basis for
data mining processes, algorithms, and systems.
Utilize software tools for both business intelligence.

Information Visualization and Visual Analytics

Theories of human visual perception and cognition,
understanding information visualization needs and use,
and understanding the data.

Transforming Information into Visualizations

Types of information visualizations,
identifying and evaluating information visualization and visual analysis tools.

Case Studies and Critical Issues

Case studies in the application of information visualization and visual analytics,
critical issues and limitations of Information Visualization and Visual Analytics,
and visualization in a big data world including issues and techniques in visualizing
large or fast-moving datasets.