National CAE Designated Institution
  • Classroom
  • Online, Instructor-Led

This course provides an introduction to data analytics, which is defined as the extensive use of data, statistical and quantitative analysis, predictive and exploratory models to drive decisions and actions. Students will learn basic concepts and essential algorithms for data analytics, including data cleaning, association rule mining, data warehousing, predictive modeling, clustering, and text mining. Students will also learn to use mainstream tools such as Weka, Orange Data Mining, MetaMap, and Tableau to solve data analytics problems with real-world datasets. Further, the students will evaluate the data analytics models, interpret the results, and understand their limitations. The students will form groups, conduct a project of data analytics, and use the tools introduced in the course to tackle the problem. This course is appropriate for students with basic knowledge and skills in database management systems. Prior programming skills are helpful but not required.

Learning Objectives

  1. Define the critical issues and theoretical underpinnings of User Experience (UX) design;
  2. Establish requirements for UX design concepts using techniques such as persona development, task description, and use cases;
  3. Develop alternatives for UX design concepts using techniques such as competitive analysis, scenario-based design, and story-boarding;
  4. Construct UX design artifacts using techniques such as flow diagrams, wire-framing, and paper prototypes;
  5. Evaluate UX design artifacts using techniques such as representative user testing, inspection methods, and expert analysis;
  6. Apply feedback from UX evaluations to improve information interfaces through a process of iterative, user-centered design.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Competency Areas