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

This course provides an intro to data analytics, which uses statistical and quantitative analysis, predictive and exploratory models to drive decisions and actions from data. Students learn basic concepts and algorithms for data exploration, data cleaning, predictive modeling, clustering, and text mining. Students will also learn how to use tools to evaluate data models and interpret the results.

Learning Objectives

  1. Describe and define basic concepts in data mining and analytics.
  2. Describe and define the basic procedure in data mining and analytics.
  3. Explain and use prominent algorithms in data analytics, including predictive analytics, clustering, association rule mining, text mining, and visual analytics.
  4. Explain the process of inferring knowledge and insights from heterogeneous data sources.
  5. Choose appropriate tools and algorithms to solve the data analytics task.
  6. Use data analytics tools with a graphical user interface (e.g., Orange Data Mining) to perform basic data analytics tasks such as data cleaning, data visualization, clustering, association rule mining, predictive modeling, and text mining.
  7. Use Python in Jupyter Notebook to perform basic data analytics tasks such as data cleaning, data visualization, and predictive modeling.
  8. Interpret the results of data analytics while acknowledging their limitations.

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