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Data Mining and Analytics - Undergrad
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.
Provider Information
Florida State University
600 W College Avenue
Tallahassee, FL 32306
Course Overview
LIS2780 Database Concepts, LIS3201 Research and Data Analytics in IT
600 W College Avenue
Tallahassee, FL 32306
Learning Objectives
- Describe and define basic concepts in data mining and analytics.
- Describe and define the basic procedure in data mining and analytics.
- Explain and use prominent algorithms in data analytics, including predictive analytics, clustering, association rule mining, text mining, and visual analytics.
- Explain the process of inferring knowledge and insights from heterogeneous data sources.
- Choose appropriate tools and algorithms to solve the data analytics task.
- 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.
- Use Python in Jupyter Notebook to perform basic data analytics tasks such as data cleaning, data visualization, and predictive modeling.
- 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
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.