Data Mining

Next Offering

Start Date: October 15, 2024
End Date: December 10, 2024

Data Mining is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion.

Data Mining is the second course in the sequence of the CPDA program. After learning how to analyze data statistically, students learn how to sort through large datasets to identify trends, patterns, and relationships and discover insights previously unknown and to leverage them in business operations. The course is delivered in 100% distance learning format and includes instructional material equivalent to a one semester credit hour class.  

Course Description

The Data Mining course provides students with a practical overview on how to use data to answer questions that leads to new methods for solving problems. The course focuses on the various steps involved in data mining starting with analyzing the data for completeness and accuracy before using a few standard algorithms to extract deeper insights. The mining methodology will touch structured and unstructured data sets and use the appropriate algorithms to explore them. In addition to data mining the course will explore ways in which the insights gained during mining are communicated to the intended audience and what happens after data mining. A project will be completed by students during the course providing the opportunity to apply all the techniques and concepts they have learned.

4 CEUs are granted upon successful completion of the course.

Students will learn to:

• Gain a practical understanding of how to use data mining techniques to learn more about how data can help identify new ways to solve business and technology problems.
• Understand how to begin the journey of solving a problem using data.
• Put the data you have to use by understanding its limitations, what it is and is not, and how to clean and use simple visualization.
• Use advanced techniques to mine the data using an example of structured and unstructured data set
• Communicate the insights you have from data mining and identify what is next.
• Select a project of your choice or choose from a set of projects presented to you and obtain a hands-on feel for what you learn week over week leading up to your final project report.

Instructor Introduction

Below is a video of the instructor for this course explaining what students will learn and can expect from this course. 

Prerequisites 

College level coursework in statistics is required. If you are pursuing the CPDA Certification it is required that students will complete Introductory Statistics for Data Analytics before taking this course. Please contact the program with questions or for clarification.   

Students must have experience using Python before taking this course. Once a student is enrolled in the Introductory Statistics course, or the Data Mining course, they will also be given access to a preparatory course on Python created by the instructors of our Data Mining and Machine Learning courses. This Python training course is free and students can complete it at their own pace. It is highly recommended that every student complete the Python training course if they wish to be successful in the Data Mining course and if they do not have experience with Python.  

Expected Time Commitment to Complete this Course

Each course is equivalent to a one semester credit hour class with each class consisting of approximately 40 hours of class time (12-13 hours of recorded faculty lectures and 23-24 hours of additional course work). Each course is seven weeks in length. Each week, there is 5.7 hours of combined class time (40 hrs / 7 weeks).

The average student should allow a 2:1 study-to-class-time ratio to complete the course. We recommend students plan to study two hours for each one hour of class time. This equates to 11-12 hours each week to complete all course work. (5.7 hrs X 2 = 11-12 hrs). Based on a person's own personal strengths and experience, you should increase or decrease the ratio. 

Course Grading

The course is graded pass/fail. Students must receive an 80% or better on all graded items to pass and receive the certificate of completion for the course.