Practical Application to Advanced Analytics
Start Date: February 27, 2019
End Date: April 22, 2019
Practical Application to Advanced Analytics is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. The course is taught by faculty from the College of Engineering. The course is delivered in 100% distance learning format and includes instructional material equivalent to a one semester credit hour class.
This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion. Students must take Foundations of Statistics and Data Mining before this course.
This course builds on the CPDA Data Mining course. Students will dive deeper into this subject and practice creating and evaluating the most common types of modeling: Decision Trees, Random Forests and Regression models. Students will have the opportunity to learn about “big data” analytics by exploring text analytics algorithms and their use in modeling, as well as be introduced to cognitive analytic capabilities. Machine Learning will be explored throughout the course as it applies to all these techniques.
4 CEUs are granted upon successful completion of the course.
You will learn:
- How to use different types of decision trees and random forests, types of regression models and when to use them.
- How text analytics techniques work and some common uses of the skill.
- How to leverage output from text analytics within classification models and why this is important.
- While machine learning has been in use for decades, why it is accelerating the success of analytics today.
- Machine learning algorithms and how to apply them within common predictive models.
- The components of a cognitive system and when the use of it applies and does not apply to a data analytic problem.
- How to apply the use of chatbot technology with text analytics using available cloud technology.
College level coursework in statistics is required. If you are pursuing the CPDA Certification and do not already have that background, it is expected that students will complete Foundation of Statistics before taking this course and also Data Mining. Please contact the program with questions or for clarification.
Students will be required to learn the R software package prior to starting the course (https://www.r-project.org/).
Free training in R software that will prepare you for this course can be found online at:
Students are also required to learn Structured Query Language (SQL). Free online training that will prepare you for this course can be found at:
Click Here to learn more about how this course is delivered 100% online!
Expected Time Commitment to Complete this Course
Each course is equivalent to a one semester credit hour class. Therefore each class consists of approximately 40 hours of class time that includes 12-13 hours of recorded faculty lectures and 23-24 hours of additional course work. Each course is seven weeks in length, so 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. This means you should 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.
Cancellations and Refunds
A full refund minus a $50 administrative fee will be made if cancellation is received three weeks prior to the start of the course. No refunds within three weeks of the course start date.
Course Offering Dates
Each course offering in this program is faculty lead therefore it operates with a specific start date and end date. Students must complete each course during the specific time frame. Access to the online course and materials is removed when the course ends.