Course Details
Course Code: ANLY600 Course ID: 4875 Credit Hours: 3 Level: Graduate
This course covers data mining using the R programming language. It offers hands on experience approach through a learn-by-doing-it strategy. It further integrates data mining topics with applied business analytics to address real world data mining cases. It continues the examination of the role of “Data Mining in R”, and review statistics techniques in prescriptive analytics, and some predictive analytics. Additionally, some standard techniques and excel functions will be also covered.
Course Schedule
| Registration Dates | Course Dates | Start Month | Session | Weeks |
|---|---|---|---|---|
| 07/25/2022 - 12/30/2022 | 01/02/2023 - 02/26/2023 | January | Winter 2023 Session B | 8 Week session |
Current Syllabi
After successfully completing this course, you will be able to
- LO 1: Demonstrate advanced knowledge of data mining concepts and techniques.
- LO 2: Apply the techniques of clustering, classification, association finding, feature selection and visualization on real world data
- LO 3: Determine whether a real world problem has a data mining solution
- LO 4: Apply data mining software and toolkits in a range of applications
- LO 5: Set up a data mining process for an application, including data preparation, modelling and evaluation
- LO 6: Demonstrate knowledge of the ethical considerations involved in data mining.
| Name | Grade % |
|---|---|
| Discussions | 28.00 % |
| Week 1: Discussion | 3.50 % |
| Week 2: Discussion | 3.50 % |
| Week 3: Discussion | 3.50 % |
| Week 4: Discussion | 3.50 % |
| Week 5: Discussion | 3.50 % |
| Week 6: Discussion | 3.50 % |
| Week 7: Discussion | 3.50 % |
| Week 8: Discussion | 3.50 % |
| Assignments | 72.00 % |
| Week 2 Assignment | 14.40 % |
| Week 3 Assignment | 14.40 % |
| Week 5 Assignment | 14.40 % |
| Week 6 Assignment | 14.40 % |
| Week 7 Assignment | 14.40 % |
After successfully completing this course, you will be able to
- LO 1: Demonstrate advanced knowledge of data mining concepts and techniques.
- LO 2: Apply the techniques of clustering, classification, association finding, feature selection and visualization on real world data
- LO 3: Determine whether a real world problem has a data mining solution
- LO 4: Apply data mining software and toolkits in a range of applications
- LO 5: Set up a data mining process for an application, including data preparation, modelling and evaluation
- LO 6: Demonstrate knowledge of the ethical considerations involved in data mining.
| Book Title: | Data Mining for Business Analytics: Concepts, Techniques, and Applications in R *Note: the price provided is for the VitalSource eBook |
| ISBN: | 9781118879368 |
| Publication Info: | Wiley |
| Author: | Shmueli, G. Peter C. Bruce; Inbal Yahav; Nitin R. Patel; |
| Unit Cost: | $145.39 |
| Electronic ISBN: | 9781118879368 |
| Electronic Unit Cost: | $145.39 |
| Book Title: | Data Mining and Business Analytics with R (Ebook available through the APUS Online Library) |
| ISBN: | 9781118447147 |
| Publication Info: | Wiley Lib |
| Author: | Johannes Ledolter |
| Unit Cost: | $104.60 |