Big Data for Agriculture
Course Code
BA4121
Number of Credits
3
Semester
Course Type
Study Material
| Study Material | Depth |
|---|---|
| Big Data Overview | Express |
| Data Analytics Overview | Express |
| Data Analytics Basics using Microsoft Excel | Expert |
| Data Analysis Method: Clustering using Microsoft Excel & R | Expert |
| Data Analysis Method: Regression using Microsoft Excel & R | Expert |
| Agricultural Data Analysis Case Study | Expert |
| Data Analysis Method: Classification using R | Expert |
| Big Data Applications: Hadoop | Expert |
| Big Data Case Study for Agricultural Engineering Applications | Expert |
Graduate Learning Outcomes (GLO) carried by the course
| CPMK Code | Course Learning Outcomes Elements (CLO) |
|---|---|
| CPMK 1 | Able to apply knowledge of information technology to gain a thorough understanding of big data principles for agriculture. |
| CPMK 2 | Able to apply bioscience and agricultural science principles to gain a thorough understanding of big data principles in agriculture. |
| CPMK 3 | Able to explain the roles, responsibilities and ethics of the engineering profession. |
| CPMK 4 | Able to make decisions based on professional ethical responsibilities in solving big data problems in agriculture. |
| CPMK 5 | Able to explain the impact of agricultural big data on community welfare, environmental security, and sustainable development. |
| CPMK 6 | Able to learn independently and continuously through lectures and experiments. |
| CPMK 7 | Able to collect information from various sources efficiently and effectively. |
| CPMK 8 | Able to discuss current issues related to engineering. |
Learning Method
- Lecture, Group Discussion, Problem Based Learning/Case Study
Learning Modality
- Synchronous Offline, Asynchronous Online/Blended
Assessment Methods
- Quiz, Group Assignment, Mid-Term Exam, Final Exam