STIS 3053 || Undergraduate
Synopsis:
Predictive analytics is the branch of data science and machine learning that concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior. This lesson explains several predictive models for forecasting. It explores foundational concepts in data management, processing, statistical computing, and dynamic visualization using modern programming tools and open source software. Concepts, ideas, and protocols are illustrated through examples of real observational, simulated and research-derived datasets. Some prior quantitative experience in programming, calculus, statistics, mathematical models, or linear algebra will be necessary.
Learning Outcomes:
Upon completing this course, you will be able to:
Course Information:
Coordinator: Dr Azizi Ab Aziz
Level: Undergraduate (STIS 3053)
E-mail : aziziaziz [at] uum [dot] edu [dot] my
Time : 1130-1320
Location: Monday (SOC Lab 1) & Thursday (DKG 4/3)
UUM Digital Learning Platform: UUM Online Learning
Announcement :
Class will resume on 27th April 2020, via WEBEX online meeting platform.
Syllabus & Lecture Notes:
Predictive analytics is the branch of data science and machine learning that concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior. This lesson explains several predictive models for forecasting. It explores foundational concepts in data management, processing, statistical computing, and dynamic visualization using modern programming tools and open source software. Concepts, ideas, and protocols are illustrated through examples of real observational, simulated and research-derived datasets. Some prior quantitative experience in programming, calculus, statistics, mathematical models, or linear algebra will be necessary.
Learning Outcomes:
Upon completing this course, you will be able to:
- Use data and predictive analytics to inform the decision-making process.
- Select and apply models appropriate for the nature of the data and the decision to be made.
- Assess model feedback and make adjustments to produce desired outcomes.
Course Information:
Coordinator: Dr Azizi Ab Aziz
Level: Undergraduate (STIS 3053)
E-mail : aziziaziz [at] uum [dot] edu [dot] my
Time : 1130-1320
Location: Monday (SOC Lab 1) & Thursday (DKG 4/3)
UUM Digital Learning Platform: UUM Online Learning
Announcement :
Class will resume on 27th April 2020, via WEBEX online meeting platform.
Syllabus & Lecture Notes:
- Introduction to Predictive Analytics
- Processes in Predictive Analytics
- Performance Evaluation
- Statistical Based Methods
- Distance-based Methods
- Tree-based Methods
- Adaptive Learning Methods
- Time-Series Methods
- Advancement & Issues in Predictive Analytics
- Assignment / Lab Work
- Assignment #1 (submission date 25th Feb 2020)
- Assignment #2 (submission date 23rd March 2020)
- Assignment #3 (submission date 14th May 2020)
- Assignment #4 (submission date 31st May 2020)
- Assignment #5 (submission date 24th June 2020)
- Individual Project (submission date 24th July 2020)
- Machine Learning Tool (WEKA)
- Data Mining Tool (Orange)
- Microsoft Azure Machine Learning Studio (MS Azure ML)
- Installing Python & Related Libraries (Python)