SKIH 2013 || Undergraduate
Synopsis:
This course is an introduction to statistical and AI-based data analytics. It covers aspects on how to extract actionable, non-trivial knowledge from a massive number of data sets. This class will focus both on the data pre-processing, visualization, descriptive and predictive analytics based on computing software tools and programming techniques. Students will implement data analytics algorithms and execute them an open-source platform. Also, this course will cover the main standard supervised and unsupervised models and will introduce improvement techniques on the model side
Learning Outcomes:
Upon completing this course, you will be able to:
Course Information:
Coordinator: Assoc. Professor Dr. Azizi Ab Aziz
Level: Undergraduate (SKIH 2013)
E-mail : aziziaziz [at] uum [dot] edu [dot] my
Time : 0830-1120
Location: Monday (SOC Lab 2) & Wednesday (DKG 4/4)
UUM Digital Learning Platform: UUM Online Learning
Announcement :
Syllabus & Lecture Notes:
Assignments/ Project:
This course is an introduction to statistical and AI-based data analytics. It covers aspects on how to extract actionable, non-trivial knowledge from a massive number of data sets. This class will focus both on the data pre-processing, visualization, descriptive and predictive analytics based on computing software tools and programming techniques. Students will implement data analytics algorithms and execute them an open-source platform. Also, this course will cover the main standard supervised and unsupervised models and will introduce improvement techniques on the model side
Learning Outcomes:
Upon completing this course, you will be able to:
- Use data and 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: Assoc. Professor Dr. Azizi Ab Aziz
Level: Undergraduate (SKIH 2013)
E-mail : aziziaziz [at] uum [dot] edu [dot] my
Time : 0830-1120
Location: Monday (SOC Lab 2) & Wednesday (DKG 4/4)
UUM Digital Learning Platform: UUM Online Learning
Announcement :
Syllabus & Lecture Notes:
- Introduction to Data Analytics
- Data Analytics Methodology
- Data Preparation
- Descriptive Analytics
- Statistical & Probability Distributions Analytics
- Exploratory Data Analytics
- Association Rules
- Predictive Analytics
- Statistical-based
- Machine Learning-based
- Prescriptive Analytics
- Selected Case Study
- Advanced Data Analytics, Issues and Trends
Assignments/ Project:
- Assignment / Lab Work
- Assignment #1 (submission date dd/mm/yy)
- Assignment #2 (submission date dd/mm/yy)
- Assignment #3 (submission date dd/mm/yy0)
- Individual Project (submission date dd/mm/yy
- Machine Learning Tool (WEKA)
- Data Mining Tool (Orange)
- Microsoft Azure Machine Learning Studio (MS Azure ML)
- Installing Python & Related Libraries (Python)