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Main
UQU Programs
Data Science

Special Topics in Data Science

DS4104
Two hours English

Special Topics in Data Science

Special Topics In Data Science
DS3402 Data Modelling
Two hours Two requisites
1 Introduction to Interpretable Machine Learning
2 Interpretability
3 Datasets
4 Interpretable models
5 Model-Agnostic methods
6 Example-based explanations
7 Global Model-Agnostic methods
8 Local Model-Agnostic methods
9 Neural Network Interpretation
10 The future of machine learning and interpretability
11 Review
1.1 Mapped to: K1

Understand and utilize sophisticated computational techniques, such as machine learning algorithms and statistical assessments, to analyse complex datasets.

Teaching Strategy Lectures covering foundation concepts relating to the field of DS Audio visual presentation including some scientific movies for special topics in DS Flipped classroom where issues relating to DS will be discussed and explored. Case study. The course will make effective use of case studies to further enhance the students understanding of presented concepts. Reading (Research Papers, Book Chapters). Debriefing: Usually conducted at the conclusion of a lesson, debriefing allows students to condense and coalesce their knowledge and information as a group or whole class.
Assessment Methods Online quizzes. Written exams (midterm and final)
1.2 Mapped to: K2

Recognize and assess the influence of recent tech developments in data science, including AI, big data tools, and data management frameworks.

Teaching Strategy Lectures covering foundation concepts relating to the field of DS Audio visual presentation including some scientific movies for special topics in DS Flipped classroom where issues relating to DS will be discussed and explored. Case study. The course will make effective use of case studies to further enhance the students understanding of presented concepts. Reading (Research Papers, Book Chapters). Debriefing: Usually conducted at the conclusion of a lesson, debriefing allows students to condense and coalesce their knowledge and information as a group or whole class.
Assessment Methods Online quizzes. Written exams (midterm and final)
1.3 Mapped to: K2

Examine and discuss about new trends in data science, addressing ethical considerations, data privacy issues, and the influence of data on decision-making procedures.

Teaching Strategy Lectures covering foundation concepts relating to the field of DS Audio visual presentation including some scientific movies for special topics in DS Flipped classroom where issues relating to DS will be discussed and explored. Case study. The course will make effective use of case studies to further enhance the students understanding of presented concepts. Reading (Research Papers, Book Chapters). Debriefing: Usually conducted at the conclusion of a lesson, debriefing allows students to condense and coalesce their knowledge and information as a group or whole class.
Assessment Methods Online quizzes. Written exams (midterm and final)
1.4 Mapped to: K1

Utilize ideas from multiple fields, including statistics, computer science, and specialized knowledge, to improve data science applications.

Teaching Strategy Lectures covering foundation concepts relating to the field of DS Audio visual presentation including some scientific movies for special topics in DS Flipped classroom where issues relating to DS will be discussed and explored. Case study. The course will make effective use of case studies to further enhance the students understanding of presented concepts. Reading (Research Papers, Book Chapters). Debriefing: Usually conducted at the conclusion of a lesson, debriefing allows students to condense and coalesce their knowledge and information as a group or whole class.
Assessment Methods Online quizzes. Written exams (midterm and final)
2.1 Mapped to: S1

Expertise in evaluating and understanding complex data through sophisticated statistical methods.

Teaching Strategy Reading around the DS topics, including core materials, materials introduced via lectures and the module website, and any relevant magazine and journal articles;
Assessment Methods Written exams (midterm and final).
2.2 Mapped to: S1, S4

Capability to apply and assess machine learning algorithms for predictive purposes.

Teaching Strategy Reading around the DS topics, including core materials, materials introduced via lectures and the module website, and any relevant magazine and journal articles;
Assessment Methods Written exams (midterm and final).
2.3 Mapped to: S2

Robust analytical and critical reasoning abilities to tackle practical issues through data-driven methods.

Teaching Strategy Reading around the DS topics, including core materials, materials introduced via lectures and the module website, and any relevant magazine and journal articles;
Assessment Methods Written exams (midterm and final).
Assessment
Weight
Quizzes
10%
Homework
20%
Midterm exam
20%
Final Exam
50%
Total
100%