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SCHOOL OF COMPUTER SCIENCES
Master of Science
(Data Science and Analytics) [MSc (DSA]
2020/2021](https://image.slidesharecdn.com/fetch-201224150238/85/Fetch-1-320.jpg)
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3.3 Master of Science (Data Science and Analytics) [MSc(DSA)]
3.3.1 Introduction and Objective
The goal of this programme is to produce workforce/human resource in the field of Big
Data Analytics who are capable of making decisions based on the availability of
comprehensive data. Therefore, the objective of this programme is to produce
graduates who:
have a deep understanding of the core concepts, practices and tools in the domain
of Data Science and Analytics.
have the knowledge and skills in collecting and consolidating data, data modelling
and advanced analysis of data which can be applied in jobs across various sectors,
especially the service, business, marketing, manufacturing and healthcare sectors.
can act as high-power data (open source or industry standards) users in a variety of
industries as well as have social, ethical and effective communication skills, and
good leadership attributes.
are innovative in the application and use of big data analytic tools in various fields
or as researchers who are capable of making decisions based on the availability of
comprehensive data as well as able to realise lifelong learning and pursue studies
at a higher level in cross-disciplinary research involving ICT.
3.3.2 Programme Outcomes
At the end of this programme, the students will be able to:
(a) Apply the core knowledge of Data Science and Analytics together with at least
one focus area namely Business Analytics or Multimodal Analytics; [Knowledge]
(b) Design solid data science and analytics solutions using theoretical knowledge,
abstraction, analytical thinking and scientific approach; [Technical Skill, Practical
Skill, Psychomotor]
(c) Formulate and make decisions using scientific consideration and management
in planning and consultancy based on application of high quality data science
and analytics tools; [Thinking Skill and Scientific Approach]
(d) Communicate effectively in the context of analysing, presenting and negotiating
in data science and analytics practices; [Communication Skill]
(e) Implement tasks in a team in data science and analytics practices including in
decision making and planning; [Social and Responsibility Skill]
(f) Demonstrate ethical attributes and professionalism in data science and analytics
practices; [Professionalism, Value, Attitude and Ethics]
(g) Demonstrate the abilities to search and manage information, adapt to current
changes, realise life-long learning and proceed to higher level studies; [Life-long
Learning and Information Management]
(h) Venture into technopreneurship and practice good management such as in
making decisions and planning in data science and analytical projects;
[Management and Entrepreneurship Skill]
(i) Demonstrate good leadership quality when participating, representing and
leading data science and analytics projects and also community projects;
[Leadership Quality]](https://image.slidesharecdn.com/fetch-201224150238/85/Fetch-2-320.jpg)
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- 1. 1 SCHOOL OF COMPUTER SCIENCES Master of Science (Data Science and Analytics) [MSc (DSA] 2020/2021
- 2. 2 3.3 Master of Science (Data Science and Analytics) [MSc(DSA)] 3.3.1 Introduction and Objective The goal of this programme is to produce workforce/human resource in the field of Big Data Analytics who are capable of making decisions based on the availability of comprehensive data. Therefore, the objective of this programme is to produce graduates who: have a deep understanding of the core concepts, practices and tools in the domain of Data Science and Analytics. have the knowledge and skills in collecting and consolidating data, data modelling and advanced analysis of data which can be applied in jobs across various sectors, especially the service, business, marketing, manufacturing and healthcare sectors. can act as high-power data (open source or industry standards) users in a variety of industries as well as have social, ethical and effective communication skills, and good leadership attributes. are innovative in the application and use of big data analytic tools in various fields or as researchers who are capable of making decisions based on the availability of comprehensive data as well as able to realise lifelong learning and pursue studies at a higher level in cross-disciplinary research involving ICT. 3.3.2 Programme Outcomes At the end of this programme, the students will be able to: (a) Apply the core knowledge of Data Science and Analytics together with at least one focus area namely Business Analytics or Multimodal Analytics; [Knowledge] (b) Design solid data science and analytics solutions using theoretical knowledge, abstraction, analytical thinking and scientific approach; [Technical Skill, Practical Skill, Psychomotor] (c) Formulate and make decisions using scientific consideration and management in planning and consultancy based on application of high quality data science and analytics tools; [Thinking Skill and Scientific Approach] (d) Communicate effectively in the context of analysing, presenting and negotiating in data science and analytics practices; [Communication Skill] (e) Implement tasks in a team in data science and analytics practices including in decision making and planning; [Social and Responsibility Skill] (f) Demonstrate ethical attributes and professionalism in data science and analytics practices; [Professionalism, Value, Attitude and Ethics] (g) Demonstrate the abilities to search and manage information, adapt to current changes, realise life-long learning and proceed to higher level studies; [Life-long Learning and Information Management] (h) Venture into technopreneurship and practice good management such as in making decisions and planning in data science and analytical projects; [Management and Entrepreneurship Skill] (i) Demonstrate good leadership quality when participating, representing and leading data science and analytics projects and also community projects; [Leadership Quality]
- 3. 3 The following table provides the matrix for programme outcomes of this programme. No. Course Code/Unit Course Title Programme Outcomes Knowledge TechnicalSkill/Practical Skill/Psychomotor ThinkingSkilland ScientificApproach CommunicationSkill SocialandResponsibility Skill Professionalism,Value, AttitudeandEthics LifelongEducationand InformationManagement Managementand EntrepreneurshipSkill LeadershipSkill CORE COURSES 1. CDS501 Principles and Practices of Data Science and Analytics 2. CDS502 Big Data Storage and Management 3. CDS503 Machine Learning 4. CDS504 Enabling Technologies and Infrastructures for Big Data 5. CDS505 Data Visualisation and Visual Analytics 6. CDS506 Research Consultancy and Professional Skills 7. CDS590 Consultancy Project and Practicum ELECTIVE COURSES 8. CDS511 Consumer Behavioural and Social Media Analytics 9. CDS512 Business Intelligence and Decision Analytics 10. CDS513 Predictive Business Analysis 11. CDS521 Multimedia Information Retrieval 12. CDS522 Text and Speech Analytics 13. CDS523 Forensic Analytics and Digital Investigations
- 4. 4 3.3.3 Applications of Soft Skills The following table provides the matrix for the applications of soft skills for this programme: No. Course Code/Unit Course Title CTPS-CriticalThinking andProblemSolving CS–Communication Skill TS-Teamwork EM-Moraland ProfessionalEthics LL-LifelongLearning andInformation Management ES-Entrepreneurship Skill LS-LeadershipSkill CORE COURSES 1. CDS501 Principles and Practices of Data Science and Analysis 2. CDS502 Big Data Storage and Management 3. CDS503 Machine Learning 4. CDS504 Enabling Technologies and Infrastructures for Big Data 5. CDS505 Data Visualisation and Visual Analytics 6. CDS506 Research Consultancy and Professional Skills 7. CDS590 Consultancy Project and Practicum ELECTIVE COURSES 8. CDS511 Consumer Behavioural and Social Media Analytics 9. CDS512 Business Intelligence and Decision Analytics 10. CDS513 Predictive Business Analysis 11. CDS521 Multimedia Information Retrieval 12. CDS522 Text and Speech Analytics 13. CDS523 Forensic Analytics and Digital Investigations
- 5. 5 3.3.4 Programme Structure Credit requirements: 44 units (i) Core Courses: 24 units (Code: T) (ii) Elective Courses: 12 Units (Code: E) Choose any three (3) courses from the table below: Business Analytics (a) CDS511/4 – Consumer Behavioural and Social Media Analytics (b) CDS512/4 – Business Intelligence and Decision Analytics (c) CDS513/4 – Predictive Business Analytics Multimodal Analytics (a) CDS521/4 – Multimodal Information Retrieval (b) CDS522/4 – Text and Speech Analytics (c) CDS523/4 – Forensic Analytics and Digital Investigations (iii) Project (Core): 8 units (Code: T) CDS590 – Consultancy Project and Practicum This experiential work-based learning course prepares students to be a data scientist/analytics consultant by enhancing the students’ knowledge and skills in research, planning and implementation of a consultancy project in the field of data science/analytics, which can be applied to real-life situation. Students are required to complete the practicum at their respective workplace or their chosen/assigned organisation. Students work under the supervision of a lecturer and an industry mentor. The students are required to solve a real-world problem or tap opportunities related to data science and analytics during their practicum. The prerequisite of this course is CDS506 which must be taken in the preceding semester. The students are required to secure practicum placement together with project proposal during CDS506. At the end of this course, the students will be able to: Perform work collaboratively in a multi-ethnic environment with superior, colleagues, staff and supervisors. Analyse the needs and/or problems related to data analytics in the workplace. (a) CDS501/4 – Principles and Practices of Data Science and Analytics (b) CDS502/4 – Big Data Storage and Management (c) CDS503/4 – Machine Learning (d) CDS504/4 – Enabling Technologies and Infrastructures for Data Science (e) CDS505/4 – Data Visualisation and Visual Analytics (f) CDS506/4 – Research, Consultancy and Professional Skills
- 6. 6 Identify suitable quantitative and analytical tools (including software, technology and technical know-how) to propose usable solution to complex, ambiguous, unstructured problem in real world setting. Practice effective communication in writing and orally the progress and achievement of the practicum. Display leadership behaviours such as initiative, focussed, and high- performance standards. 3.3.5 Study Schemes The programme is offered on full time basis with a minimum period of candidature of three (3) semesters and a maximum of six (6) semesters. The study schemes are as follows: 1.5 Year Study Schemes: Course Type (Code) (Unit) September Intake: 1.5 Years (3 Semesters) Year I Semester I (September) Unit Year I Semester II (February) Unit Year II Semester I (September) Unit Core (T) (32 Unit) CDS501 4 CDS504 4 CDS590+ 8 CDS502 4 CDS506+ 4 CDS503 4 CDS505 4 Elective (E) (12 Units) Elective I 4 Elective III 4 Elective II 4 Total: 44 Units 16 16 12 + Must be taken in two consecutive semesters, CDS506 followed by CDS590 Course Type (Code) (Unit) February Intake: 1.5 Years (3 Semesters) Year I Semester II (February) Unit Year I Semester I (September) Unit Year II Semester II (February) Unit Core (T) (20 Unit) CDS501 4 CDS502 4 CDS590+ 8 CDS503 4 CDS505 4 CDS504 4 CDS506+ 4 Elective (E) (24 Units) Elective I 4 Elective II 4 Elective III 4 Total: 44 Units 16 16 12 + Must be taken in two consecutive semesters, CDS506 followed by CDS590
- 7. 7 2 Year Study Schemes: Course Type (Code) (Unit) September Intake: 2 Years (4 Semesters) Year I Semester I (September) Unit Year I Semester II (February) Unit Year II Semester I (September) Unit Year II Semester II (February) Unit Core (T) (20 Unit) CDS501 4 CDS504 4 CDS505 4 CDS590+ 8 CDS502 4 CDS506+ 4 CDS503 4 Elective (E) (24 Units) Elective I 4 Elective III* 4 Elective III* 4 Elective II 4 Total: 44 Units 12 12 8/12 8/12 + Must be taken in two consecutive semesters, CDS506 followed by CDS590 * Alternative Semester Course Type (Code) (Unit) February Intake: 2 Years (4 Semesters) Year I Semester II (February) Unit Year I Semester I (September) Unit Year II Semester II (February) Unit Year II Semester I (September) Unit Core (T) (20 Unit) CDS501 4 CDS502 4 CDS506+ 4 CDS590+ 8 CDS503 4 CDS505 4 CDS504 4 Elective (E) (24 Units) Elective I 4 Elective II 4 Elective III* 4 Elective III* 4 Total: 44 Units 12 12 8/12 8/12 + Must be taken in two consecutive semesters, CDS506 followed by CDS590 * Alternative Semester 2.5 Year Study Schemes: Course Type (Code) (Unit) September Intake: 2.5 Years (5 Semesters) Year I Semester I (Sep) Unit Year I Semester II (Feb) Unit Year II Semester I (Sep) Unit Year II Semester II (Feb) Unit Year III Semester I (Sep) Unit Core (T) (20 Unit) CDS501 4 CDS504 4 CDS502 4 CDS506+ 4 CDS590+ 8 CDS503 4 CDS505 4 Elective (E) (24 Units) Elective I 4 Elective II 4 Elective III* 4 Elective III* 4 Total: 44 Units 8 8 8 8/12 8/12 + Must be taken in two consecutive semesters, CDS506 followed by CDS590 * Alternative Semester
- 8. 8 Course Type (Code) (Unit) February Intake: 2.5 Years (5 Semesters) Year I Semester II (Feb) Unit Year I Semester I (Sep) Unit Year II Semester II (Feb) Unit Year II Semester I (Sep) Unit Year III Semester II (Feb) Unit Core (T) (20 Unit) CDS501 4 CDS502 4 CDS504 4 CDS590+ 8 CDS503 4 CDS505 4 CDS506+ 4 Elective (E) (24 Units) Elective I 4 Elective III* 4 Elective III* 4 Elective II 4 Total: 44 Units 8 8 8 8/12 8/12 + Must be taken in two consecutive semesters, CDS506 followed by CDS590 * Alternative Semester Course offering and timetable slots are given in the table below: Semester I (September) Semester II (February) CDS501*, CDS522 CDS501*, CDS523 CDS502 CDS503*, CDS513 CDS503*, CDS511 CDS504 CDS505 CDS506 CDS506* CDS512, CDS521 * Offered in both semesters 3.3.6 Graduation Requirements A student should accumulate a total of 44 units as shown in the table below with a CGPA 3.00 for graduation. Components Units 6 Core Courses 24 1 Consultancy Project & Practicum (Core) 8 3 Elective Courses 12 TOTAL 44
- 9. 9 Course Synopsis and Learning Outcomes for Master of Science (Data Science & Analytics) Courses CDS501/4 – Principles & Practices of Data Science & Analysis This course introduces the basic goals and techniques in data science and analytics process with some theoretical foundations which include useful statistical and machine learning concepts so that the process can transform hypotheses and data into actionable predictions. The course provides basic principles on important steps of the process which include data collecting, curating, analysing, building predictive models and reporting and presenting results to audiences of all levels. R programming language and statistical analysis techniques are introduced based on examples such as from marketing, business intelligence and decision support. At the end of this course, the students will be able to: Organize effectively all the necessary steps in any data science and analytics real-world project. Adapt the R programming language and useful statistical and machine learning techniques in data science and analytics projects. Practice all the skills needed by the data scientist, which include acquiring the data, managing the data, choosing the modelling technique, writing the code, and verifying and presenting the results. CDS502/4 – Big Data Storage and Management Storing and managing big data addresses different issues compared to conventional databases. Big data involves huge amount of data (volume), supports heterogeneous data format (variety) and can be accessed at high speed (velocity). The course includes fundamental on big data storage and management related issues. Understanding of various storage infrastructures includes understanding of technologies ranging from traditional storage to cloud-based storage. The course provides exposure on recent technologies in manipulating, storing and analyzing big data. The technologies include but not limited to Hadoop, MongoDB and Apache Cassandra. At the end of this course, the students will be able to: Compare the various data storage infrastructures, advanced concepts and technologies Build a database to support big data using related big data storage system. Identify and master the rules of modern and traditional in storing and managing large data. CDS503/4 – Machine Learning Upon successful completion of the course, students will have a broad understanding of machine learning algorithms. Students will be acquiring skills of applying relevant machine learning techniques to address real-world problems. Students will be able to adapt or combine some of the key elements of existing machine learning algorithms. Topics which will be covered in this course include supervised and unsupervised learning techniques, parametric and non-parametric methods, Bayesian learning,
- 10. 10 kernel machines, and decision trees. The course will also discuss recent applications of machine learning. Students are expected to obtain hands-on experience during labs and assignments to address practical challenges. An understanding of the current state-of-the-art in machine learning is done via a review of key research papers allowing students to further research in machine learning. At the end of this course, the students will be able to: To apply relevant machine learning algorithms for typical real-world problems. Manipulate machine learning algorithms which can be adapted to more complex scenarios. Synthesize findings and recommendations. CDS504/4 – Enabling Technologies & Infrastructures for Big Data Data science is advancing the inductive conduct of science and is driven by big data available on the Internet. This course will explain the technologies and techniques to improve the access, security, and performance of big data processing and storage systems. This course will help students to: Acquire the necessary skills as an analyst for big data system. Identify the security aspects of the data and determine the appropriate measures to protect it. Have an exposure and training in designing basic infrastructure for the application of big data with sensitive nature of the low-power edge devices. This course includes parallel and distributed processing, grid and cloud computing, big data tools, big data processing techniques, network infrastructure and architecture, network performance and security for big data. At the end of this course, the students will be able to: Distinguish major concepts of data science which are high-performance parallel and distributed computing; computing with emerging technologies, and network performance. Identify the needs and issues for big data security to protect sensitive data and suitable access controls. Design a cloud platform and efficient techniques that can support end-users running latency-sensitive big data applications on low-powered edge devices. CDS505/4 - Data Visualisation & Visual Analytics This course discusses the use of computer-supported, interactive and visual representations of data in order to amplify cognition, help people reason effectively about information, find patterns and meaning in the data, and easily explore the datasets from different perspectives in particular in data-intensive environment. The course covers techniques from two branches of visual representation of data, namely data visualization and visual analytics. In data visualization, the course covers scientific visualisation techniques (representations of empirically-gathered scientific datasets) such as contours, isosurface, and volume rendering as well as specific techniques in information visualisation (representations of abstract datasets) which include tables, networks and trees, and mapcolour. In visual analytics, a visualization process features a significant amount of computational analysis and human- computer interaction. So, the topics covered in this part of the course include view
- 11. 11 manipulation, multiple views, reduction in items and attributes, and focus + context as well as analysis case studies involving a visualization system or tool. At the end of this course, the students will be able to: Select the right visualization techniques for any given problems or applications. Adapt visualization techniques for particular application. Apply several techniques either by designing or developing specific visualization techniques or using existing tools. CDS506/4 - Research, Consultancy and Professional Skills The course provides knowledge and effective skills that are required in research, consultancy and professional practice. For the research section, it will cover literature review, development of research questions, usage of theories, research design, data collection as well as related analysis techniques. For the consultancy skills, students will be equipped with the mindset tools and skills to provide effective consulting advice to clients. In the final section, professional issues, and different aspects such as ethical, legal and social in conducting research and consultancy will also be discussed. At the end of this course, the students will be able to: Combine theory and consultation techniques to effectively meet clients' needs Adapt a structured and effective research method in data science and analytics research. Correlate professional issues inherent in research methods and consultancy. CDS511/4 - Consumer Behavioural and Social Media Analytics This course provides a broad and interdisciplinary research and practise focusing on two areas: behaviour and web & social media analytics. Specifically, behaviour analytics concerns the process of systematically converting multimodal human behavioural cues (facial, speech, textual etc.) to machine readable form, in order to automatically model the human behaviour. The focus is on humans as consumers. This involves human-computer interaction (HCI), user behaviour modelling, computational models of emotions, and emotion sensing and recognition. Web and social media analytics concerns the strategies to leverage powerful social media data concerning customer needs, behaviour and preferences. Students will learn the strategies to derive insights from the above mentioned data that are crucial for business decisions. Students will be encouraged to explore statistical, machine learning and analytical tools such as SPSS, R, WEKA, Google Analytics, TrueSocial Metrics and Clicky for analysis. It is worth to note that an understanding of the current state-of-art in consumer behavioural and social media analytics is done via a review of key research papers, and book chapters allowing students to further research in this area if needed. At the end of this course, the students will be able to: Distinguish the suitable metrics for assessing multimodal human behavioural cues in a consumer perspective.
- 12. 12 Identify human behavioural cues across a variety of contexts with state-of-the- art tools to facilitate better interaction and decision making. Construct predictive models (by extracting, analyzing and deriving insights) from the related web and social media data for data-informed decision-making within a business perspective. CDS512/4 - Business Intelligence & Decision Analytics The course will focus on the knowledge and skills to select, apply and evaluate business intelligence and decision analytics techniques which discover knowledge that can add value to a company. The course will also discuss innovative applications and exploitation of the current techniques and approaches related to business intelligences and performance measurement, and mathematical model to facilitate decision-making process in business and operations. At the end of this course, the students will be able to: Elaborate concepts, technologies and theories related to business intelligences and decision analytics. Integrate the use of different types of business intelligence models and tools, and decision analytics models to various real-life problems. Propose improvement strategies for enhancing business performance by applying business intelligence and decision analytics techniques. CDS513/4 - Predictive Business Analytics The course provides the theory behind predictive analytics, and methods, principles and techniques for conducting predictive business analytics projects. The course introduces the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics that translate big data into meaningful, usable business information. The course also explores the tips and tricks that are essential for successful predictive modelling in areas such as business performance, pharmaceutical industry, finance, accounting, and organization management. The course takes technology approach to address a big data analytic challenge by applying the concepts taught in the course in the context of predictive analytics project lifecycle. Students will be exposed to a predictive business analytics tool. At the end of this course, the students will be able to: Apply appropriate predictive business analytics techniques and tools to effectively interpret big data. Revise and adapt insights that can lead to actionable results and pragmatic business solutions. Construct a business challenge as a predictive business analytics challenge. CDS521/4 - Multimodal Information Retrieval This course provides the basic concepts, principles and applications for multimodal (text, image, video and audio) retrieval. This course covers basic techniques for content processing, indexing, representation, ranking, querying, and evaluation for multimodal information retrieval. In addition, advanced techniques such as large scale retrieval, multimodal analysis, and cross media retrieval will be covered based on the latest context such as mobile devices, social media and big data.
- 13. 13 At the end of this course, the students will be able to: Summarize and criticize the state of the art of multimodal information retrieval. Adapt the framework, models and techniques of multimodal information retrieval. Solve problems in emerging multimodal applications using the learned techniques. CDS522/4 - Text and Speech Analytics A lot of the information resides in documents and speech format. This information however is not directly utilisable because they are unstructured. The course focuses on the theory and applications of natural language processing and speech processing to retrieve linguistic knowledge in these sources. The linguistic knowledge from words, syntax and semantics of sentences will be combined with machine learning algorithms and statistical approach to find, organize, categorize, analyze and interpret the unstructured and semi-structured text that allow users to seek advice to make a decision. At the end of this course, the students will be able to: Describe basic concepts and algorithms in natural language and speech processing, for example tokenization, morphological analysis, ngram, tagging, parsing, word sense disambiguation and decoding. Manipulate natural language processing and speech processing approaches to obtain different levels of linguistics information such as word, sentence and semantics for text analytics. Design custom solutions using natural language processing and speech processing techniques or text and speech analytics problems in organizations. CDS523/4 - Forensic Analytics and Digital Investigations This course introduces fundamental knowledge and techniques of computer forensics and digital investigations. Starting from an overview of the profession of digital investigator, issues on the digital forensics and investigations on big data, and the current practices for processing crime and incident scenes will be explained. Next, the principles of interpretation of evidence, ways of controlling and preserving evidence, and techniques for manual interpretation of raw binary data will be detailed. The students will learn advanced techniques in forensic investigations on big data: methods to identify big data evidence, collecting and performing analysis on the data, and then the proper techniques to report and present the forensic findings as well as the proper way to act as expert witness in reporting results of investigations. In addition, technical and legal difficulties involved in searching, extracting, maintaining and storing digital evidence will be explained along with the legal implications of such investigations and the rules of legal procedure relevant to electronic evidence. At the end of this course, the students will be able to:
- 14. 14 Conduct digital investigations that conform to accepted professional standards and are based on the investigative process: identification, preservation, examination, analysis and reporting. Identify and document potential security breaches of computer data that suggest violations of legal, ethical, moral, policy and/or societal standards. Master the principles and practices of big data forensics and digital investigations. Access and critically evaluate relevant technical and legal information and emerging industry trends. CDS590/8 - Consultancy Project & Practicum See Section 3.3.4(iii) for detail on this course