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Session: 2022/23
Last modified: 30/09/2020 09:44:43
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Title of Module: Advanced Data Science |
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Code: COMP11068 |
SCQF Level: 11
(Scottish Credit and Qualifications Framework) |
Credit Points: 20 |
ECTS: 10
(European Credit Transfer Scheme) |
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| School: | School of Computing, Engineering and Physical Sciences |
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| Module Co-ordinator: | Zeeshan
Pervez |
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| Summary of Module |
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The connected world generates large volume of data that need to be understood and analysed to develop insight in particular application areas for instance financial sector, healthcare, market analysis, community behaviours to name a few. With the adoption of smart devices and ubiquitous deployment of sensing capabilities around us in the form of Internet-of-Things (IoT), data is becoming increasing important. Data from various modalities ranging from personal devices to mass deployment of IoT are utilised to provision personalised services commonly known as data driven services. The efficacy of these services is greatly influenced by intelligence gained through data. The availability of high speed internet and connected devices generates data that need to be integrated, analysed and then fed to data driven services.
Data science is an emerging filed which offers innovation solutions catering all 4V’s of big data; with its roots in statistical analysis and machine learning it offers powerful algorithms, methodologies, and tools to manage, transform and analyse big data. This module address advance topics of data science yet in incremental manner – starting from fundamentals of data analysis life cycle to data stream processing. This module is designed to cover theoretical knowledge of data processing, management and analysis to hands-on experience on big data frameworks used to crunch massive data sets.
The following topics will be covered in this module:
- Introduction to data science
- Data management for data science services and applications
- Data science at scale
- Data stream processing
- New developments in data science
- This module will work to develop a number of the key 'I am UWS' Graduate Attributes to make those who complete this module:
Universal
• Critical Thinker
• Ethically-minded
• Research-minded
Work Ready
• Problem-Solver
• Effective Communicator
• Ambitious
Successful
• Autonomous
• Resilient
• Driven
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| Module Delivery Method |
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| Face-To-Face | Blended | Fully Online | HybridC | HybridO | Work-based Learning |
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Face-To-Face
Term used to describe the traditional classroom environment where the students and the lecturer meet synchronously in the same room for the whole provision.
Blended
A mode of delivery of a module or a programme that involves online and face-to-face delivery of learning, teaching and assessment activities, student support and feedback. A programme may be considered “blended” if it includes a combination of face-to-face, online and blended modules. If an online programme has any compulsory face-to-face and campus elements it must be described as blended with clearly articulated delivery information to manage student expectations
Fully Online
Instruction that is solely delivered by web-based or internet-based technologies. This term is used to describe the previously used terms distance learning and e learning.
HybridC
Online with mandatory face-to-face learning on Campus
HybridO
Online with optional face-to-face learning on Campus
Work-based Learning
Learning activities where the main location for the learning experience is in the workplace.
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| Term(s) for Module Delivery |
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(Provided viable student numbers permit).
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| Term 1 | | Term 2 | | Term 3 | |
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| Learning Outcomes: (maximum of 5 statements) |
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On successful completion of this module the student will be able to:
L1.
Demonstrate an extensive knowledge of the fundamental data science concepts and their usage for varied dataset types and volume.
L2.
Demonstrate a comprehensive understanding of data analytics algorithms and libraries to design and develop data driven services.
L3.
Analyse and apply widely used big data frameworks for various application domains and expected outcomes.
L4.
Develop skills to make use of big data frameworks for data science pipelines supporting data driven services. |
| Employability Skills and Personal Development Planning (PDP) Skills |
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| SCQF Headings |
During completion of this module, there will be an opportunity to achieve
core skills in:
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| Knowledge and Understanding (K and U) |
SCQF Level 11.
Extensive understanding of data science concepts. Comprehensive knowledge of data analytics life cycle and its application for various domains. |
| Practice: Applied Knowledge and Understanding |
SCQF Level 11.
In-depth knowledge to various data analysis methodologies and data management platforms to design and develop data driven services. |
| Generic Cognitive skills |
SCQF Level 11.
Critical and theoretical analysis of state-of-the-art presented in published technical reports and scholarly articles. |
| Communication, ICT and Numeracy Skills |
SCQF Level 11.
Assignments and discussions session will assist students to work collaboratively and discuss possible solutions to a problem related to the knowledge they gained in lectures and lab sessions. |
| Autonomy, Accountability and Working with others |
SCQF Level 11.
Each student will generate a comprehensive report summarizing his/her finding for a given scenario. For groups based assignments, participants of each group will have to justifying their findings within a group discussion lead by a group leader, and finally submit a collective report – delineating their findings. |
| Pre-requisites: |
Before undertaking this module the student should have
undertaken the following:
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Module Code:
| Module Title:
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| Other: | |
| Co-requisites | Module Code:
| Module Title:
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* Indicates that module descriptor is not published.
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| Learning and Teaching |
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This module comprises of lectures, labs, and assignments. The lectures will deliver fundamental knowledge of data science and application in various domains. Through lectures students will be able to learn various methodologies to prepare, persists and process data from varied sources in large volume. Furthermore these lectures will develop theoretical understanding of algorithms and frameworks used to process big data. Students will also be introduced to selected topics of data science research. Labs will help in developing in-depth understanding of the knowledge delivered in the lectures, and critical evaluation of algorithms and methodologies when applying for a specific problem.
The list of indicative lectures, labs and tutorials:
Lectures
• Introduction to data science – motivation, real world applications, use case studies
• Data analytics life cycle – discovery, preparation, planning, building, results and operationalization
• Data structures and data management for data science
• Programming for data science
• Programming libraries for data science
• Introduction to big data frameworks - abstraction, joins, implementation
• Data science as scale
• Data stream processing – concepts, limitations
• Data stream processing frameworks
• Research developments in data science
Labs and Tutorials
• Setting up NoSQL database; data modelling and querying
• Programming for data science
• Dataset manipulation
• Data analytics
• Big Data frameworks
• Data analytics with big data framework
Group / Individual assignments
• Data pipeline with real world dataset
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Learning Activities
During completion of this module, the learning activities undertaken to
achieve the module learning outcomes are stated below:
| Student Learning Hours
(Normally totalling 200 hours):
(Note: Learning hours include both contact hours and hours spent on other learning activities) |
| Lecture/Core Content Delivery | 20 |
| Laboratory/Practical Demonstration/Workshop | 12 |
| Personal Development Plan | 10 |
| Independent Study | 158 |
| 200
Hours Total
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**Indicative Resources: (eg. Core text, journals, internet
access)
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The following materials form essential underpinning for the module content
and ultimately for the learning outcomes:
Lectures notes and hand-outs will be provided through moodle.
Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data by EMC Education Services; John Wiley & Sons.
Data Science from Scratch: First Principles with Python Paperback by Joel Grus
Python Programming for the Absolute Beginner 3rd Edition by Mike Dawson
Programming Python Paperback by Mark Lutz
Software Packages:
- PyCharm - IDE for Python,
- Python 3.x or latest release
- MongoDB 3.x or latest release
- VirtualBox 5.x or latest release
- Libraries NumPy, SciPy, Matplotlib, Pandas, scikit-learn
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(**N.B. Although reading lists should include current publications,
students are advised (particularly for material marked with an asterisk*) to
wait until the start of session for confirmation of the most up-to-date
material)
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| Engagement Requirements |
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In line with the Academic Engagement Procedure, Students are defined as academically engaged if they are regularly engaged with timetabled teaching sessions, course-related learning resources including those in the Library and on the relevant learning platform, and complete assessments and submit these on time. Please refer to the Academic Engagement Procedure at the following link: Academic engagement procedure |
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Supplemental Information
| Programme Board | Computing |
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| Assessment Results (Pass/Fail) |
No
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| Subject Panel | Business & Applied Computing |
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| Moderator | Naeem Ramzan |
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| External Examiner | C Luo |
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| Accreditation Details | |
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| Version Number | 1.06 |
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| Assessment: (also refer to Assessment Outcomes Grids below) |
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| During the laboratory sessions each student will be required to successfully complete the tasks mentioned in the lab sheet (weighted 30%), consequently assessing the achievement of L1 and L2. |
| Each student will select a topic for term project (weighted 70%) to analysis, design, and develop a data science pipeline using skills developed through lectures and lab sessions. Term project will evaluate L3 and L4. Topic of the term project must be agreed between the student and module coordinator/module tutor to ensure its relevance with the learning outcomes. |
(N.B. (i) Assessment Outcomes Grids for the module
(one for each component) can be found below which clearly demonstrate how the learning outcomes of the module
will be assessed.
(ii) An indicative schedule listing approximate times
within the academic calendar when assessment is likely to feature will be
provided within the Student Handbook.)
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Assessment Outcome Grids (Footnote A.)
Footnotes
A. Referred to within Assessment Section above
B. Identified in the Learning Outcome Section above
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Note(s):
- More than one assessment method can be used to assess individual learning outcomes.
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Schools are responsible for determining student contact hours. Please refer to University Policy on contact hours (extract contained within section 10 of the Module Descriptor guidance note).
This will normally be variable across Schools, dependent on Programmes &/or Professional requirements.
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| Equality and Diversity |
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The University policies on equality and diversity will apply to this module. In relation to students with special needs, when a student discloses a disability the individual module tutor, in consultation with the special needs coordinator will agree any appropriate adjustments to be made. Students should note that the language of instruction is English and that they will need to have a reasonable grasp of the language in order to understand the teaching materials.
UWS Equality and Diversity Policy |
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(N.B. Every effort
will be made by the University to accommodate any equality and diversity issues
brought to the attention of the School)
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