Master of Science in Data Science
JPT/BPP(N-DL/0613/7/0015)10/28
N-DL/0613/7/0015
MQA/PSA 16962
Introduction
One of the important approaches to achieve competitiveness in digital transformation is through a data-driven approach.With the right data science capabilities, the human capital development from the computing area is highly required in order to meet the calls for under-explored hydrocarbon reserve all over the world. This trend brings about many benefits to the nation and the people, but at the same time has introduced acute shortage of skilled manpower in this area. The requirements for knowledge workers with MSc and PhD qualifications have increased
Programme Objective
The programme objectives are:
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Data Scientist having advanced knowledge in Data Science capable of adopting best methodologies and techniques to provide innovative solutions to various industries and society.
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Data Scientist who has leadership skills and able to communicate as well as interact effectively with diverse stakeholders.
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Data Scientist having positive attitudes, engaging in lifelong learning activities and entrepreneurial mindset for continual career development.
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Data Scientist who upholds and practice ethics and professionalism for self and profession integrity.
Programme Outcomes
Upon graduation the candidates shall be able to:
- Integrate advanced knowledge related to current issues in Data Science
- Recommend innovative solutions that is at the forefront of developments in the fields
- Evaluate data solutions and tools in terms of their usability, efficiency and effectiveness
- Demonstrate effective interaction within a group and with diverse audience through project discussion related to the fields of study
- Demonstrate effective communication within a group and with diverse audience by publishing and presenting technical materials in the fields of study
- Utilise digital skill to acquire, interpret and extend knowledge in data science
- Apply numerical skill to acquire, interpret and extend knowledge in data science
- Demonstrate leadership, teamwork, autonomy and responsibility in delivering services related to field of study
- Exhibit capabilities to extend knowledge through life-long learning related to the fields of study
- Exhibit capabilities to extend knowledge with entrepreneurs’ mind-set related to the fields of study
- Uphold professional and ethical practices in conducting research and delivering services related to the field of study
Why you should join our MSc in Data Science (ODL)Industries demand for graduates with technical capability to fill in the workforce gaps in various areas in data science, big data analytics and advanced algorithms domain. The impact of IR4.0 expansion requires rapid needs of Data Scientists to analyze, prepare and visualize data as well as evaluating and improving model solutions. The MSc in Data Science programme prepares students to become Data Scientists with skillsets and knowledge in data engineering, data management, data analytics, project management, machine learning, and optimization.
The MSc in Data Science has the objective to produce data science experts with the needed skills and knowledge, so they can manage the challenges in IR4.0 with data analytics capability.
The MSc in Data Science programme will support TN50 to help Malaysia to become a developed nation in 2050 by fulfilling the needs of the current and potential future markets. The contribution involves economic development, citizen well-being and innovation.
Furthermore, the MSc in Data Science programme will also provide the potential graduates with knowledge and skills of two demanding areas of data science and analytics: through its advanced data analytics and data engineering specializations.
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Graduation Requirements
In order to graduate with a Master of Science in Data Science (ODL) degree, students are required to:
- Obtain a minimum cumulative grade point average (CGPA) of 3.00
- Satisfy all the requirements approved by UTP Senate.
- Fulfill 40 credit hours and pass the Research Methodology course.
Course Duration and Offering
Duration : 12 months to 36 months Offering : This course is available in Open & Distance Learning (ODL).
Entry Requirements
a) Academic
RequirementsThe entry requirement for admission to MSc in Data Science is shown as below:
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Bachelor’s Degree in a relevant field from a recognised university with a minimum CGPA of 2.75 or its equivalent.
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Bachelor’s Degree in a relevant field from a recognised university with a CGPA of 2.50 - 2.74 or its equivalent will require internal rigorous assessment.
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Bachelor’s Degree in a relevant field from a recognised university with a CGPA of 2.00 - 2.49 or its equivalent will require 5 years of working experience and internal rigorous assessment.
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Apply with your working experience. Candidates who satisfy APEL A requirements are eligible to enroll.
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For candidates without Computing Degree (Level 6 MQF), prerequisites modules in computing will be offered to adequately prepare them.
b) English Requirements
Pass English requirement with a minimum score of:
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A minimum TOEFL score of 550 or equivalent
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A minimum IELTS score of 6.0 or equivalent
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Native English speakers or holding a degree with English as the medium of instruction may be exempted from this requirement.
Programme Curriculum Structure
|
Semester 1 |
|
OAS5013
OAS5023
OAS5033
OAS5043
|
Data Science Concept
Research Method in IT
Big Data Analytics
Statistical Method for Data Analysis
Total (Semester 1) |
3
3
3
3
12
|
|
|
Core
Core/National Requirement
Core/University Requirement
Core
|
|
Semester 2 |
|
OAS5053
OAS5063
OAS5073
OAS5XX3
OAS5133
|
IT Project Management
Data Management
Data Analytical Programming
Elective 1
MSc Project 1
Total (Semester 2) |
3
3
3
3
3
15
|
|
|
Core/University Requirement
Core
Core
Elective
Project
|
|
Semester 3 |
OAS5083
OAS5XX3
OAS5147
|
Data Mining & Machine Learning
Elective 2
MSc Project 2
Total (Semester 3) |
3
3
7
13
|
|
|
Core
Elective
Project |
|
Total Credit Hours |
40 |
|
|
| Advanced Data Analytics |
- Digital Analytics
- Real-time Analytics
|
Data Engineering
|
- Numerical Optimization
- Deep Learning
|
Programme Module Synopsis
| OAS5013 |
Data Science Concepts |
3 credits |
| This course introduces the basic concepts of data science, use cases, technologies, challenges, and opportunities. Furthermore, it presents effective methods of data visualization and summary statistics to explore complex data, and reviews probability theory, with an emphasis on conditional probability as a foundation of modern computational statistical methods and AI. The course covers basic computational statistical inference employing three approaches: maximum likelihood frequentist, bootstrap frequentist, and Bayesian. There is an overview of the properties and behavior of the rich family of linear models, which are foundational to many ML and AI algorithms, and a focus on applying Bayesian models and inference to real-world problems. models for time series data and spatial data are explored. |
| OAS5073 |
Data Analytical Programming |
3 credits |
| A fast-paced introduction to the Python programming language. The course introduces a range of python objects and control structures, then builds on these with classes and object-oriented programming. The last component of the course is devoted to Python’s system of packages for data analysis. Students will gain experience in different styles of programming, including scripting, object-oriented design, test- driven development, and functional programming. Weekly programming exercises are designed to reinforce each programming concept, while two larger projects give students experience in developing a larger program and in manipulating a dataset. Aside from Python, the course also spends time on several other technologies that are fundamental to the modern practice of data science, including use of the command line, coding, and presentation with Jupyter notebooks, and source control with Git and GitHub |
| OAS 5043 |
Statistical Methods for Data Analysis |
3 credits |
| The goal of this course is to provide students with an introduction to many different types of quantitative research methods and statistical techniques for analyzing data. We begin with a focus on measurement, inferential statistics, and causal inference. Then, we will explore a range of statistical techniques and methods using the open-source statistics language, R. We will use many different statistics and techniques for analyzing and viewing data, with a focus on applying this knowledge to real-world data problems. Topics in quantitative techniques include descriptive and inferential statistics, sampling, experimental design, parametric and non-parametric tests of difference, ordinary least squares regression, and logistic regression. |
| OAS 5063 |
Data Management |
3 credits |
In this course, various data genres and management tools are being explored to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools. Familiarity with techniques using real-time and semi-structured data examples is emphasized. This course provides techniques to extract value from existing untapped data sources and discovering new data sources. At the end, you will be able to recognize different data elements, explain the need to design a Big Data Infrastructure Plan and Information System Design, identify the frequent data operations required for various types of data, select a data model to suit the characteristics of your data, apply techniques to handle streaming data, and differentiate between a traditional Database Management System and a Big Data Management System.
|
| OAS 5033 |
Big Data Analytics |
3 credits |
The course provides thorough understand process, content, concepts, techniques, issues and challenges involved in big data analytics, prepare students to be technically competent in analyzing data and prepare students to improve management decision making using data analytics tools.
|
| OAS 5023 |
Research Method in IT |
3 credits |
To equip new post-graduate students with the philosophy and methodology of conducting research so as to maximize their success in proposing and managing their graduate research study plan for successful completion of the objectives by taking into consideration factors such as HES, ethical conducts and intellectual property protections.
|
| OAS 5083 |
Data Mining and Machine Learning |
3 credits |
Upon successful completion of the course, students will have a broad understanding of data mining and 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, unsupervised and reinforcement learning techniques, parametric and non-parametric methods, Bayesian learning, 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.
|
OAS 5053
|
IT Project Management |
3 credits |
| This course provides a comprehensive view of the ten project management knowledge areas and the five project management process groups, following the PMBOK® Guide. The course build on the PMBOK® Guide, an American National Standard, provides a solid framework and context for managing information technology projects. To better equip students for this environment, the course will include a team project, in which students will learn how to successfully plan, manage, and deliver projects. Students will also learn how to implement project management processes, develop leadership skills, and respond to real-world scenarios. All activities in this course are targeted towards exploring a variety of problems and issues in managing projects, addressing both the technical and social or human sides of the field. |
| OAS 5133/ OAS 5147 |
MSc Project 1/MSc Project 2 |
3 credits/7 credits |
The module allows each student to work independently on an industry-based project under the supervision of a faculty member and a supervisor from the industry. The student is expected to review the subject, propose an experimental / analytical plan, and follow that through to feasibility study, investigation, design / simulation, test and implementation. Each student must prepare a comprehensive technical report (MSc thesis), present and demonstrate findings and results of the project work.
|
Elective Group : Advanced Data Analytics
OAS 5093
|
Digital Analytics |
3 credits |
| In this course we introduce the concept of digital analytics and explore its’ various major components such as web mining, web analytics, data visualization and online business performance measurement in detail. In particularly, we look at the process, contents and context of managerial decision making. This included on how the implementation of Digital Analytics can help in improving management decisions and discuss issues affecting the success of digital analytics. |
| OAS 5103 |
Real-time Analytics |
3 credits |
This course introduces students to the principles, methodologies, applications, and management of real time big data sets. Topics may include real time systems and technologies, big data basics, industry examples of big data, big data technologies, information management, business analytics, real time analytics, security, compliance, auditing and protection of big data, mobile marketplaces, mobile sites, mobile apps, mobile data tracking.
|
Elective Group : Data Engineering
| OAS 5113 |
Numerical Optimization |
3 credits |
This course is intended to provide a thorough background of computational methods for the solution of linear and nonlinear optimization problems. Particular attention will be given to the description and analysis of methods that can be used to solve practical problems. Although the focus is on methods, it is necessary to learn the theoretical properties of the problem and of the algorithms designed to solve it.
|
OAS 5123
|
Deep Learning |
3 credits |
This course provides a hands-on introduction to very large-scale data and the practical issues surrounding how the data is stored, processed, and analyzed, both in the Cloud and on the Edge. Students will work with cloud computing systems, edge devices, large data collections, and high-velocity data streams. The class material will be introduced gradually as it helps students accomplish their projects and assignments throughout the course with exposure to many of the computing applications and technologies in the market. Hands-on activities will enable the students to learn the practical toolkit required to work with data at scale. Deep Learning applications (image / video processing) will serve as the major use case throughout the class.
|
CONTACT
Programme Manager
Ts Dr Emelia Akashah Patah Akhir
Email: emelia.akhir@utp.edu.my
