The Bachelor of Science (Data Analytics) with Honours is designed to provide a program of study that combines data science, statistics, machine learning, and mathematics that is in line with the Industrial Revolution 4.0. The program applies the 2u2i elements through the implementation of 2.5 years of study in the university (university component) and 1 year of study in the industry (industrial component). Upon completion of this program, the students will also obtain SAS Certificate known as “SAS Academic specialization in Data Analytics”. The program curriculum has been fully integrated to meet the increasing need for highly skilled data analysts who can analyze the growing amount of data in a variety of disciplines and transform it into usable information for use in decision-making. The program also aims to address the high industry demand for business and data analysts. Graduates will be trained in the latest data analytics methods, concepts and tools used to make sense of data that are available in various forms through knowledge, skills, and abilities. Students will have an opportunity to work with the industry through their 4 industry component courses in the form of Work Based Learning (WBL) that are offered in the final year of study. This involves learning in a real-life industrial environment project related to IR4.0. The implementation of the 2u2i mode will expose students to actual learning and practice directly from relevant industry practitioners. Such training will add value to their qualification and increase their employment opportunities
PEO1 : Able to apply knowledge (PLO1) and technical skills (PLO2) as well as practical skills supported by intellectual skills (PLO3) in the field of Data Analytics in line with the Industrial Revolution 4.0 (IR4.0)
PEO2 : Able to communicate effectively in various levels of autonomy (PLO4) as well as the ability to plan, manage relationships in teams and in organizations of different political, cultural and social backgrounds (PLO5)
PEO3 : Practicing knowledge in an ethical and professional manner, with integrity and accountability (PLO6)
PEO4 : Able to solve problems in an IR 4.0 environment effectively with the spirit of “esprit de corps” (PLO7) and able to make decisions critically and analytically in various levels of autonomy in the organization (PLO9)
PEO5 : Able to sharpen the entrepreneurial mindset related to IR 4.0 (PLO8) by leveraging knowledge and digital technology skills (PLO10) supported by quantitative skills to analyze and manage economic, political, social environment and climate change in IR 4.0 environment (PLO11)
This course covers the basic concept of corruption, including the value of integrity, anti-corruption, forms of corruption, abuse of power in daily activities and organizations as well as waysto prevent corruption. Cases related to corruption are discussed. Teaching and learning methods are implemented in the form of ‘experiential learning’ through individual and group activities. At the end of this course, students are able to understand the practice of integrity, the concept of corruption, anti-corruption, abuse of power as well as the prevention of corruption in society and organizations.
This course gives students an exposure to the basic concepts of entrepreneurship. Students will do learning activities that lead to building an entrepreneurial mindset as an initial preparation for a future career. This course provides an exposure to students on knowledge in entrepreneurship. It also gives students the opportunity to apply the knowledge obtained from their respective fields. In addition, the course aims to apply the entrepreneurial mind sets into their life after graduation.
This course is offered to international students who want to learn the fundamentals of the Malay Language to be used in daily conversations either in formal or informal settings. Students will also be trained to read simple reading materials and to write simple essays. Other than that, students will also be exposed to aspects of the Malaysian/Malay culture, through video presentations and field trip.
This course introduces the student to the collection, preparation, data acquisition, cleaning, aggregation, exploratory data analysis, modelling and visualization of data, feature engineering, and model creation and validation covering both conceptual and practical issues. Examples from diverse fields will be presented, and hands-on use of statistical and data manipulation software will be included.
This course discusses the topics like the limit and continuity, multivariable functions, partial derivatives, total derivative and multiple integration. In addition, this course discusses the cylinder coordinate, spherical coordinate and the change of variables in multiple integration.
This course discusses the basics of machine learning which include introduction to machine learning, various concepts and methods in machine learning, classification of machine learning algorithms, various types of machine learning such as “Neural Networks”, “Support Vector Machine” and ending with language learning
Pre-requisite No This course discusses the topics like the limit and continuity, multivariable functions, partial derivatives, total derivative and multiple integration. In addition, this course discusses the cylinder coordinate, spherical coordinate and the change of variables in multiple integration.
This course introduces fundamental elements of the emerging science of complex networks, with emphasis on social and information networks. Students will learn about mathematical and computational methods used to visualize & analyse networks, methods used to understand and predict behaviour of networked systems, and theories used to reason about network dynamics. Students will also be exposed to current trend in the field, and derive insights on complex structures.
This course introduces to the principles and basic data visualization design; visualization representation methods and techniques including charts, tables, graphics, effective presentations, multimedia content, animation, and dashboard design for visualizing multivariate, temporal, text-based, geospatial, hierarchical and network data. Hands-on visualization exercises based on common data domains will be given to experience designing data graphics and visualizations, and reporting findings using data visualization tool.
This course introduces fundamental elements of the emerging science of Topological Data Analysis (TDA) with the underlying principles from computational geometry, algebraic topology, data analysis, and many other related scientific areas. The application of topological techniques to complex data has opened up new opportunities in exploratory data analysis and data mining. This course is intended to cover theory, algorithm and application of TDA for identifying topological signatures of complex datasets, not just massive in size, but rich in features.
This course introduces programming methods to solve problems. Topics for this course include the introduction to data structure such as linked list, stack, queue, tree, graph, sorting techniques and searching methods. Emphasis will be given on modular programming technique. This course also introduces algorithm time complexity as a measuring technique of an efficient algorithm. In addition, the time complexity algorithm as an efficient algorithm measurement technique will also be introduced.
This course introduces database concepts (goals of DBMS, relationships, physical and logical organization, schema and subschema); data models, normalisation (until BCNF), canonical schema and data independence; data description language; query facilities, query functions; design and translation strategies; and data integrity and reliability.
This course exposes students to the systematic scientific research, writing good dissertation and effective presentation
This course introduces the student to the basic requirement of industrial project particularly data science projects at industrial environment.
This course introduces students to the concepts and techniques used in managing a project particularly data science projects in industrial environment. Through this project, students will generate project plan and schedule, cost estimation, in addition to preparing the related documents. The aspect that will be accessed here is students’ ability in managing project development. This course will be supervised by 130 industrial supervisor and monitored by UMT.
This course introduces the student to the important concepts in probability and statistics such as probability, random variables, probability distribution random variables, sampling distribution theory, estimation and hypothesis testing. Examples data from marine and aquatic will be presented, and handson use of statistical and data manipulation software will be included.
In this course, we will learn how to develop linear models via simple and multiple linear regression. Mastery of this knowledge is important because statistical methods are widely used today that involve real data and big data.
This course begins with Basic principles of experimental design; Randomization; Completely randomized design; Randomized blocks, Latin Squares, Factorial design; Blocking in factorial design; 2k factorial design; Extension of 2k factorials; Blocking and confounding in 2k factorials; Fractional factorial designs; Blocking in fractional factorials; Nested and split-plot designs; Replicated and un-replicated designs; Random effects model
The course introduces the student to a set of principles of survey and data analytics that are the basis of standard practices in these fields. This course begins with how to do survey research for data analytics, quality of data, modes of survey data collection, data generation from other sources, sampling technique such as simple random, probability sampling, stratified sampling, ratio and regression estimation, cluster and systematic sampling, two-stage sampling, estimating the population size, total survey error, writing reports and managing the survey process. Examples from diverse fields will be presented, and hands-on.
This course introduces student to the knowledge of widely-used forecasting technique, such as the forecasting problem, an introduction to time series methods, time series methods, data reduction and big data. Examples data from marine and aquatic will be presented, and hands-on use of forecasting methods and data manipulation will be included.
The course begins with a brief overview of the probabilities and is followed by Statistics. Topics to be covered in this course include one-parameter models, multiparameter models, Marke Monte Carlo chains and model comparisons
This course covers introduction to multivariate data, multivariate data visualization, application of multivariate models such as principal component analysis, multidimensional scaling, factor analysis and group analysis.
General Entry Requirements:
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Specific Requirements:
AND
General Entry Requirements:
AND
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Specific Requirements
AND
General Entry Requirements
AND
AND
Specific Requirements
General Entry Requirements
English Language Requirements
Our International Centre office will be happy to advise prospective students on entry requirements. See our International Centre website for further information for international students.
Local | International | Additional Costs |
RM 7,950 | USD 7,220 | Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at UMT. |
Government funding
You may be eligible for government finance to help pay for the costs of studying. See the Government’s student finance website
Scholarships are available for excellence in academic and co-curricular activities, and are awarded on merit. For further information on the range of awards available and to make an application see our scholarships website.
Email : safiihmd@umt.edu.my
Phone (office) : +609-6683247
(mobile) : 6013 – 927 0553