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Second Major in Data Analytics
With data becoming pervasive in the way we live and do business, companies are investing in data analytics capabilities to keep up with developments and competition. However, data analytics tools are evolving at a rapid pace and there is a shortage of qualified data analysts and data scientists in the market today.
To align students with emerging employment trends, the College of Computing and Data Science (CCDS) and College of Science (CoS) jointly offer the Second Major in Data Analytics. Engineering and Science students can take advantage of their technical knowledge and training in their majors to integrate applications in data analytics. This will expand their career options after graduation and ramp up their employability.
PROGRAMME OPTIONS
The Second Major in Data Analytics is applicable to the following CoE programmes from which prospective students can choose according to their interests and strengths.
Bachelor of Engineering:
- Aerospace Engineering
- Bioengineering
- Chemical and Biomolecular Engineering
- Civil Engineering
- Electrical and Electronic Engineering
- Environmental Engineering
- Information Engineering and Media
- Materials Engineering
- Mechanical Engineering
Bachelor of Science:
PROGRAMME STRUCTURE
Overview
The structure of the Bachelor of Engineering / Science with a Second Major in Data Analytics (DA) integrates the requirements of both majors, with between 6 - 12AU of double-counting, within the typical candidature of 4 years. Incorporating relevant courses across different schools to provide students with the foundation and practical tools for data analytics, the DA curriculum has been curated to ensure that students receive critical knowledge and skills in the following areas:
(A) Foundation in Mathematics, Statistics and Algorithms: The core courses in this group are focused on probability and statistics, linear algebra and algorithms/programming. It is essential that every data analyst or data scientist understands the theoretical underpinnings in order to be able to build reliable models with real-world applications.
(B) Essentials in Data Analytics: The core courses in this group are focused on database, data mining and data visualization/management. These courses aim to prepare students for key responsibilities of a data analyst which generally include designing and maintaining data systems and databases, mining data from primary and secondary sources, using statistical tools to interpret data for diagnostic and predictions, and visualization tools for reporting and communications.
(C) Advanced Electives in Data Analytics: With a variety of elective courses across different schools in COS and COE, students are able to gain in-depth exposure to artificial intelligence, neural network, machine learning, natural language processing and higher level courses in statistics, computations and algorithms.
Courses and AU Requirements
DA constitutes a total of 30 - 38AU, including 21 - 26AU of Compulsory Courses covering 7 key knowledge areas, as well as 9 - 12AU of data-related electives (the range of AU is due to a mix of 2, 3 and 4AU courses). Table 1 below shows the course options offered in each Knowledge Area as well as the electives that students can choose from. Some courses in Knowledge Areas 1 - 4 are Core or Major Prescribed Elective (MPE) in the respective primary Bachelor of Engineering / Science programme and can therefore be double-counted towards both majors. Please select your programme in Programme Options above to view the recommended courses and study plan.
Table 1: Courses and AU Requirements for the Second Major in Data Analytics
| KNOWLEDGE AREA | COURSES | AU |
|---|---|---|
| COMPULSORY (1 course required in each knowledge area) | ||
| 1) Probability and Statistics | • CH2010 Engineering Statistics (3AU) • IE4491 Probability Theory and Applications (3AU) • MA4849 Operations Research (3AU) • MH2500 Probability & Introduction to Statistics (4AU) • MH2814 Probability and Statistics (3AU) • MS4012 Quality Control (3AU) • SC2000 Probability and Statistics for Computing (3AU) | 3 - 4 |
| 2) Linear Algebra | • CB1117 Engineering Mathematics (4AU) • CV2019 Matrix Algebra and Computational Methods (3AU) • IE2107 Engineering Mathematics II (3AU) • MA2006 Engineering Mathematics (3AU) • MH1201 Linear Algebra II (4AU) • MH1804 Mathematics for Chemistry (2AU) • MH2802 Linear Algebra for Scientists (3AU) • MH2811 Mathematics II (3AU) • MT2004 Mathematics II for Maritime Studies (3AU) • SC1004 Linear Algebra for Computing (3AU) | 2 - 4 |
| 3) Data Analysis / Computing | • BG2211 / CH2107 Introduction to Computational Thinking (3AU) • BS1009 Introduction to Computational Thinking (3AU) • CV1014 Introduction to Computational Thinking (3AU) • IE1005 From Computational Thinking to Programming (3AU) • ES2001 Computational Earth Systems Science* (4AU) • MA1008 Introduction to Computational Thinking (3AU) • MH3511 Data Analysis with Computer (3AU) • MS1008 Introduction to Computational Thinking (3AU) • PS0001 Introduction to Computational Thinking (3AU) • SC1003 Introduction to Computational Thinking (3AU) | 3 - 4 |
| 4) Algorithms | • IE2108 Data Structure and Algorithms (3AU) • MH1403 Algorithms and Computing (3AU) • MS4671 Introduction to Materials Simulation (3AU) • SC1007 Data Structure and Algorithms (3AU) | 3 |
| 5) Database | • BC2402 Designing & Developing Databases (4AU) • IE4791 Database Systems (3AU) • SC2207 Introduction to Database* (3AU) | 3 - 4 |
| 6) Data Mining | • MH4510 Statistical Learning & Data Mining* (4AU) • IE4483 Artificial Intelligence & Data Mining* (3AU) • SC4020 Data Analytics and Mining* (3AU) | 3 - 4 |
| 7) Data Visualisation / Management | • BC2406 Analytics I: Visual and Predictive Techniques* (4AU) • SC4023 Big Data Management* (3AU) • SC4024 Data Visualization* (3AU) | 3 - 4 |
| Total AU for Compulsory Courses | 21 - 26 | |
| ELECTIVES (Minimum 9AU) | ||
| • BC2407 Analytics II: Advanced Predictive Techniques* (4AU) • BS3008 Computational Biology and Modeling* (3AU) • BS4017 High-Throughput Bioinformatics* (3AU) • CM4043 Molecular Modelling: Principles and Applications* (3AU) • CM4044 Artificial Intelligence in Chemistry* (3AU) • ES2001 Computational Earth Systems Science* (4AU) • MH3400 Algorithms for the Real World* (4AU) @ • MH3500 Statistics* (4AU) @ • MH3510 Regression Analysis* (4AU) @ • MH3511 Data Analysis with Computer* (3AU) @ • MH3701 Basic Optimization* (4AU) • MH4500 Time Series Analysis* (4AU) @ • MH4513 Survival Analysis* (4AU) @ • MH4302 Theory of Computing* (4AU) • MH4320 Computational Economics* (4AU) @ • MH4511 Sampling and Survey* (4AU) @ • MH4512 Clinical Trials* (4AU) • MH4702 Probabilistic Methods in OR* (4AU) @ • CH4244 Numerical Method and Data Analytics* (3AU) • IE4414 Machine Learning Design & Application* (3AU) • IE4497 Pattern Recognition & Machine Learning (3AU) • MA4829 Machine Intelligence (3AU) • MA4830 Real Time Software for Mechatronics System (3AU) • MA4832 Microprocessor System (3AU) • MS4671 Introduction to Materials Simulation (3AU) • SC3020 Database System Principle* (3AU) • SC4001 Neural Network and Deep Learning* (3AU) • SC4002 Natural Language Processing* (3AU) • SC4021 Information Retrieval* (3AU) • SC4022 Network Science* (3AU) | 9 - 12 | |
| Total AU for Second Major | 30 - 38 | |
* Pre-requisites apply. To enable students to access these courses, some of the official pre-requisites have been mapped to comparable courses students may have read in their respective programmes. Please see the list of approved alternative pre-requisites here.
@ These courses require MH2500 as one of the pre-requisites or earlier pre-requisites. Students are advised to plan accordingly. If MH2500 is taken, it can be used to fulfil Knowledge Area 1 in Probability & Statistics.
GRADUATION
Graduates of the Second Major in Data Analytics will be awarded a Bachelor of Engineering / Science with a separate certificate for the second major.
CAREER PROSPECTS
The Second Major in Data Analytics will open up a broad and diverse range of career prospects including but not limited to the following:
- Data Scientist
- Research Scientist
- R&D Engineer
- Business Intelligence Developer
- Data Analyst
- Data Architect
ADMISSION REQUIREMENTS
Candidates must meet the entry requirements of the primary Bachelor of Engineering / Science programme, including the minimum subject requirements. Please refer to the Office of Admissions for more information.
TUITION FEES AND SCHOLARSHIPS
The tuition fees for the Second Major in Data Analytics will be pegged to the fees of the primary Bachelor of Engineering / Science programme. Eligible students may also be considered for scholarships that include fully-paid
subsidised tuition fees and living allowances. Scholarship terms and conditions apply. For more information on tuition fees and scholarships, please refer to the Office of Admissions.
ACCREDITATION
All Bachelor of Engineering programmes at NTU College of Engineering are accredited by The Institution of Engineers Singapore, the Singapore signatory of the Washington Accord, through its Engineering Accreditation Board. The Washington Accord is an international agreement for mutual recognition of the substantial equivalence of engineering academic programmes in satisfying the academic requirements for the practice of engineering at the professional level.
INFORMATION AND ENQUIRIES
For more programme information or enquiries, please refer to our contact list here.