With the emergence of new smart technologies such as the Industrial Internet of Things (IIoT) and Industry 4.0, data is being generated at a phenomenal rate with most of the generated data being unstructured.  This type of data may not be immediately usable for deep analysis and needs to be processed to allow it to be used effectively to derive insights.  This is where data engineering comes in.  Data engineers build tools, infrastructure, frameworks and services which allow them to tease insights from the myriad of data streams being generated.  They have the ability to collect, curate, analyse and visualise data in all its forms for intelligent decision making and sense-making.

Forbes Magazine predicts that technology-related jobs will experience a 12.5% growth over a period of ten years, with the data engineer being one of the top six most in-demand technology-related jobs.  Due to the pervasiveness of data and the critical value of data engineering skills, graduates with this Minor will be attractive to a wide range of companies in diverse industries.  Graduates should be able to find employment in local, regional and global companies in the semiconductor, telecommunications and networking, healthcare, transportation, energy, security and financial industries, as well as at research institutes and organizations.

The main aim of the Minor Data Engineering programme is to train graduates with the ability to handle and manage the large volume of data generated by industry and glean actionable insights from that data. It is open to CDE Engineering and SoC undergraduate students who have fundamental computational and engineering training.

The Minor is launched from AY 2020/21. It is offered to students of selected programmes at the point of admission from cohort AY2019/20 onwards. Exceptionally good students may also opt to take the Minor by Semester 5 of their study to align to the NUS major/minor enrolment policy.  Students in this Minor are expected to complete their degree within the normal candidature period of 4 years.

Requirements

Students in the Minor DE are required to complete a minimum of 20 Units consisting of 4 mandatory core courses and at least 1 elective course.

Minor DE Core Courses	Recommended Elective Courses (Choose one from the list)
EE3801 Data Engineering Principles  *   IT2002 Database Technology and Management or CS2102 Database Systems   EE4802/IE4213 Learning from Data ** or CS3244 Machine Learning / CS3263 Foundations of Artificial Intelligence  /  CS3264 Foundations of Machine Learning or IT3011 Introduction to Machine Learning and Applications   CS4225 Big Data Systems for Data Science	BT4015 Geospatial Analytics EE4704 Image Processing and Analysis EE5907 Pattern Recognition IE4210 Operations Research II IE4211 Modelling and Analytics IE4243 Decision Modeling and Risk Analysis

Advisory:

*    Minor in DE students should take EE3801 before EE4802.

**  Engineering students should take EE4802/IE4213

Students should be familiar with a scientific programming language such as Python. All assignments in class will be done in Python.

Note: NUS policy on Minor Double-counting rules

This minor is open to Engineering students and # School of Computing (SoC) students.

# All SoC minors and 2^nd majors should not be taken together with minor in DE.   For specific queries relating SoC minor, SoC 2^nd majors and minor DE, pls email socug@comp.nus.edu.sg

For general queries on minor DE, pls email ECE Special Programmes with your full name, student number, degree program name and home dept.

This minor is not open to students under Data Science & Analytics degree program in (CHS) College of Humanities and Science.

Registration:    https://www.nus.edu.sg/modreg/academic-plan-application-declaration.html

Minor in Data Engineering (DE) is a restricted minor i.e. application is subject to approval.   Students submit application via myEduRec.  Application is open only to Year 2 - Engineering students and # School of Computing (SoC) students from start of reading week to end of 1st week of examinations – in semester 2 of the Academic Year.