Practice Module for Engineering Big Data

Objectives

The objective of the practice module is twofold:

•  Firstly, to expose participants to real world problems so that they may practice the use of the skills they have learned during the component courses in a holistic manner.
•  Secondly, to enable participants to demonstrate their proficiency across all of the skills that they have learned in the course modules and hence obtain a grade at the Graduate Certificate Level.

Intended Audience

This practice module is targeted at participants who wish to complete the certification process for the Graduate Certificate in “Engineering Big Data”.

Prerequisites

Participants must have successfully obtained a competent score (or have been exempted from) the three component courses for the ‘Graduate Certificate in Engineering Big Data’ as listed in the introduction to the Graduate Certificate page.

Components

There are two parts to the Practice Module.

1. Practice Project: Participants will need to undertake one or more projects to gain practical experience and demonstrate their understanding and mastery of the skills taught in the three component courses. The practice project will require each participant to expend an estimated 10 days of effort. These days are not expected to be contiguous and may stretch over many weeks. These projects may be conducted by individual participants or in teams depending on the nature of the project requirements.
2. Examination: Each participant is required to sit for an examination on a stipulated date and time.
3. The overall grade for the participant will be based on the practice project and examination.

Typical examples of projects to be undertaken

1. Problem description:

   A taxi company would like to open up its internally operated delivery platform to public customers via mobile app. The potential of this project can be to build a real time taxi service. This requires integrating multiple disparate large-scale data sources to derive data-driven insights. This would also require building scalable functional resilient distributed data with spark framework and different databases. The taxi is built on streaming services to provide real time services such as driver tracking, hailing and dynamic messaging. The big data architecture needs to be analysed, architected, designed, implemented and tested to ensure that they derive the business requirements originally conceived and also helps mining data driven insights that will improve business decision making. The solution is composed of the end-to-end big data solutions starting from ingestion to storage to processing and optionally visualization.

   Deliverables and success criteria:

   • Identify polyglot persistence need and design appropriate NoSQL and NewSQL solutions as deemed appropriate based on business requirements.
   • Identify the data sources and formulate relevant ingestion patterns.
   • Build scalable resilient distributed big data solutions using analytics libraries.
   • Build automated big data solutions using machine and deep learning algorithms.
   • Build scalable real time systems that can process continuously changing data streams.
   • Devise appropriate test use cases and verify the effectiveness of the solution built.
2. Problem description:

Another potential project can be to build an integrated 360-degree customer dashboard for an ecommerce. The solution helps customer with personalised recommendations for products, customized event ads, and an entire bevy of buyable goods and services curated just for them.  Data of customer behaviours is collected from all gadgets the customer uses to interact with the ecommerce solution, which includes smart-phones, tablets, and desktops. The big data solution can be built using scalable functional resilient distributed data with spark framework and different databases. The solution will provide 360-degree perspective of customer behaviours so that to the ecommerce will be able to produce effective business recommendations. The solution is composed of the end-to-end big data solutions starting from ingestion to storage to processing and optionally visualization.

Deliverables and success criteria:

• Identify polyglot persistence need and design appropriate NoSQL and NewSQL solutions as deemed appropriate based on business requirements.
• Identify the data sources and formulate relevant ingestion patterns.
• Build scalable resilient distributed big data solutions using analytics libraries.
• Build automated big data solutions using machine and deep learning algorithms.
• Build scalable real time systems that can process continuously changing data streams.
• Devise appropriate test use cases and verify the effectiveness of the solution built.

Some popular real-world examples of types of projects to be considered are as follows:

1) Grab

2) Shopee

3) PropertyGuru

Application (For Stackable Students)

• Participants are only allowed to take the practice module after completing all courses in the Grad Cert.
• Participants who wish to take the practice module concurrently in the same semester with the courses in the same Grad Cert must write to ask-iss@nus.edu.sg citing reasons by the application deadline. Email requests received after the deadline will not be considered. Requests will be reviewed after the deadline and approved on a case-by-case basis.
• Participants who miss the application window will have to apply for the practice module in the next semester.
• Participants who do not attend the briefing will be withdrawn from the practice module.