Practice Module for Big Data Analytics

Objectives

• Firstly, it exposes participants to real world problems so that they learn to practice the use of the skills they have learned during the component courses in a holistic manner.
• Secondly, it enables participants to demonstrate their proficiency across all 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 the participants who wish to complete the certification process for the Graduate Certificate in “Big Data Analytics”.

Components

Practice Module Agenda:

There are two parts in 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 man days of effort. These days are not expected to be contiguous and may stretch over many weeks and months. These projects may be conducted by individual participants or in teams depending on the nature of the project requirements. Participants are expected to understand business requirements for Big Data Analytics projects, identify multiple data sources, build sophisticated hybrid analytics model and apply key data analytic techniques to find out the insights and solutions.
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:

One of the potential project can be the urban planning by using large amount of user data.

With the continuing urbanization of the world’s population and the rapid growth of cities, urban planners are facing more challenges, such as heavily congested roads and overzealous developments. In order to address these problems, the urban planners need to develop a better method to understand the city dynamics. Logging anonymized Call Detail Records (CDR) data is one of the methods to capturing city dynamics. Considering with other data sources such as the Geospatial data and traffic data, urban planners develop strategies according to the analysis of CDR data:

• To investigate how much can be learned from these data.
• To identify the people density using crowd analysis.
• To understand mobility patterns

Deliverables and success criteria:

• devise data processing techniques taught within the certificate
• implement feature engineering algorithm to extract features from raw data
• implement data analytics techniques taught within this certificate
• interpret analytical results and demonstrate the insights from the results

   

1. Problem description

   A smart home environment usually consists of a number of devices within the house including sensors, actuators, appliances like air-conditioner, fridge, TV etc. In a typical smart home environment, Internet of Things is formed by the integration of these components. Such environments are designed to help make living experiences better and secure through big data processing and by providing personalized services. As a smart home projects, it should do the following:

   • To investigate the smart home sensor data
   • To identify a potential application of smart home
   • To make use of all the sensor data available to make the home smarter

   Deliverables and success criteria:

   • devise data processing techniques taught within the certificate
   • implement feature engineering algorithm to extract features from raw data
   • implement data analytics techniques taught within this certificate
   • interpret analytical results and demonstrate the insights from the results

Some popular real-world projects to consider are as follows:

1) E-commerce

2) Online retailing analysis

3) IT log analytics

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.