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Introduction to Analytics Practicum

The Analytics Practicum module (ANLY482) is a compulsory module for those who are taking the Analytics Second Major program. It involves a project that assess the students' ability to apply analytics in real-time events extensively. These projects come from both the academics and industry. Students can also get a good sense of how analytics are used in their field of study.

Welcome to the Analytics Practicum (ANLY482)! -

Practicum Projects for AY2014/2015 Term 2

Team	Project Name	Student Member(s)	Project Supervisor	Sponsor
Social Media & Public Opinion	Unstructured data is challenging and when it comes to unstructured textual data the analytical toolkit needs more tools! This project aims at quantifying and studying the trends in human emotions expressed by Twitter users over a period of time. The data-set provided comprises of social media data in form of tweets published by Singapore-based Twitter users over several months. Individual teams have to come up with their granular analysis of change in mood trends, periods of significance (may be weekends or any weekday) and other noteworthy actionable insights coming out of analysis done. It is expected that the results are presented as a web-based visualization that summarizes the trend of the happiness level over time and allows the inspection of factors associated with the happiness level at a certain point in time. Also, it should provide the end-users with various drill-down features to choose from while interacting with the end system. Types of analysis: Text mining and analytics is a vast topic so to help you get started we suggest you look into techniques like Sentiment analysis, word stemming, word frequency analysis, social network analysis (centrality, network diameters and density etc.), Influencer analysis and visual analytics techniques. File:ReferenceDocument01.docx Suggested Platforms: SAS EM, Python, R, Gephi, NodeXL (Microsoft) Libraries to explore: SAS EM sentiment analysis package, NLTK tools & libraries (both Python 2.7 and R), D3.js, C3.js Other recommendations: No prior knowledge of any courses is assumed while designing this project but prior knowledge of Social and Contextual Analytics, Visual Analytics will be good for taking up advanced analysis during project related tasks. Recommended Team Composition: Students are free to come up with their own teams but forming a team with diverse backgrounds and skill-sets is highly recommended.	Kean KWOK Jin Miguel Nicholas Sherman TAN	Prof. Seema Chokshi Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics Prof. KAM Tin Seong Associate Professor of Information Systems Senior Advisor, SIS Programmes in Analytics	Palakorn Achananuparp Research Scientist at LARC
Emergency Department and Queuing Theory	Queuing theory and optimization has been a problem of interest in the field of computer science, operations and analytics for a while now. It finds applications in the fields of traffic engineering, telecommunications, banks, hospitals and many other operational research related use cases. various models and algorithms have been constructed so that queue lengths and waiting times can be predicted. The current project applies the knowledge of queuing theory and hospital related domain knowledge in dynamically managing queues in emergency department. Suggested Platforms: SAS EM, Python, R Other recommendations: In order to get a better understanding of subject matter you may first read through the links provided below and materials on queuing theory and discrete event simulations. Improving Patient Length-of-Stay in Emergency Department Through Dynamic Queue Management Code Blue Analisys of hospital bed capacity via queuing theory and simulation Research on genetic optimization algorithm of queue rules based on simulation model Smart Priority Queue Algorithms for Self-Optimizing Event Storage Recommended Team Composition: Students are free to come up with their own teams but forming a team with diverse backgrounds and skill-sets is highly recommended.	Jinq-Yi Marcus TAN Muhammad Faris	Prof. Seema Chokshi Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics Prof. KAM Tin Seong Associate Professor of Information Systems Senior Advisor, SIS Programmes in Analytics	Prof. TAN Kar Way Assistant Professor of Information Systems (Practice)
Sustainability Jobs: Analyse the trends	The Green Transformation Lab (GTL) is a joint initiative by SMU and DHL aimed at accelerating the evolution of sustainable logistics across Asia Pacific and creating solutions that help companies transform their supply chains, becoming greener, more resource efficient and sustainable. GTL’s Sustainability Heat-map is a group of heat-maps with global profiling of areas and trends on: a. sustainability-related jobs and b. sustainability-related topics on Twitter. This project aims at analyzing the crawled data to get deeper actionable insights and answer questions such as: a. Is there a trend in the type of sustainability jobs in the various regions, e.g., Europe, Asia, America? b. Why does sustainable jobs matter? Is it more attractive to potential employees? Or is it policy driven in some countries? c. Which is the more important measurement? Number of sustainability jobs per unit GDP or number of sustainability jobs per person? Could there be any other measurements? Types of analysis: Students need to understand the term sustainability well and its applied meaning in sustainable job environment. This project requires text mining and analytics with stress on sustainability as the key area to look into. You may look into techniques like clustering, noun phrase analysis, social network analysis (centrality, network diameters and density etc.) and visual analytics techniques. You need to (but not restricted to) unravel trends in sustainability jobs in various domains across the globe, see what kind of profiles come under this term and examine if any patterns emerge. File:ReferenceDocument03.docx Suggested Platforms: Python, R, Gephi, NodeXL (Microsoft), Tableau Libraries to explore: SAS EM sentiment analysis package, NLTK tools & libraries (both Python 2.7 and R), D3.js, C3.js Other recommendations: Students are free to bring in any data inputs which they see fit to support their findings such as any government policy that mandates issuance of sustainability jobs in a particular field. No prior knowledge of any courses is assumed while designing this project but prior knowledge of Social and Contextual Analytics, Visual Analytics will be good for taking up advanced analysis during project related tasks. In order to get a better understanding of subject matter you may first read through the links provided below and materials. GT Lab gLab SMU Recommended Team Composition: Students are free to come up with their own teams but forming a team with diverse backgrounds and skill-sets is highly recommended.	TAN Siong Min Janice KOH TAY Hui Shia	Prof. Seema Chokshi Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics Prof. KAM Tin Seong Associate Professor of Information Systems Senior Advisor, SIS Programmes in Analytics	Prof. TAN Kar Way Assistant Professor of Information Systems (Practice)
Visualisation of Economies & Consumption Patterns	Arisaig Partners (Asia) Pte Ltd is an independent investment management company established since 1996. Yearly, Arisaig Partners holds a Consumer Symposium for potential clients around the globe, and wishes to increase the effectiveness of their presentation by building an interactive dashboard. The project covers two main research areas: Macro: Demographics & Economic indicators Micro: Individual sector matrices Considering the research areas and sample data, the project is to be as followed: Macro Visualisation #1: Exploration of Demographic Data Relationship a. Birth rate per women b. Average age of country/region c. Income d. Household size & Household Income Macro Visualisation #2: Exploration of Economic Data a. GDP, GDP growth rate b. Debt to GDP ratios, Savings rate c. Capital Market Size Micro Visualisations: Exploration of Consumption per Capita Platforms Considered: d3	Tan Kei Rong Benjamin Sean Chua Kian Shun Zoey Teo Kai Ying	Prof. KAM Tin Seong Associate Professor of Information Systems Senior Advisor, SIS Programmes in Analytics	Gordon Yeo, Investment Analyst Arisaig Partners (Asia) Pte Ltd
Network Analysis of Interlocking Directorates	Our project aims to review and analyse the interlocking directorates among corporate organizations in Singapore. While interlocking directorates is lawful and has its advantages, it may give rise to conflicts of interests and unfair advantages especially if the relationship is between public and private organizations. Using corporates and directorships database from OneSource, we will analyze the network of interlocking directorates amongst Singapore firms. Through our analysis, readers will be able to identify possible areas where conflicts of interest may arise between public and private organizations. They will also be able to observe patterns of interdependency between sectors in Singapore which might assist them in making better business decisions. Key features of our project includes: Visualization of the network of interlocking directorates Analysis of this network, focusing on the relationships from public to private sectors. Gathering useful insights from the analysis, which may be helpful the corporate leaders in decision-making in the future. Technologies considered: SAS EM, R, Gephi	Le Hoang Trinh Zheng Tianwei	Prof. KAM Tin Seong Associate Professor of Information Systems Senior Advisor, SIS Programmes in Analytics	Prof. KAM Tin Seong Associate Professor of Information Systems Senior Advisor, SIS Programmes in Analytics

Practicum Projects for AY2014/2015 Term 1

Team	Project Name	Student Member(s)	Project Supervisor	Sponsor
Kolaveri Di Social Analytics Project	"This Kolaveri Di" is a Tamil song from the soundtrack of Tamil film 3. It was written and sung by actor Dhanush and composed by music director Anirudh Ravichander. The song was officially released on 16 November 2011, and it instantly became viral on social networking sites for its quirky "Tanglish" lyrics. Soon, the song became the most searched YouTube video in India and an internet phenomenon across Asia. Within a few weeks, YouTube honoured the video with a Recently Most Popular Gold Medal Award for receiving a large number of hits in a short time. The objective of this project is to identify the key element(s) that explains the success of this video, particularly for its capability in drawing listeners and spreading its viral effect over the online domain. The analysts are required to submit a report, detailing out these elements along with some recommendations that could help to replicate its success.	Lee Jaehyun Chan Wei Yin	Prof. Seema Chokshi Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics	Prof. Srinivas K Reddy Professor of Marketing, Director, Centre for Marketing Excellence, Academic Director, LVMH-SMU Asia Luxury Brand Research Initiative, Area
Visualization of Consumer Satisfaction	Consumer research has been a hot topic. Businesses and government agencies are interested to know the satisfaction levels of Singaporean consumers and effectively take actions that can create valuable and meaningful impact in the society. This project explores these satisfaction levels. It uses the respondent level data from the Satisfaction Index of Singapore (2008-2013). The objective of this project is to produce a dashboard that shows trends of consumer satisfaction visually.	Mohamed Yousof Bin Shamsul Hameed Kee Eng Sen	Prof. Seema Chokshi Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics	Prof. Marcus Lee Assistant Professor of Marketing (Practice), Academic Director for the Institute of Service Excellence at SMU (ISES)
Twitter Analytics	The background of the project is horizon scanning; creating an analytical platform that is scanning the online (social/established data) to identify upcoming topics and keywords clusters. The objective is to not stop at the cloud creation but to be able to provide a time series analysis and forecast of the ‘relevance’ of the topic over the course of X number of days	Fransisca Fortunata	Prof. Seema Chokshi Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics	David Hardoon Head, Analytics, SAS Institute Pte Ltd, Singapore

Grading

Project Proposal

Update of Wikipage: 1%

Proposal Report: 14%

Mid-Term Presentation and Report

Mid-Term Presentation: 10%

Mid-Term Report: 15%

Mid-Term Update of Wikipage: 5%

Final Presentation and Report

Final Report: 25%

Final Presentation: 15%

Project Poster: 10%

Update of Wikipage: 5%