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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.		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. Average Length of Stay (LOS) is an important KPI specially for private hospitals. During Phase one of the project specific strategies were formulated to improve patients' average length of stay (LOS) without affecting the usual patient influx. Under phase two (current scope), teams will be dealing with real hospital data in order to understand the ED process and test various strategies formulated during phase two on their applicability on real life data and provide further recommendations. Types of analysis: Try to verify if the proposed strategies in the framework indeed have an advantage over the existing methods (e.g., first-in-first-out (FIFO), random selection of patients by doctors for review patient and FIFO for new patients) with stress on ED process parameters such as arrival rates of patients, service rates of doctors, percentages of test/treatment ordered, any relationship between patient’s demographic and test order, estimation of consultation time, service rates of each test. File:ReferenceDocument02.docx 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.		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.		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)
Financial Analytics & Visual Modelling	Analytics has a wide spread presence in the field of finance. One can see analytics being applied on a daily basis in various areas of finance to achieve increased revenue, cost optimization, customer satisfaction, proactive risk management, and maximized return on marketing spends. During this project students will be provided with a financial data-set which needs to be thoroughly analysed. Students have to create a case study around the data provided based on their understanding and also build visual models which consume the data and present insights in a user friendly and easy to understand manner. Types of analysis: Based on their understanding and data-set provided students can explore multiple areas from the list below. This list is only suggestive and shouldn't be considered as complete. Students are allowed to bring in more analytical measurements through their work. 1) Risk Management a. Credit risk analytics and management b. Operational risk analytics and management c. Regulatory compliance d. Capital planning and forecasting 2) Treasury Analytics a. Market risk analytics and measurement b. Derivative valuation c. Counter-party credit risk d. Asset liability management e. Risk and compliance f. Model validation 3) Multi-Channel Customer Management a. Marketing analytics including cross-sell, campaign management, and loyalty analytics b. Contact center analytics and optimization File:ReferenceDocument04.docx Suggested Platforms: Python, R, MS Excel, Tableau Libraries to explore: Numpy,SciPy, The Rmetrics suite of packages, D3.js Other recommendations: No prior knowledge of any courses is assumed while designing this project but prior knowledge of 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. Ideally the team should have one or more students with Majors in Accounting and 2nd Majors in Analytics for this project		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. Gary PAN Associate Dean School of Accounting (Student Matters) Associate Professor of Accounting (Education) Prof. Venky-SHANKARARAMAN Associate Professor of Information Systems (Education) Associate Dean, Education

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%