Difference between revisions of "ANLY482 AY2015-16 Term 2"

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[[ANLY482_Current_Practicums: Current Practicums| <font color="#FFFFFF">Current Run</font>]]
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[[ANLY482_AY2015-16_Term_2| <font color="#FFFFFF">Current Run</font>]]
  
 
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[[ANLY482_AY2015-16_Potential_Projects: Project Up For Grabs| <font color="#FFFFFF">Projects Up For Grabs!</font>]]
  
 
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<div style="background: #fdf5e6; padding: 13px; font-weight: bold; text-align: left; line-height: wrap_content; text-indent: 20px;font-size:20px; font-family:helvetica"><font color= #3d3d3d> Past Runs</font></div>
 
<b>[[AY2014_Term_2|Practicum Projects for AY2014 Term 2]]</b><br>
 
<b>[[AY2014_Term_1|Practicum Projects for AY2014 Term 1]]</b><br>
 
<b>[[ANLY482_AY2015-16_Term_2|Analytics Practicum Projects for AY2015-16 Term 2]]</b><br>
 
  
<div style="background: #fdf5e6; padding: 13px; font-weight: bold; text-align: left; line-height: wrap_content; text-indent: 20px;font-size:20px; font-family:helvetica"><font color= #3d3d3d> Current Run</font></div>
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<div style="background: #fdf5e6; padding: 13px; font-weight: bold; text-align: left; line-height: wrap_content; text-indent: 20px;font-size:20px; font-family:helvetica"><font color= #3d3d3d>List of Projects</font></div>
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<table class="wikitable centered" width="100%" color="blue">
 
<table class="wikitable centered" width="100%" color="blue">
 
<tr>
 
<tr>
<th width=10%>Team</th>
+
<th width=10%>Title</th>
<th>About the Project</th>
+
<th>Analytics Practicum Description</th>
 
<th width=150>Student Member(s)</th>
 
<th width=150>Student Member(s)</th>
 
<th width=150>Project Supervisor</th>
 
<th width=150>Project Supervisor</th>
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<tr>
 
<tr>
<td>[[Terrorism : Unraveling Hidden Patterns]]</td>
+
 
 +
<td>[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY1516_G1_Team_Skulptors Optimizing Warehouse Processing]</td>
 +
<td>The practicum’s project sponsor is a supply chain and logistics company. Currently, its warehouse data are not being utilized. By providing its warehouse data (inbound and outbound) transactions to the team, the sponsors would like the team to create a dashboard to help solve 3 main issues. Firstly, to help sponsor categorize warehouse SKUs into ABC categories (each category refers to how fast the goods move) to facilitate optimizing storage of SKUs in warehouse. Secondly, to provide employees with a high level visualization of the SKUs movement into, within, and out of the warehouse. Lastly, to analyze and suggest possible alternatives to improve the picking process within the warehouse (order picking VS batch picking).
 +
</td>
 
<td>
 
<td>
The purpose of the practicum is to acquaint students with quantitative studies of terrorism. The dataset includes the merger of two kinds of datasets. The first is a dataset of terrorist organizations and their attributes (ideology, size, age, funding, etc). The second is a dataset of events or terrorist attacks and their attributes (date, target, weapon used, people killed, etc) from 1967-2011. The goal is to 1) find a stable pattern, 2) display it visually, and 3) attempt to explain it or at least identify some factors that correlate with it. Those should be theoretically informed. You can also display the way the factors are correlated with explaining the pattern.
 
  
 +
'''[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY1516_G1_Team_Skulptors Group01- Team Skulptors]'''
 +
* LEONG Junkang, Gabriel
 +
* TAN Siying
 +
* ZHOU Xuanyi
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>
 +
A local multi-national corporation (MNC)
 +
* Mr Khoo, Head of Operation Innovation & Development (Corporate)
 +
* Miss Shen, Executive, Operations Innovation & Development (Corporate)
 +
* Mr Sundar, Process Improvement Executive (Automation)
 +
</td>
 +
</tr>
  
'''Types of analysis''':  <br/>
+
<tr>
Theme: At the big-picture level, the project should focus on two aspects of terrorism:  tactics and targets of terrorist organizations. For example you may consider how a particular tactic such as the use of car bombs has spread across the globe. Alternatively you may examine how there has been a shift in targets over time. <br/>
+
<td>[https://wiki.smu.edu.sg/ANLY482/Analysis_of_User_and_Merchant_Dropoff_for_Sugar_App Analysis of User and Merchant Dropoff for Sugar App]</td>
 +
<td>Sugar is an interactive city guide that seeks to encourage a culture of exploration in Singapore and helping local small businesses get discovered. Sugar’s merchants are mainly small local businesses in Singapore. It has a large variety, including cafes, small restaurants, bars, hair salons, gyms, gift shops. The benefits for merchants is advertising to users that are in close proximity to them. Users in turn get discounts on products that are in the closest proximity to them.
  
Here are some ideas that express this theme:  <br/>
+
As Sugar is a relatively young startup in Singapore, it has not yet attained a critical mass of user and merchant numbers. As such, user growth and user experience is vitally important. To reach this critical mass, Sugar needs to minimize user and merchant attrition, and retain vital segments of both groups. Furthermore, as a two-sided market, Sugar needs the network effect and also find out which group(Users or Merchants) adds more value to their bottomline.  
Idea 1: One particular area that is relevant to Singapore is what I call Proxy Terrorism, which is terrorism aimed at a third-party. For most terrorist acts the targets are coterminous with the countries that are being attacked. The target in country A is meant to punish country A. However, some terrorist acts seek to attack targets in country A to punish country B. Examples of such attacks include tourist areas and embassies. In Singapore’s history the single successful terrorist attack – the Laju Incident – was a proxy attack. Similarly plots by Jemaah Islamiyah, that were successfully foiled, were against foreign embassies in Singapore. Neighborhood groups in Indonesia and Malaysia also engaged in proxy terrorism against tourist resorts in those countries or other proxy targets.
 
To illustrate with the above example, you may find that proxy terrorist attacks have declined precipitously or are clustered in a particular geographic area. You might display a graph over time and you may identify some factors or some conditions that make proxy terrorist attacks more likely.  <br/>
 
Idea 2: Most terrorist organizations attack both civilians and military targets. You might find that a given terrorist organization’s civilian:military ratio tends to increase over the lifespan of the terrorist organization. You might illustrate this by displaying a series of graphs over time. It may be that terrorist organizations that are ethnonationalist are more likely to have this ratio increase, while terrorist organizations that are religious tend to have this ratio decrease. <br/>
 
We will soon share a document with more ideas to help you.
 
  
<br />
+
The objective of our project will be to improve Sugar's bottomline via
 +
Merchant Analysis <br />
 +
User Analysis <br />
 +
Two-Sided Market Analysis <br />
 +
Geospatial Analysis <br />
  
'''Suggested Platforms''': SAS Visual Analytics suite; tableau or D3.js for developing custom visualizations<br />
+
We will be using several techniques such as funnel plots, time series analysis, shortest distance analysis(Geospatial) and regression modeling to get insights and subsequently deriving recommendations for Sugar to increase its revenue and growth.</td>
 +
<td>
 +
'''[https://wiki.smu.edu.sg/ANLY482/Analysis_of_User_and_Merchant_Dropoff_for_Sugar_App Group02- Team TurnKEY]'''
 +
* Kang Long
 +
* Elizabeth Tan
 +
* Yi Sheng, Lim
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>
 +
Benjamin Lee
  
'''Libraries to explore''':  SAS VA, D3.js, C3.js<br />
+
Founder and Chief Executive Officer of Sugar Technologies Pte Ltd.
 +
</td>
 +
</tr>
  
'''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.<br />
+
<tr>
 
+
<td>[https://wiki.smu.edu.sg/ANLY482/Teppei_Syokudo_-_Improving_Store_Performance Teppei Syokudo - Improving Store Performance]</td>
'''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.<br/>
+
<td>Teppei Syokudo is a Japanese Food and Beverage chain, under the umbrella of the famous Teppei Japanese Restaurant. In order to drive store performance through controllable factors, Teppei Syokudo is looking at focusing on staff performance. Most F&B businesses, including Teppei Syokudo, do not set detailed KPIs to evaluate how their staff are performing. If Teppei Syokudo is able to track the performance of their staff through relevant KPIs, they will be able to motivate staff to meet these KPIs, which will in turn boost the business’ bottom line.Teppei Syokudo has identified the following KPIs to assess their staff:
More information on the dataset will be shared soon.
+
*Percentage of drinks sales (number of drinks sold / number of meals sold)
 +
**a measurement of how hard the staff are up-selling
 +
*Labour Productivity (sales $ / working hours)
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**a measurement of how effective the shop manager is in staffing the shop
 +
However, the business is uncertain if these are the right KPIs to set. Also, if they are the right ones, they are unsure as to what would be a good target to meet.
 +
Another factor for driving store performance is through product portfolio mix. Even though the staff may be up-selling and cross-selling, they may not know the right products to cross-sell to increase the probability of the customer making additional purchases. For example, most customers may tend to purchase Drink X together with Don X. In this case, if a customer orders Don X and is about to make payment, the staff can suggest Drink X to the customer, hence prompting a higher probability for the customer to purchase Drink X.
 
</td>
 
</td>
 
<td>
 
<td>
 +
'''Group03- Team APSM'''
 +
* TAN Jhun Boon
 +
* YAP Jessie
 +
* OH Peng Ho
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>Itaru Nagao,
 +
Managing Director
  
 +
YCP Management Southeast Asia Pte. Ltd.
 +
 +
YCP Retailing SEA Pte. Ltd.
 
</td>
 
</td>
<td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
+
</tr>
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
+
 
 +
<tr>
 +
<td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_Atom Car Park Overspill Study]</td>
 +
<td>The objective of this project is to assist Media Research Consultants Pte Ltd (MRC) in understanding the current parking situations in 65 different locations in Singapore. These 65 parking locations compromise of 30 retail malls, 15 retails and Food & Beverage (F&B) clusters in landed housing estates, 10 hawker centers, and 10 community clubs. </td>
 +
<td>
 +
'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_Atom Group04- Team ATOM]'''
 +
* Macus KHOO JunHao
 +
* YAN ShaoHong Chris
 +
* YO Wee King
 
</td>
 
</td>
<td>[http://www.smu.edu.sg/faculty/profile/108496/Michael-GENKIN Prof. Michael GENKIN]
+
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
Assistant Professor of Sociology, SOSS
+
Associate Professor of Information Systems (Practice)</td>
 +
<td>[http://www.mrconsultants.sg Media Research Consultants Pte Ltd]
 
</td>
 
</td>
 
</tr>
 
</tr>
  
 
<tr>
 
<tr>
<td>[[Improved Decisions for Ocean Freights]]</td>
+
<td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_AP Social Media Analytics]</td>
 
<td>
 
<td>
The Green Transformation Lab (GTL) is a joint initiative by SMU and DHL aimed at accelerating the evolution of sustainable logistics across Asia Pacific. Leveraging SMU’s multi-faculty academic excellence and DHL’s sustainability services, expertise and capability in supply chains, the Green Transformation Lab is focused on creating solutions that help companies transform their supply chains, becoming greener, more resource efficient and sustainable. <br/>
+
<p>The aim of this project is to provide deeper insight into SGAG's social network across its multiple platforms, namely Instagram and Twitter. </p>
As part of on-going effort to improve fill-rate of ocean freight, GTL needs a dashboard to visualize and allow decision-makers to select the most appropriate mode of shipment. There are two modes of ocean freight shipments: FCL (Full-container-load) and LCL (Less-than-container-load).<br/>
 
More information about general things that GTL does can be found at GTL official [http://gtl.smu.edu.sg website]
 
  
'''Types of analysis''':
+
<p>Our client is the co-founder of the company and he seeks insights that can spur growth in SGAG's follower numbers. Through our analysis and research, we aim to help discover what kind of users are on each platform; the key engagement leaders for each topic; and how wide is the reach of these individuals. </p>
The aim of this analytics project is to perform data analytics and build a dashboard using a visualization tool such as Tableau.
 
The features on the visualization tool includes (but not limited to):<br/>
 
a. Ocean freight profiling for the selected customer<br/>
 
b. Visualization of major trade-lanes (pair of source and destination of shipments)<br/>
 
c. Simple what-if analysis of a different mode of shipment is selected (e.g., customer choose LCL instead of FCL) on key defined KPIs such as cost, CO2 emissions
 
<br />
 
  
'''Suggested Platforms''': Project Sponsor would like the team to use Tableau for developing visualizations ;<br />
+
<p>The final deliverables will aim to: <br>
 +
# Visualise the social networks of SGAG
 +
# Identifying the user segments who engage SGAG's content, as filtered by topics and their reach
 +
</p>
 +
</td>
 +
<td>
 +
'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_AP Group05- Team AP]'''
 +
* WANG Shyan Ann
 +
* NG Tse Siong
 +
* Sherman YONG Chin Wei
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>Karl Mak, Co-founder at SGAG</td>
 +
</tr>
  
'''Other recommendations''': No prior knowledge of any courses is assumed while designing this project but prior knowledge Visual Analytics will be good for taking up advanced analysis during project related tasks.<br />
 
  
'''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.<br/>
+
<tr>
More information on the dataset will be shared soon.
+
<td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_SkyTrek Skyscanner Content Analysis]</td>
 +
<td>
 +
<p>
 +
The project aims to help Skyscanner analyse its content sites in order to facilitate more effective planning of news articles. It will help understand the factors that affect content performance.
 +
</p>
 +
 
 +
<p>
 +
The team will analyse content related data from multiple sources to determine what differentiates good and bad content based on certain performance metrics set by Skyscanner. This will be done through Text Based Cluster Analysis, Exploratory Modelling with logistic regression and Data Visualization using Tableau
 +
</p>
 +
<p>
 +
The deliverables include creating a dashboard with visualizations that will help Skyscanner team to better understand performance of content across different content channels.
 +
It will be used to validate some of the intuitions they might have about certain content topics/types and to determine the best time to publish them.
 +
The dashboard will benchmark certain metrics against pageviews as well as additional attributes that Skyscanner does not currently analyse via Google Analytics, such as the impact of title, text length, theme of article and number of images.
 +
</p>
 +
<p>
 +
Data set includes data from Skyscanner websites for the Singapore, Malaysia and Thailand markets.
 +
</p>
 
</td>
 
</td>
 
<td>
 
<td>
Terence CHU Tailun<br />
+
'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_SkyTrek Group06- Team SkyTrek]'''
PHANG Ming Min<br />
+
* Aseem PRABHAT
NG Zhen Jie
+
* Jedaiah TAN Jia Le
 +
* NGUYEN Viet Huy
 
</td>
 
</td>
<td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
+
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
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Associate Professor of Information Systems (Practice)</td>
</td>
+
<td>
<td>[http://sis.smu.edu.sg/faculty/profile/104156/TAN-Kar-Way| Prof. TAN Kar Way]
+
Ms. Antoinette Tan <br>
Assistant Professor of Information Systems (Practice)
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Content Manager, APAC<br>
 +
Skyscanner
 
</td>
 
</td>
 
</tr>
 
</tr>
 +
 
<tr>
 
<tr>
<td>[[Team BEK|Recommendations Matter to Us! - Team BEK]]</td>
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<td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_YSR Understanding Employee Social Networks]</td>
<td> Recommendations and opinions from others are a part of our daily lives. From eating out in a restaurants to buying commodities online we yearn to know what others have to say about them. We want to see what others have experienced from a purchase partly because it involves spending our hard earned money but largely because of the plethora of information on available options out there. Seeking recommendations is an attempt towards staying away from bad experiences and maximizing once sense of satisfaction from a purchase.<br/>
+
<td><p>People analytics is a rapidly growing area of business intelligence and big data technology. It uses various facets of people-related data to optimize business outcomes and solve business problems. The application of people analytics with new techniques such as predictive behavioural analytics has helped organisations to save millions of dollars while improving attrition rates, employee engagement and identify underlying training requirements.  
This project shares people's opinion and their ratings on Yelp about businesses operating in various cites from U.K., Germany, Canada to Unites States. It will give you an opportunity to apply exploratory and predictive analytics techniques such as n-gram analysis, topic identification, sentiment analysis, ratings prediction, item-item similarities to design a flow that can help users across the world find the place of their choice faster with improved precision. Stress will be given on how you design a general purpose model using restaurant related data which can be applied to any other practice in hospitality domain such as hospitals, hotels equally.
 
  
'''Types of analysis''': The teams can analyse the data to unravel many aspects such as:<br/>
+
TrustSphere is the widely recognized market leader in Relationship Analytics. TrustSphere enables forward thinking organizations to unlock the inherent value of their own networks using next generation technology. The solutions provide real-time intelligence and insights which help clients across the globe improve salesforce effectiveness, enterprise-wide collaboration and corporate governance.The motivation behind this project is to assist TrustSphere in verifying the effectiveness of their product through other statistical techniques.
1. What seasonal trends and patterns can be detected in data. Are there places being reviewed only on certain occasions or time periods around the year. Are their cities where people tend to eat out more then people from other cities. Try to predict what type of restaurants or bars will be reviewed more based on an upcoming event or festival.  
 
  
2. Clustering similar businesses, Item-Item to user-user similarities to develop and refine recommendations.
+
Access to TrustSphere’s datasets will allow the team to build a system from scratch using previously unused raw data to better understand turnover and attrition rules.
 +
 +
The minimum research points we would like to address:
 +
* Understand the number of relationships an employee will have at different periods of time in his or her working life
 +
* Measure the speed of growth at which employee relationships grow in a company
 +
* Correlations between the sizes of internal and external relationships employees have
 +
* Through social network analysis, calculate the likelihood of an employee in an informal group leaving a company upon the exit of another closely tied employee
 +
* Identification of metrics that can help predict the likelihood of an employee leaving
 +
</p></td>
 +
<td>
  
3. Sentiment analysis of English text, use of n-gram analysis to filter out prominent phrases and tips from the reviews, identification of topics of discussions. Identifying sarcasm and sarcastic reviewers. Identifying what are the main complainants, suggestions and wishes of reviewers.
+
'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_YSR Group07- Team YSR]'''
 +
* Ridwan Ismeer
 +
* Sadhvi Ilango
 +
* Yashraj Jalota
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>Greg Newman, Product Manager - TrustSphere </td>
 +
</tr>
  
4. Apply above mentioned analysis techniques to warn businesses when their overall image starts to go down, highlights new ideas for them to increase sales. By using a dashboard to display findings.
 
<br />
 
  
'''Suggested Platforms''': SAS Visual Analytics suite; SAS EM, python or R for text analytics; tableau or D3.js for developing visualizations<br />
+
<tr>
 +
<td>'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_HealthTics Health Analytics]'''</td>
 +
<td>To build a web application that utilises GIS functions for geospatial planning and analysis. This application aims to facilitate the computation and analysis of Health Promotion Board (HPB) KPI reporting metrics. The insights generated from the application will be used to manage Health Promotion Programme and Outreach Planning.
 +
</td>
 +
<td>
 +
'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_HealthTics_About_Us Group08 - Team HealthTics]'''
 +
* Erwin
 +
* Trinh Hiep Dang Khoa
 +
* Nyein Su Aye
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>[http://www.hpb.gov.sg/HOPPortal/ Health Promotion Board]</td>
 +
</tr>
  
'''Libraries to explore''':  SAS EM and SAS VA sentiment analysis package, Natural language tool kit & libraries (both Python 2.7 and R), scikit-learn (Machine Learning in Python), D3.js, C3.js<br />
+
<tr>
 +
<td>'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Group09_Project_Overview CodeBetter Analytics: Identifying Patterns in Learning]'''</td>
 +
<td>Students using a web portal to learn programming languages and frameworks will answer a series of questions, which have their timings and results recorded. An analytics dashboard will be built to read from a live Firebase, thus dynamically generating charts and information for students to view their progress. Course conductors may also use this dashboard to better understand the needs of their students and modify their course accordingly.</td>
 +
<td>
 +
'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Group09_Project_Overview Group09 - Team KyuuBI]'''
 +
* Shane GOH Ghee Gin
 +
* Darren LIM Fei Hong
 +
* LIM Wei Yang
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9606/Chris-BOESCH Prof. Chris Boesch]
 +
Associate Professor of Information Systems (Education)</td>
 +
</tr>
  
'''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.<br />
+
<tr>
 +
<td>'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_CommuteThere Commuting Patterns in Singapore]'''</td>
 +
<td>
 +
The main aim of this project is to understand how the urban form affects one’s decision to either use public transport or to walk by focusing on Tampines as our case study
 +
 
 +
 
 +
<b>The objectives of our project are:</b><br>
 +
Analyse commuting patterns for the 3 different age groups (i.e Adult, Elderly and Student)<br>
 +
• <u>Overall</u> commuting patterns for the 3 different age groups <br>
 +
• <u>Inter town</u> commuting patterns for the 3 different age groups<br>
 +
• <u>Intra town</u> commuting patterns for the 3 different age groups<br>
 +
• <u>Intra town</u> commuting patterns for the 3 different age groups who made at least 4 trips in one week during morning peak period<br>
 +
 
 +
Analyse multimodal transportation patterns for 3 different age groups<br>
 +
• <u>Transfer time interval</u> for buses and MRT for the 3 different age groups <br>
 +
• Relationship between walking and bus transportation using Student group as a proxy<br>
  
'''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.<br/>
 
More information on the dataset in this [[File:ReferenceDocumentYelp.docx]]
 
 
</td>
 
</td>
 
<td>
 
<td>
Keith TAN Xiang Wei<br/>
+
'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_CommuteThere Group10 - CommuteThere]'''
YAO Min Gee<br/>
+
* Jeanne Sim Peh Wuen
ZHENG Boyang
+
* Lim Hui Ting
 +
* Jaclyn Lim Hui Ting
 
</td>
 
</td>
<td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
+
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
+
Associate Professor of Information Systems (Practice)</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
</tr>
 +
 
 +
<tr>
 +
<td>'''[https://wiki.smu.edu.sg/ANLY482/Uncovering_Market-Insights_for_Charles_&_Keith Uncovering Market-Insights for Charles & Keith ]'''</td>
 +
 
 +
<td>This project aims to help Charles & Keith gain a better understanding of its China market through the use of data-analytics. With the market insights gathered, provide recommendations for the business on how they may apply them to its business to positively affect consumer buying decisions and capture more market share within China.
 +
Key objectives of this project involves:
 +
* Visualise the overall generic consumer purchasing patterns and behaviour within China
 +
* Identify best-selling products and item set for different regions or tiers of cities
 +
* Highlight the differences between transactions of different locations in terms of product specifications or types
 +
 
 +
Some business areas where the project findings could be applied:
 +
* Strategic placement of products within product catalogues
 +
* In-store shelving decision
 +
* Strategic marketing promotions to cross-sell and upsell, at a localized market level
 +
* Suggest Market Basket Analysis algorithm solutions for C&K’s e-commerce site
 +
 
 +
Data set includes sales transaction data from all of Charles & Keith’s stores in China.</td>
 +
<td>
 +
'''[https://wiki.smu.edu.sg/ANLY482/Uncovering_Market-Insights_for_Charles_&_Keith Group11-Team AYE]'''
 +
* Audrey Jee Zhi Ying
 +
* Edwin Tan Soon Hong
 +
* Toh Yan Ying
 
</td>
 
</td>
<td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
+
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
+
Associate Professor of Information Systems (Practice)</td>
 +
<td>Charles & Keith Group</td>
 +
</tr>
 +
 
 +
<tr>
 +
<td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Griffins Health Promotion Board GIS Application]</td>
 +
<td>Our objective is to build an interactive and visual web application that utilises GIS functions for geospatial planning and analysis. It will be able to compute and analyse HPB KPI reporting metrics. The web application should be easy to use to support the staff both technical and non-technical in their many Health Promotion programmes and outreach planning. It will also assist the staff in clear and easy to understand presentations to the executives who have little or no background in GIS analysis so as to be able to plan their operations. </td>
 +
<td>
 +
'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Griffins Group12 - Griffins]'''
 +
* Melissa Lim Seok Yu
 +
* Tan Wei Liang
 +
* Tan Yu Ling
 
</td>
 
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>[http://www.hpb.gov.sg/HOPPortal/ Health Promotion Board]</td>
 
</tr>
 
</tr>
 +
 
<tr>
 
<tr>
<td>[[Team Accuro|Recommendations Matter to Us!]] - Group 2 (Team Accuro)</td>
+
<td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team13_Natasha_Studio Natasha Studio]</td>
<td> Recommendations and opinions from others are a part of our daily lives. From eating out in a restaurants to buying commodities online we yearn to know what others have to say about them. We want to see what others have experienced from a purchase partly because it involves spending our hard earned money but largely because of the plethora of information on available options out there. Seeking recommendations is an attempt towards staying away from bad experiences and maximizing once sense of satisfaction from a purchase.<br/>
+
<td>Natasha Studio is a dance studio in Singapore that offers various genres of street dance lessons to interested individuals, including Bboying, Hiphop and Kpop. Currently, the company has no formal sales management system and is looking to apply analytics to create more customer satisfaction and increase the competitiveness of the business. Based on discussions with both our client and project sponsor, our business objective is to provide Natasha Studio with recommendations for class types, packages and dance genres that it should offers.  
This project shares people's opinion and their ratings on Yelp about businesses operating in various cites from U.K., Germany, Canada to Unites States. It will give you an opportunity to apply exploratory and predictive analytics techniques such as n-gram analysis, topic identification, sentiment analysis, ratings prediction, item-item similarities to design a flow that can help users across the world find the place of their choice faster with improved precision. Stress will be given on how you design a general purpose model using restaurant related data which can be applied to any other practice in hospitality domain such as hospitals, hotels equally.
 
  
'''Types of analysis''': The teams can analyse the data to unravel many aspects such as:<br/>
+
To achieve this business objective, the technical goal would be to first create a proper database system to aid in data recording and ensure consistency of data. This would then lay the foundation for our team to apply techniques like market basket analysis to identify customer purchasing behaviours and propose appropriate business actions for Natasha Studio.
1. What seasonal trends and patterns can be detected in data. Are there places being reviewed only on certain occasions or time periods around the year. Are their cities where people tend to eat out more then people from other cities. Try to predict what type of restaurants or bars will be reviewed more based on an upcoming event or festival.  
 
  
2. Clustering similar businesses, Item-Item to user-user similarities to develop and refine recommendations.
+
Thus, our final deliverables would be as follows:
  
3. Sentiment analysis of English text, use of n-gram analysis to filter out prominent phrases and tips from the reviews, identification of topics of discussions. Identifying sarcasm and sarcastic reviewers. Identifying what are the main complainants, suggestions and wishes of reviewers.
+
1. Relative Database Management System with a User Interface
 +
2. Identification of Customer’s purchasing behaviours and thus offer appropriate business recommendations</td>
 +
<td>
 +
'''Group13 Team Ameilax'''
 +
* Sng Ei Leen
 +
* Wong Jia Wei
 +
* Tan Ziling Amy
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>Larry Liu
 +
Business Manager
 +
Natasha Studio</td>
 +
</tr>
  
4. Apply above mentioned analysis techniques to warn businesses when their overall image starts to go down, highlights new ideas for them to increase sales. By using a dashboard to display findings.
+
<tr>
<br />
+
<td>'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_Hew Exploratory Analysis of Indonesian Motor Insurance ]'''</td>
 +
<td>To conduct an exploratory analysis on Tokio Marine Indonesia's Motor Insurance segment. Factors that will be explored include (a) Policy growth, (b) Underwriting Profitability and (c) Claim Insights</td>
 +
<td>'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_Hew Group14- Team HEW]'''
 +
* Huai Zher
 +
* Elijah Tan Yi Rong
 +
* Winston Ong Bing Shen
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>Benito Mable
 +
Vice President, New Opportunities
 +
Tokio Marine Asia</td>
 +
</tr>
  
'''Suggested Platforms''': SAS Visual Analytics suite; SAS EM, python or R for text analytics; tableau or D3.js for developing visualizations<br />
+
<tr>
 +
<td>[https://wiki.smu.edu.sg/ANLY482/T15_Home Singapore Students Performance Analysis] </td>
 +
<td>
 +
This project makes use of PISA (Programme for International Student Assessment) data collected during the latest survey of 2012 with regards to Singapore. The aim of this project is to explore the relationship between computer usage in school and secondary-school student performance in reading and mathematics. Building on the current international work done by PISA, our project will bring the analysis to Singapore national level and study various aspects of students of performance relative to their access to computer in and outside of school, in order to provide insights for education policy makers of Singapore Ministry of Education (MOE).
 +
</td>
 +
<td>
 +
'''Group15'''
 +
* Nguyen Le Hong Ngoc
 +
* Poh Jin Hui
 +
* Zhao Yazhi
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
</tr>
  
'''Libraries to explore'''SAS EM and SAS VA sentiment analysis package, Natural language tool kit & libraries (both Python 2.7 and R), scikit-learn (Machine Learning in Python), D3.js, C3.js<br />
+
<tr>
 +
<td>[https://wiki.smu.edu.sg/ANLY482/Car_Park_Overspill_Study_Home Car Park Overspill Study]</td>
 +
<td>Land Transport Authority(LTA) aims to do an analysis to understand the current parking situation at these selected locations. The LTA Contract Parking Study was awarded to Media Research Consultants in March 2015 to undertake the Study involving 65 car park locations in Singapore, including 30 retail malls, 15 retail and F&B clusters in landed housing estates, 10 hawker centres, and 10 community clubs.
  
'''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.<br />
+
This study was to conduct parking occupancy surveys, human traffic counts, and interview surveys at the selected locations at stipulated times. The study incorporated the conventional method of manual counting as well as deployment of automated counting equipment. Face-to-face interviews were employed for the interview survey segment.  
  
'''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.<br/>
+
In addition to the study and taking of detailed data from field surveys, this project aims to develop a simulation tool that enables the systematic analysis of the impacts of various parameters, using a collected set of quantifiable data.  
More information on the dataset in this [[File:ReferenceDocumentYelp.docx]]
 
 
</td>
 
</td>
 
<td>
 
<td>
Li Xiang<br>
+
'''Group16 Blackbox'''
Rhea Chandra<br>
+
* Chen Xueye
Piyush Pritam Sahoo<br>
+
* Zeng Jiadong
Malvania Smeet Saunil
+
* Zheng Wei
 
</td>
 
</td>
<td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
+
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
+
Associate Professor of Information Systems (Practice)</td>
 +
<td>[http://www.mrconsultants.sg Media Research Consultants Pte Ltd]</td>
 +
</tr>
 +
<tr>
 +
<td>[https://wiki.smu.edu.sg/ANLY482/ANLY482_Team_wiki%3A_2015T2_TeamROLL Social Media Content Analysis]</td>
 +
<td> This project aims to uncover valuable insights on SGAG’s content attributes in order to achieve audience growth. Using data gathered from SGAG’s facebook page for the year 2015, the team hopes to firstly, conduct exploratory data analysis so as to identify overall performance trends. Next, the team will be performing cluster analysis followed by sentiment analysis, topic analysis and content analysis. Lastly, the team will be building a regression model, which includes findings derived from the analysis conducted, in order to predict better performing future posts. With the insights gained, the team will be providing recommendations to enable data driven content creation, thus allowing SGAG to achieve their aim of greater growth.</td>
 +
<td>
 +
'''[https://wiki.smu.edu.sg/ANLY482/ANLY482_Team_wiki%3A_2015T2_TeamROLL_About_Us Group17 T(eam)ROLL]'''
 +
* Nur Amirah Bte Mohd Noor
 +
* Gan Sze Huey
 
</td>
 
</td>
<td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
+
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
+
Associate Professor of Information Systems (Practice)</td>
 +
<td>
 +
Karl Mak, Co-founder at SGAG
 
</td>
 
</td>
 
</tr>
 
</tr>
 
<tr>
 
<tr>
<td>[[Recommendations Matter to Us !!]] - Group 3</td>
+
<td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Group18 Optimizing Decision Making in a Connected World]</td>
<td> Recommendations and opinions from others are a part of our daily lives. From eating out in a restaurants to buying commodities online we yearn to know what others have to say about them. We want to see what others have experienced from a purchase partly because it involves spending our hard earned money but largely because of the plethora of information on available options out there. Seeking recommendations is an attempt towards staying away from bad experiences and maximizing once sense of satisfaction from a purchase.<br/>
+
<td>
This project shares people's opinion and their ratings on Yelp about businesses operating in various cites from U.K., Germany, Canada to Unites States. It will give you an opportunity to apply exploratory and predictive analytics techniques such as n-gram analysis, topic identification, sentiment analysis, ratings prediction, item-item similarities to design a flow that can help users across the world find the place of their choice faster with improved precision. Stress will be given on how you design a general purpose model using restaurant related data which can be applied to any other practice in hospitality domain such as hospitals, hotels equally.
 
  
'''Types of analysis''': The teams can analyse the data to unravel many aspects such as:<br/>
+
Taylor Nelson Sofres (TNS) is one of the largest research agencies worldwide. They provide actionable insights to help companies make impactful decisions that drive growth. Our team will be involved in the 2015 Connected Life study, which is TNS's global syndicated study to understand connected consumer better. <br>
1. What seasonal trends and patterns can be detected in data. Are there places being reviewed only on certain occasions or time periods around the year. Are their cities where people tend to eat out more then people from other cities. Try to predict what type of restaurants or bars will be reviewed more based on an upcoming event or festival.  
 
  
2. Clustering similar businesses, Item-Item to user-user similarities to develop and refine recommendations.
+
The aim of the project is to build an effective explanatory model that will help to reduce the number of variables needed for a market research study. By identifying pertinent variables and omitting variables that do not add value to the study results, we will be able to effectively reduce the number of survey questions in a study and reduce strain on survey respondents. This helps to increase accuracy of survey results and reduces the cost needed to incentivise respondents to complete long surveys, while meeting the aims of the marketers.
 
+
</td>
3. Sentiment analysis of English text, use of n-gram analysis to filter out prominent phrases and tips from the reviews, identification of topics of discussions. Identifying sarcasm and sarcastic reviewers. Identifying what are the main complainants, suggestions and wishes of reviewers.
+
<td>
 
+
'''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Group18 Group18 - Team VisCon]'''
4. Apply above mentioned analysis techniques to warn businesses when their overall image starts to go down, highlights new ideas for them to increase sales. By using a dashboard to display findings.
+
* Denise Quek Si Ying
<br />
+
* Tan Wei Song
 +
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>Subhashish Dasgupta,
 +
SEA Automotive and Regional Client Director at TNS
 +
</td>
 +
</tr>
 +
<tr>
 +
<td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Inpatient_Meals_Survey NUH Inpatient Meals Survey Analysis]</td>
 +
<td>National University Hospital (NUH) is one of the leading medical institutions in Singapore with around 50,000 inpatients and 600,000 outpatients. Sodexo is the esteemed provider of meals to the inpatients of NUH and is in charge of maintaining the standards of the meals for the patients. 
  
'''Suggested Platforms''': SAS Visual Analytics suite; SAS EM, python or R for text analytics; tableau or D3.js for developing visualizations<br />
+
Sodexo is in charge of conducting surveys with the inpatients to gather feedback on the meals that are provided to them in the hospital. The purpose of the survey is also to gain insights on what factors are most important for the patients and recommendations that can be helpful in improving the services provided to them. Sodexo appointed Media Research Consultants, a Mediacorp Enterprise specializing in market research, for conducting and analyzing the NUH In-Patient Meals Audit Survey from 2015-2017. This project is sponsored by MRC Mediacorp.  
  
'''Libraries to explore''': SAS EM and SAS VA sentiment analysis package, Natural language tool kit & libraries (both Python 2.7 and R), scikit-learn (Machine Learning in Python), D3.js, C3.js<br />
+
The objectives of the project are:
  
'''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.<br />
+
* Online dynamic dashboard with data visualizations
 +
* An analysis of the important attributes of the survey
 +
* An in-depth analysis of the satisfaction of different attributes by drilling down to wards, patient types and diet types
 +
* Correlation between different attributes
 +
* Summarize areas of improvements, customer feedback and suggestion for Sodexo
  
'''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.<br/>
 
More information on the dataset in this [[File:ReferenceDocumentYelp.docx]]
 
 
</td>
 
</td>
 
<td>
 
<td>
Ng Hui Ying <br/>
+
 
Aldred Lau Wen Yang <br/>
+
'''Group19 DataStat'''
Michelle Teo Sok Lee <br/>
+
* Pooja Tulsyan
Tan Yi Hao
+
* Akshat Agarwal
</td>
 
<td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
 
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
 
</td>
 
<td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
 
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
 
 
</td>
 
</td>
 +
<td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
 +
Associate Professor of Information Systems (Practice)</td>
 +
<td>'''Jason Soriano'''<br/>MRC Mediacorp</td>
 +
 +
<tr>
 +
 +
 
</tr>
 
</tr>
 
 
</table>
 
</table>

Latest revision as of 10:39, 9 September 2016

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About

Current Run

Projects Up For Grabs!

Grading & Deliverables

Downloads & FAQ

 


List of Projects
Title Analytics Practicum Description Student Member(s) Project Supervisor Sponsor
Optimizing Warehouse Processing The practicum’s project sponsor is a supply chain and logistics company. Currently, its warehouse data are not being utilized. By providing its warehouse data (inbound and outbound) transactions to the team, the sponsors would like the team to create a dashboard to help solve 3 main issues. Firstly, to help sponsor categorize warehouse SKUs into ABC categories (each category refers to how fast the goods move) to facilitate optimizing storage of SKUs in warehouse. Secondly, to provide employees with a high level visualization of the SKUs movement into, within, and out of the warehouse. Lastly, to analyze and suggest possible alternatives to improve the picking process within the warehouse (order picking VS batch picking).

Group01- Team Skulptors

  • LEONG Junkang, Gabriel
  • TAN Siying
  • ZHOU Xuanyi
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice)

A local multi-national corporation (MNC)

  • Mr Khoo, Head of Operation Innovation & Development (Corporate)
  • Miss Shen, Executive, Operations Innovation & Development (Corporate)
  • Mr Sundar, Process Improvement Executive (Automation)
Analysis of User and Merchant Dropoff for Sugar App Sugar is an interactive city guide that seeks to encourage a culture of exploration in Singapore and helping local small businesses get discovered. Sugar’s merchants are mainly small local businesses in Singapore. It has a large variety, including cafes, small restaurants, bars, hair salons, gyms, gift shops. The benefits for merchants is advertising to users that are in close proximity to them. Users in turn get discounts on products that are in the closest proximity to them.

As Sugar is a relatively young startup in Singapore, it has not yet attained a critical mass of user and merchant numbers. As such, user growth and user experience is vitally important. To reach this critical mass, Sugar needs to minimize user and merchant attrition, and retain vital segments of both groups. Furthermore, as a two-sided market, Sugar needs the network effect and also find out which group(Users or Merchants) adds more value to their bottomline.

The objective of our project will be to improve Sugar's bottomline via Merchant Analysis
User Analysis
Two-Sided Market Analysis
Geospatial Analysis

We will be using several techniques such as funnel plots, time series analysis, shortest distance analysis(Geospatial) and regression modeling to get insights and subsequently deriving recommendations for Sugar to increase its revenue and growth.

Group02- Team TurnKEY

  • Kang Long
  • Elizabeth Tan
  • Yi Sheng, Lim
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice)

Benjamin Lee

Founder and Chief Executive Officer of Sugar Technologies Pte Ltd.

Teppei Syokudo - Improving Store Performance Teppei Syokudo is a Japanese Food and Beverage chain, under the umbrella of the famous Teppei Japanese Restaurant. In order to drive store performance through controllable factors, Teppei Syokudo is looking at focusing on staff performance. Most F&B businesses, including Teppei Syokudo, do not set detailed KPIs to evaluate how their staff are performing. If Teppei Syokudo is able to track the performance of their staff through relevant KPIs, they will be able to motivate staff to meet these KPIs, which will in turn boost the business’ bottom line.Teppei Syokudo has identified the following KPIs to assess their staff:
  • Percentage of drinks sales (number of drinks sold / number of meals sold)
    • a measurement of how hard the staff are up-selling
  • Labour Productivity (sales $ / working hours)
    • a measurement of how effective the shop manager is in staffing the shop

However, the business is uncertain if these are the right KPIs to set. Also, if they are the right ones, they are unsure as to what would be a good target to meet. Another factor for driving store performance is through product portfolio mix. Even though the staff may be up-selling and cross-selling, they may not know the right products to cross-sell to increase the probability of the customer making additional purchases. For example, most customers may tend to purchase Drink X together with Don X. In this case, if a customer orders Don X and is about to make payment, the staff can suggest Drink X to the customer, hence prompting a higher probability for the customer to purchase Drink X.

Group03- Team APSM

  • TAN Jhun Boon
  • YAP Jessie
  • OH Peng Ho
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Itaru Nagao,

Managing Director

YCP Management Southeast Asia Pte. Ltd.

YCP Retailing SEA Pte. Ltd.

Car Park Overspill Study The objective of this project is to assist Media Research Consultants Pte Ltd (MRC) in understanding the current parking situations in 65 different locations in Singapore. These 65 parking locations compromise of 30 retail malls, 15 retails and Food & Beverage (F&B) clusters in landed housing estates, 10 hawker centers, and 10 community clubs.

Group04- Team ATOM

  • Macus KHOO JunHao
  • YAN ShaoHong Chris
  • YO Wee King
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Media Research Consultants Pte Ltd
Social Media Analytics

The aim of this project is to provide deeper insight into SGAG's social network across its multiple platforms, namely Instagram and Twitter.

Our client is the co-founder of the company and he seeks insights that can spur growth in SGAG's follower numbers. Through our analysis and research, we aim to help discover what kind of users are on each platform; the key engagement leaders for each topic; and how wide is the reach of these individuals.

The final deliverables will aim to:

  1. Visualise the social networks of SGAG
  2. Identifying the user segments who engage SGAG's content, as filtered by topics and their reach

Group05- Team AP

  • WANG Shyan Ann
  • NG Tse Siong
  • Sherman YONG Chin Wei
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Karl Mak, Co-founder at SGAG
Skyscanner Content Analysis

The project aims to help Skyscanner analyse its content sites in order to facilitate more effective planning of news articles. It will help understand the factors that affect content performance.

The team will analyse content related data from multiple sources to determine what differentiates good and bad content based on certain performance metrics set by Skyscanner. This will be done through Text Based Cluster Analysis, Exploratory Modelling with logistic regression and Data Visualization using Tableau

The deliverables include creating a dashboard with visualizations that will help Skyscanner team to better understand performance of content across different content channels. It will be used to validate some of the intuitions they might have about certain content topics/types and to determine the best time to publish them. The dashboard will benchmark certain metrics against pageviews as well as additional attributes that Skyscanner does not currently analyse via Google Analytics, such as the impact of title, text length, theme of article and number of images.

Data set includes data from Skyscanner websites for the Singapore, Malaysia and Thailand markets.

Group06- Team SkyTrek

  • Aseem PRABHAT
  • Jedaiah TAN Jia Le
  • NGUYEN Viet Huy
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice)

Ms. Antoinette Tan
Content Manager, APAC
Skyscanner

Understanding Employee Social Networks

People analytics is a rapidly growing area of business intelligence and big data technology. It uses various facets of people-related data to optimize business outcomes and solve business problems. The application of people analytics with new techniques such as predictive behavioural analytics has helped organisations to save millions of dollars while improving attrition rates, employee engagement and identify underlying training requirements.

TrustSphere is the widely recognized market leader in Relationship Analytics. TrustSphere enables forward thinking organizations to unlock the inherent value of their own networks using next generation technology. The solutions provide real-time intelligence and insights which help clients across the globe improve salesforce effectiveness, enterprise-wide collaboration and corporate governance.The motivation behind this project is to assist TrustSphere in verifying the effectiveness of their product through other statistical techniques.

Access to TrustSphere’s datasets will allow the team to build a system from scratch using previously unused raw data to better understand turnover and attrition rules.

The minimum research points we would like to address:

  • Understand the number of relationships an employee will have at different periods of time in his or her working life
  • Measure the speed of growth at which employee relationships grow in a company
  • Correlations between the sizes of internal and external relationships employees have
  • Through social network analysis, calculate the likelihood of an employee in an informal group leaving a company upon the exit of another closely tied employee
  • Identification of metrics that can help predict the likelihood of an employee leaving

Group07- Team YSR

  • Ridwan Ismeer
  • Sadhvi Ilango
  • Yashraj Jalota
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Greg Newman, Product Manager - TrustSphere
Health Analytics To build a web application that utilises GIS functions for geospatial planning and analysis. This application aims to facilitate the computation and analysis of Health Promotion Board (HPB) KPI reporting metrics. The insights generated from the application will be used to manage Health Promotion Programme and Outreach Planning.

Group08 - Team HealthTics

  • Erwin
  • Trinh Hiep Dang Khoa
  • Nyein Su Aye
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Health Promotion Board
CodeBetter Analytics: Identifying Patterns in Learning Students using a web portal to learn programming languages and frameworks will answer a series of questions, which have their timings and results recorded. An analytics dashboard will be built to read from a live Firebase, thus dynamically generating charts and information for students to view their progress. Course conductors may also use this dashboard to better understand the needs of their students and modify their course accordingly.

Group09 - Team KyuuBI

  • Shane GOH Ghee Gin
  • Darren LIM Fei Hong
  • LIM Wei Yang
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Prof. Chris Boesch Associate Professor of Information Systems (Education)
Commuting Patterns in Singapore

The main aim of this project is to understand how the urban form affects one’s decision to either use public transport or to walk by focusing on Tampines as our case study


The objectives of our project are:
Analyse commuting patterns for the 3 different age groups (i.e Adult, Elderly and Student)
Overall commuting patterns for the 3 different age groups
Inter town commuting patterns for the 3 different age groups
Intra town commuting patterns for the 3 different age groups
Intra town commuting patterns for the 3 different age groups who made at least 4 trips in one week during morning peak period

Analyse multimodal transportation patterns for 3 different age groups
Transfer time interval for buses and MRT for the 3 different age groups
• Relationship between walking and bus transportation using Student group as a proxy

Group10 - CommuteThere

  • Jeanne Sim Peh Wuen
  • Lim Hui Ting
  • Jaclyn Lim Hui Ting
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Prof. Kam Tin Seong Associate Professor of Information Systems (Practice)
Uncovering Market-Insights for Charles & Keith This project aims to help Charles & Keith gain a better understanding of its China market through the use of data-analytics. With the market insights gathered, provide recommendations for the business on how they may apply them to its business to positively affect consumer buying decisions and capture more market share within China.

Key objectives of this project involves:

  • Visualise the overall generic consumer purchasing patterns and behaviour within China
  • Identify best-selling products and item set for different regions or tiers of cities
  • Highlight the differences between transactions of different locations in terms of product specifications or types

Some business areas where the project findings could be applied:

  • Strategic placement of products within product catalogues
  • In-store shelving decision
  • Strategic marketing promotions to cross-sell and upsell, at a localized market level
  • Suggest Market Basket Analysis algorithm solutions for C&K’s e-commerce site
Data set includes sales transaction data from all of Charles & Keith’s stores in China.

Group11-Team AYE

  • Audrey Jee Zhi Ying
  • Edwin Tan Soon Hong
  • Toh Yan Ying
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Charles & Keith Group
Health Promotion Board GIS Application Our objective is to build an interactive and visual web application that utilises GIS functions for geospatial planning and analysis. It will be able to compute and analyse HPB KPI reporting metrics. The web application should be easy to use to support the staff both technical and non-technical in their many Health Promotion programmes and outreach planning. It will also assist the staff in clear and easy to understand presentations to the executives who have little or no background in GIS analysis so as to be able to plan their operations.

Group12 - Griffins

  • Melissa Lim Seok Yu
  • Tan Wei Liang
  • Tan Yu Ling
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Health Promotion Board
Natasha Studio Natasha Studio is a dance studio in Singapore that offers various genres of street dance lessons to interested individuals, including Bboying, Hiphop and Kpop. Currently, the company has no formal sales management system and is looking to apply analytics to create more customer satisfaction and increase the competitiveness of the business. Based on discussions with both our client and project sponsor, our business objective is to provide Natasha Studio with recommendations for class types, packages and dance genres that it should offers.

To achieve this business objective, the technical goal would be to first create a proper database system to aid in data recording and ensure consistency of data. This would then lay the foundation for our team to apply techniques like market basket analysis to identify customer purchasing behaviours and propose appropriate business actions for Natasha Studio.

Thus, our final deliverables would be as follows:

1. Relative Database Management System with a User Interface

2. Identification of Customer’s purchasing behaviours and thus offer appropriate business recommendations

Group13 Team Ameilax

  • Sng Ei Leen
  • Wong Jia Wei
  • Tan Ziling Amy
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Larry Liu

Business Manager

Natasha Studio
Exploratory Analysis of Indonesian Motor Insurance To conduct an exploratory analysis on Tokio Marine Indonesia's Motor Insurance segment. Factors that will be explored include (a) Policy growth, (b) Underwriting Profitability and (c) Claim Insights Group14- Team HEW
  • Huai Zher
  • Elijah Tan Yi Rong
  • Winston Ong Bing Shen
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Benito Mable

Vice President, New Opportunities

Tokio Marine Asia
Singapore Students Performance Analysis

This project makes use of PISA (Programme for International Student Assessment) data collected during the latest survey of 2012 with regards to Singapore. The aim of this project is to explore the relationship between computer usage in school and secondary-school student performance in reading and mathematics. Building on the current international work done by PISA, our project will bring the analysis to Singapore national level and study various aspects of students of performance relative to their access to computer in and outside of school, in order to provide insights for education policy makers of Singapore Ministry of Education (MOE).

Group15

  • Nguyen Le Hong Ngoc
  • Poh Jin Hui
  • Zhao Yazhi
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Prof. Kam Tin Seong Associate Professor of Information Systems (Practice)
Car Park Overspill Study Land Transport Authority(LTA) aims to do an analysis to understand the current parking situation at these selected locations. The LTA Contract Parking Study was awarded to Media Research Consultants in March 2015 to undertake the Study involving 65 car park locations in Singapore, including 30 retail malls, 15 retail and F&B clusters in landed housing estates, 10 hawker centres, and 10 community clubs.

This study was to conduct parking occupancy surveys, human traffic counts, and interview surveys at the selected locations at stipulated times. The study incorporated the conventional method of manual counting as well as deployment of automated counting equipment. Face-to-face interviews were employed for the interview survey segment.

In addition to the study and taking of detailed data from field surveys, this project aims to develop a simulation tool that enables the systematic analysis of the impacts of various parameters, using a collected set of quantifiable data.

Group16 Blackbox

  • Chen Xueye
  • Zeng Jiadong
  • Zheng Wei
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Media Research Consultants Pte Ltd
Social Media Content Analysis This project aims to uncover valuable insights on SGAG’s content attributes in order to achieve audience growth. Using data gathered from SGAG’s facebook page for the year 2015, the team hopes to firstly, conduct exploratory data analysis so as to identify overall performance trends. Next, the team will be performing cluster analysis followed by sentiment analysis, topic analysis and content analysis. Lastly, the team will be building a regression model, which includes findings derived from the analysis conducted, in order to predict better performing future posts. With the insights gained, the team will be providing recommendations to enable data driven content creation, thus allowing SGAG to achieve their aim of greater growth.

Group17 T(eam)ROLL

  • Nur Amirah Bte Mohd Noor
  • Gan Sze Huey
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice)

Karl Mak, Co-founder at SGAG

Optimizing Decision Making in a Connected World

Taylor Nelson Sofres (TNS) is one of the largest research agencies worldwide. They provide actionable insights to help companies make impactful decisions that drive growth. Our team will be involved in the 2015 Connected Life study, which is TNS's global syndicated study to understand connected consumer better.

The aim of the project is to build an effective explanatory model that will help to reduce the number of variables needed for a market research study. By identifying pertinent variables and omitting variables that do not add value to the study results, we will be able to effectively reduce the number of survey questions in a study and reduce strain on survey respondents. This helps to increase accuracy of survey results and reduces the cost needed to incentivise respondents to complete long surveys, while meeting the aims of the marketers.

Group18 - Team VisCon

  • Denise Quek Si Ying
  • Tan Wei Song
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Subhashish Dasgupta,

SEA Automotive and Regional Client Director at TNS

NUH Inpatient Meals Survey Analysis National University Hospital (NUH) is one of the leading medical institutions in Singapore with around 50,000 inpatients and 600,000 outpatients. Sodexo is the esteemed provider of meals to the inpatients of NUH and is in charge of maintaining the standards of the meals for the patients.

Sodexo is in charge of conducting surveys with the inpatients to gather feedback on the meals that are provided to them in the hospital. The purpose of the survey is also to gain insights on what factors are most important for the patients and recommendations that can be helpful in improving the services provided to them. Sodexo appointed Media Research Consultants, a Mediacorp Enterprise specializing in market research, for conducting and analyzing the NUH In-Patient Meals Audit Survey from 2015-2017. This project is sponsored by MRC Mediacorp.

The objectives of the project are:

  • Online dynamic dashboard with data visualizations
  • An analysis of the important attributes of the survey
  • An in-depth analysis of the satisfaction of different attributes by drilling down to wards, patient types and diet types
  • Correlation between different attributes
  • Summarize areas of improvements, customer feedback and suggestion for Sodexo

Group19 DataStat

  • Pooja Tulsyan
  • Akshat Agarwal
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Jason Soriano
MRC Mediacorp