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| − | <td>[[Terrorism : Unraveling Hidden Patterns]]</td> | + | <td>Optimizing Manufacturing Costs Between Different Manufacturing Locations</td> |
| | + | <td>Awaiting Project Summary</td> |
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| − | 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.
| + | <b>Group 1 - Group Name</b> |
| − | | + | * Cheryl Yong Li Ru |
| − | | + | * Lam Youkang |
| − | '''Types of analysis''': <br/>
| + | * Tan Yong Ying |
| − | 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/>
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| − | Here are some ideas that express this theme: <br/>
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| − | 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.
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| − | 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/>
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| − | 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/>
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| − | We will soon share a document with more ideas to help you.
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| − | '''Suggested Platforms''': SAS Visual Analytics suite; tableau or D3.js for developing custom visualizations<br />
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| − | '''Libraries to explore''': SAS VA, D3.js, C3.js, Gephi and NodeXL<br />
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| − | '''Other recommendations''': No prior knowledge of any courses is assumed while designing this project but prior knowledge of network Analysis, Visual Analytics will be good for taking up advanced analysis during project related tasks.<br />
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| − | '''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/>
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| − | More information on the dataset will be shared soon.
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| − | </td>
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| − | <td>
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
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| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
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| − | </td>
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| − | <td>[http://www.smu.edu.sg/faculty/profile/108496/Michael-GENKIN Prof. Michael GENKIN]
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| − | Assistant Professor of Sociology, SOSS
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| − | </td>
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| − | </tr>
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| − | <tr>
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| − | <td>[[Improved Decisions for Ocean Freights]]</td>
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| − | <td>
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| − | 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/>
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| − | 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/>
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| − | More information about general things that GTL does can be found at GTL official [http://gtl.smu.edu.sg website]
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| − | '''Types of analysis''':
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| − | The aim of this analytics project is to perform data analytics and build a dashboard using a visualization tool such as Tableau.
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| − | The features on the visualization tool includes (but not limited to):<br/>
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| − | a. Ocean freight profiling for the selected customer<br/>
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| − | 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
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| − | '''Suggested Platforms''': Tableau or D3.js for developing custom visualizations, SAS Visual Analytics suite;<br />
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| − | '''Libraries to explore''': D3.js, C3.js, SAS VA, Gephi<br />
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| − | '''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 />
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| − | | |
| − | '''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/>
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| − | More information on the dataset will be shared soon.
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| − | </td>
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| − | <td>
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
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| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/104156/TAN-Kar-Way| Prof. TAN Kar Way]
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| − | Assistant Professor of Information Systems (Practice)
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| | </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>FMCG Company</td> |
| | </tr> | | </tr> |
| − | <tr>
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| − | <td>[[Recommendations Matter to Us !!]]</td>
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| − | <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.
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| − | This project which shares people's opinion of various businesses and their ratings on [http://www.yelp.com/singapore Yelp]. It will give you an opportunity to apply exploratory and predictive analytics techniques such as sentiment analysis, ratings prediction, and item-item similarities to design a flow that can help online 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 this data which can be applied to any practice in hospitality domain such as hospitals, hotels equally.
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| − | '''Types of analysis''': Clustering similar businesses, Item-Item to user-user similarities to develop and refine recommendations,sentiment analysis of english text, analyzing text for prominent phrases and topics of discussions, dashboard creation to display the solution.
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| − | '''Suggested Platforms''': SAS Visual Analytics suite; SAS EM, python or R for text analytics; tableau or D3.js for developing visualizations<br />
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| − | '''Libraries to explore''': SAS EM and SAS VA sentiment analysis package, Natural language tool kit & libraries (both Python 2.7 and R), D3.js, C3.js<br />
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| − | '''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 />
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| − | '''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/>
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| − | More information on the dataset in this [[File:ReferenceDocumentYelp.docx]]
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| − | </td>
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| − | <td>
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
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| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
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| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
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| − | </td>
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| − | </tr>
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| | </table> | | </table> |
