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| − | ='''Introduction to Analytics Practicum''' =
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| − | [[Image:logo.png]]
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| | </div> | | </div> |
| | + | <!--MAIN HEADER --> |
| | + | {|style="background-color:#1B338F;" width="100%" cellspacing="0" cellpadding="0" valign="top" border="0" | |
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| | + | [[Main_Page| <font color="#FFFFFF">About</font>]] |
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| − | The [[Analytics]] Practicum module (ANLY482) is a compulsory module for those who are taking the [[Analytics]] [http://sis.smu.edu.sg/2nd-majors-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.
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| − | | + | ; |
| − | '''Welcome to the [[Analytics]] Practicum (ANLY482)! -
| + | [[ANLY482_AY2017-18_Term_2| <font color="#FFFFFF">Current Practicums</font>]] |
| − | | |
| − | =Practicum Projects for AY2014/2015 Term 2=
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| − | <table class="wikitable centered" width="100%" color="blue">
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| − | <tr>
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| − | <th width=10%>Team</th>
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| − | <th>Project Name</th>
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| − | <th width=150>Student Member(s)</th>
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| − | <th width=150>Project Supervisor</th>
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| − | <th width=150>Sponsor</th>
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| − | </tr>
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| − | <tr>
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| − | <td>[[Social Media & Public Opinion]]</td>
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| − | <td> 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 [http://twitter.com/| 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.
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| − | '''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|200px|thumb|left|alt text]]
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| − | <br />
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| − | | |
| − | '''Suggested Platforms''': SAS EM, Python, R, Gephi, NodeXL (Microsoft)<br />
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| − | | |
| − | '''Libraries to explore''': SAS EM sentiment analysis package, NLTK tools & 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.
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| − | </td>
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| − | <td>
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| − | Kean KWOK Jin<br>
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| − | Miguel Nicholas<br>
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| − | Sherman TAN
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]<p>
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| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics</p>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p></td>
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| − | <td>[http://centres.smu.edu.sg/larc/research-staff-fully-supported-by-larc-palakorn-achananuparp/| Palakorn Achananuparp]
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| − | Research Scientist at LARC
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| − | </td>
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| − | </tr>
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| − | <tr>
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| − | <td>[[Emergency Department and Queuing Theory]]</td>
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| − | <td> 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.
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| − | The current project applies the knowledge of queuing theory and hospital related domain knowledge in dynamically managing queues in emergency department. <br />
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| − | '''Suggested Platforms''': SAS EM, Python, R<br />
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| − | '''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. <br />
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| − | [http://scholar.google.com.sg/citations?view_op=view_citation&hl=en&user=Ytl-OxEAAAAJ&citation_for_view=Ytl-OxEAAAAJ:IjCSPb-OGe4C| Improving Patient Length-of-Stay in Emergency Department Through Dynamic Queue Management]
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| − | [https://wiki.smu.edu.sg/is480/IS480_Team_wiki%3A_2014T1_Code_Blue| Code Blue]
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| − | [http://dl.acm.org/citation.cfm?id=2694015&dl=ACM&coll=DL&CFID=609186238&CFTOKEN=75235724| Analisys of hospital bed capacity via queuing theory and simulation]
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| − | [http://www.sysengi.com/EN/abstract/abstract110225.shtml| Research on genetic optimization algorithm of queue rules based on simulation model]
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| − | [http://isl.ucf.edu/publication/papers/hbahr/01_HBahr_SPQ_Alg_0902.pdf| Smart Priority Queue Algorithms for Self-Optimizing Event Storage]
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| − | <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.
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| − | </td>
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| − | <td>
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| − | Jinq-Yi<br/>
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| − | Marcus TAN<br>
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| − | Muhammad Faris
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]<p>
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| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics</p>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p></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>
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| − | </tr>
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| − | <tr>
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| − | <td>[[Sustainability Jobs: Analyse the trends]]</td>
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| − | <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 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:<br />
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| − | a. sustainability-related jobs and <br />
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| − | b. sustainability-related topics on Twitter. <br />
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| − | This project aims at analyzing the crawled data to get deeper actionable insights and answer questions such as:<br />
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| − | a. Is there a trend in the type of sustainability jobs in the various regions, e.g., Europe, Asia, America?<br />
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| − | b. Why does sustainable jobs matter? Is it more attractive to potential employees? Or is it policy driven in some countries?<br />
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| − | 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?<br />
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| − | '''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|Reference Document]]<br />
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| − | '''Suggested Platforms''': Python, R, Gephi, NodeXL (Microsoft), Tableau<br />
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| − | | |
| − | '''Libraries to explore''': SAS EM sentiment analysis package, NLTK tools & libraries (both Python 2.7 and R), D3.js, C3.js<br />
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| − | '''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.<br />
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| − | 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.
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| − | In order to get a better understanding of subject matter you may first read through the links provided below and materials. <br />
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| − | [http://heatmap.greentransformationlab.com/| GT Lab]
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| − | [http://gtl.smu.edu.sg/| gLab SMU]
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| − | <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.
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| − | </td>
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| − | <td>
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| − | TAN Siong Min<br/>
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| − | Janice KOH <br/>
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| − | TAY Hui Shia
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]<p>
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| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics</p>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p></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>
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| − | </tr>
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| − | | |
| − | <tr>
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| − | <td>[[Arisaig_Home | Visualisation of Economies & Consumption Patterns]]</td>
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| − | <td>
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| − | Arisaig Partners (Asia) Pte Ltd is an independent investment management company established since 1996. Yearly, Arisaig Partners holds a Consumer Symposium for potential clients around the globe, and wishes to increase the effectiveness of their presentation by building an interactive dashboard.
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| − | The project covers two main research areas:
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| − | Macro: Demographics & Economic indicators
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| − | Micro: Individual sector matrices
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| − | | |
| − | Considering the research areas and sample data, the project is to be as followed:<br />
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| − | '''Macro Visualisation #1''': Exploration of Demographic Data Relationship<br />
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| − | a. Birth rate per women <br />
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| − | b. Average age of country/region <br />
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| − | c. Income <br />
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| − | d. Household size & Household Income<br />
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| − | '''Macro Visualisation #2:''' Exploration of Economic Data<br />
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| − | a. GDP, GDP growth rate<br />
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| − | b. Debt to GDP ratios, Savings rate<br />
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| − | c. Capital Market Size<br />
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| − | '''Micro Visualisations:''' Exploration of Consumption per Capita<br />
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| − | | |
| − | '''Platforms Considered''': d3<br />
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| − | </td> | |
| − | <td>
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| − | Tan Kei Rong Benjamin<br/>
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| − | Sean Chua Kian Shun <br/>
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| − | Zoey Teo Kai Ying
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| − | </td>
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| − | <td>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p></td>
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| − | <td>Gordon Yeo, Investment Analyst<br>
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| − | Arisaig Partners (Asia) Pte Ltd
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| − | </td>
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| − | </tr>
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| − | | |
| − | <tr>
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| − | <td>[[Network Analysis of Interlocking Directorates]]</td>
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| − | <td>
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| − | Our project aims to review and analyse the [http://en.wikipedia.org/wiki/Interlocking_directorate| interlocking directorates] among corporate organizations in Singapore. While interlocking directorates is lawful and has its advantages, it may give rise to conflicts of interests and unfair advantages especially if the relationship is between public and private organizations.
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| − | <br/>
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| − | <br/>
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| − | Using corporates and directorships database from OneSource, we will analyze the network of interlocking directorates amongst Singapore firms. Through our analysis, readers will be able to identify possible areas where conflicts of interest may arise between public and private organizations. They will also be able to observe patterns of interdependency between sectors in Singapore which might assist them in making better business decisions.
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| − | <br/>
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| − | <br/>
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| − | '''Key features''' of our project includes:
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| − | #Visualization of the network of interlocking directorates
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| − | #Analysis of this network, focusing on the relationships from public to private sectors.
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| − | #Gathering useful insights from the analysis, which may be helpful the corporate leaders in decision-making in the future.
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| − | '''Technologies considered:''' SAS EM, R, Gephi
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| − | </td>
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| − | <td>
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| − | Le Hoang Trinh<br/>
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| − | Zheng Tianwei<br/>
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| − | </td>
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| − | <td>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p></td>
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| − | <td>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p>
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| − | </td>
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| − | </tr>
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| − | | |
| − | <tr>
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| − | <td>[[GeoVisual Analytics Tool for population health analysis]]</td>
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| − | <td>
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| − | Health Promotion Board(HPB) is established to promote national health status in Singapore. It looks after the entire population, making sure that health care is available to Singapore citizens when they are in need. One objective of HPB’s is to make health care facilities accessible to everyone and this project aims to define the accessibility of these facilities to Singapore population.
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| − | <br/>
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| − | <br/>
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| − | We aim to demonstrate the distribution of health care agencies of Health Promotion Boards and determine the accessibility of each agency. We will visualize the distribution of facilities using Geographical Information Systems(GIS) and then further zoom in to look at individual region and facility.
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| − | <br/>
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| − | <br/>
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| − | '''Key features''' of our project includes:
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| − | #Facility and Population distribution visualisation
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| − | #Dashboard to filter regions and facilities
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| − | <br/>
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| − | '''Technologies considered:''' QGIS, PostGIS, R
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| − | </td>
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| − | <td>
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| − | Song Chengyue<br/>
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| − | Wang Jing<br/>
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| − | </td>
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| − | <td>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p></td>
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| − | <td>
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| − | Health Promotion Board
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| − | <br/>
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| − | <br/>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p>
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| − | </td>
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| − | </tr>
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| − | | |
| − | <tr>
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| − | <td>[[ALOS]]</td>
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| − | <td>
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| − | Length of stay (LOS) in hospital for inpatient treatment is a measure of crucial recovery time and is often used as a measure of hospital performance and a proxy of hospital resource consumption.
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| − | <br/>
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| − | | |
| − | Our project is basically to improve the prediction of the LOS based on more variables thus helping clinicians to improve the rate of prediction.
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| − | <br/>
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| − | </td>
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| − | <td>
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| − | Koh Ying Ying Trecia<br/>
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| − | Luqman Haqim Bin Ab Rahman <br/>
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| − | </td>
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| − | <td>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p></td>
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| − | <td>
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| − | Jurong Health Services
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| − | | |
| − | <br/>
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| − | <br/>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p>
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| − | </td>
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| − | </tr>
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| − | | |
| − | <tr>
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| − | <td>[[Come back after 30 days!]]</td>
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| − | <td>
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| − | '''At the moment of discharging from hospital,'''<br />
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| − | <i>Nurse</i>: "Thank you for choosing our hospital, we hope that you have a speedy recovery!" <br />
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| − | <i>Patient</i>: "Thank you, i feel much more at ease if i stay under the care of your hospital staff. If i feel any slightest discomfort, i will come back immediately ok!" <br / >
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| − | <i>Nurse</i>: *with an horrified face* "Oh.. uh.. actually, if its really something minor, you do not need to come back. But well, we can't stop any patients from visiting us... repeatedly" <br />
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| − | <i>Patient</i>: "Yeah, so that's it. I will come back whenever i feel not at ease, even though it may be eventually minor. You know, just gotta be safe."
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| − | <br/>
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| | | | |
| − | Hospitals have been studying about the likelihood of patients readmitting within 30 days starting on the day of discharge primarily to reduce costs and operational overhead. It has grown to be a governmental concern as health systems, especially in UK, has decided to incentivize hospitals that abide by the rules and penalize those who did not manage to reduce their number of 30 days readmissions. Furthermore, for governments with welfare systems that provide for health care, patients readmitted within 30 days may be an avoidable expense if the hospitals were able to identify such patients during the first diagnosis. At the same time, various researchers claimed their superiority over other studies. Even though LACE (Length of stay, Acuity of admission, Comorbidity, Emergency department visits) index has been acknowledged as the gold standard in prediction of 30 day readmissions rates, authors claimed that their model perform better, often with caveats.
| + | | style="font-family:Century Gothic; font-size:100%; solid #1B338F; border-bottom:7px solid #1B338F; background:#000000; text-align:center;" width="20%" | |
| − |
| + | ; |
| − | The outcomes of such a study allow hospitals to identify patients with a higher risk of readmissions and prescribe interventions in the form of house visits, or a wide-spectrum treatment in order to mitigate the problem. Data analytics, especially predictive ones, enable hospitals to do so. If effectively implemented, hospitals can reduce their costs and focus their limited resources to prevent avoidable readmissions. Consequently, hospitals may be able to stretch their resources to care for a larger number of patients, instead of serving readmitted patients..
| + | [[ANLY482_Past_Practicums: Past Practicums| <font color="#FFFFFF">Past Practicums</font>]] |
| | | | |
| − | '''Objective''': To derive a model predicting the likelihood of a patient readmitting within 30 days using 2 approaches (i.e. random forest decision tree & multivariable logistic regression), along with its corresponding ROC curves to determine the predictive power of our models.
| + | | style="font-family:Century Gothic; font-size:100%; solid #1B338F; border-bottom:7px solid #1B338F; background:#000000; text-align:center;" width="20%" | |
| | + | ; |
| | + | [[ANLY482_Grading: Current Practicums | <font color="#FFFFFF">Grading & Deliverables</font>]] |
| | | | |
| − | '''Suggested Platforms''': SAS EM, JMP, Tableau<br />
| + | | style="font-family:Century Gothic; font-size:100%; solid #1B338F; border-bottom:7px solid #1B338F; background:#000000; text-align:center;" width="20%" | |
| | + | ; |
| | + | [[ANLY482_FAQ: FAQ | <font color="#FFFFFF">Downloads & FAQ</font>]] |
| | + | | |
| | + | |} |
| | | | |
| − | <br/> | + | '''<font size = 5>Welcome to ANLY482 Analytics Practicum</font> |
| − | </td>
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| − | <td>
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| − | Nicholas Lee Desheng<br/>
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| − | Goh Jian Hao<br/>
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| − | </td>
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| − | <td>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p></td>
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| − | <td>
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| − | <br/>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p>
| |
| − | </td>
| |
| − | </tr>
| |
| | | | |
| − | <tr> | + | <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>Overview</font></div> |
| − | <td>[[GLC]]</td> | |
| − | <td> | |
| − | GLC is an international postal and logistic company with a global network in over 220 countries and territories across the globe. The company offers a wide range of services such as international express deliveries; global freight forwarding by air, sea, road and rail; warehousing solutions from packaging, to repairs, to storage; mail deliveries worldwide; and other customised logistic services. In recent years, the company’s sale volume and revenue in the Asia-Pacific has grown rapidly. However, the company is also experiencing increasing competition from its competitors. In order to stay competitive in the Asia-Pacific market, besides producing cutting edge products, the company believes that it is also very important to understand consumers’ needs.
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| | | | |
| − | <b>Objective and task:</b> GLC wishes to maximise its sales revenue and market share in 2015 by formulating appropriate product strategies and distribution channel policies based on the analysis of historical sales data. Actionable recommendations may be useful in helping the management to meet the business objectives and to shape this aspect of its business strategy and operations.
| + | In recent years, there is an increasing use of data analytics to discover business issues and to drive business strategy throughout organizations. This has created a parallel rising demand for business graduates, who understand how to use data analytics, to solve business issues. To prepare students taking Analytics Second Major to cope with this demand, this course provides students the practical experience on how to apply the analytics techniques and tools that they have learned in class to help companies solve real world challenges. |
| − | </td>
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| − | <td>
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| − | CHENG Fu Mei<br/>
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| − | LEONG Wai Sum<br/>
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| − | Lynette SEOW Hui Xin<br/>
| |
| − | </td>
| |
| − | <td>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p></td>
| |
| − | <td>
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| − | GLC (anonymous)
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| − | <br/>
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| − | [http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. KAM Tin Seong]<p>
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| − | Associate Professor of Information Systems</p><p>
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| − | Senior Advisor, SIS Programmes in Analytics</p>
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| − | </td>
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| − | </tr>
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| − | </table>
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| | | | |
| − | =Practicum Projects for AY2014/2015 Term 1=
| + | '''IMPORTANT:''' ANLY482 Analytics Practicum is a compulsory module for students who are taking the [http://sis.smu.edu.sg/2nd-majors-analytics Analytics Second Major program]. |
| | | | |
| − | <table class="wikitable centered" width="100%" color="blue"> | + | <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> Prerequisites</font></div> |
| − | <tr>
| |
| − | <th width=10%>Team</th>
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| − | <th>Project Name</th>
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| − | <th width=150>Student Member(s)</th>
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| − | <th width=150>Project Supervisor</th>
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| − | <th width=150>Sponsor</th>
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| − | </tr>
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| − | <tr>
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| − | <td>[[Kolaveri Di Social Analytics Project]]</td>
| |
| − | <td>"[http://www.youtube.com/watch?v=YR12Z8f1Dh8|Why This Kolaveri Di]" is a Tamil song from the soundtrack of Tamil film 3. It was written and sung by actor [http://en.wikipedia.org/wiki/Dhanush Dhanush] and composed by music director [http://en.wikipedia.org/wiki/Anirudh_Ravichander Anirudh Ravichander]. The song was officially released on 16 November 2011, and it instantly became viral on social networking sites for its quirky "[http://en.wikipedia.org/wiki/Tanglish 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 [http://www.sify.com/movies/kolaveri-bags-youtube-gold-award-news-news-lmhmfCidgif.html?scategory=tamil 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.
| |
| − | </td>
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| − | <td>
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| − | #[[Lee Jaehyun]]
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| − | #[[Chan Wei Yin]]</td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]<p>
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| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics</p></td>
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| − | <td>[http://www.smu.edu.sg/faculty/profile/9528/Srinivas%20K%20REDDY Prof. Srinivas K Reddy]<p>
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| − | Professor of Marketing, Director, Centre for Marketing Excellence, Academic Director, LVMH-SMU Asia Luxury Brand Research Initiative, Area</p></td>
| |
| − | </tr>
| |
| − | <tr>
| |
| − | <td>[[Visualization of Consumer Satisfaction]]</td>
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| − | <td>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 [http://ises.smu.edu.sg/sites/default/files/ises/pdf/csisg2013_q1_executivesummary.pdf|Customer Satisfaction Index of Singapore (2008-2013)]. The objective of this project is to produce a dashboard that shows trends of consumer satisfaction visually.</td>
| |
| − | <td>
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| − | #[[Mohamed Yousof Bin Shamsul Hameed]]
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| − | #[[Kee Eng Sen]]</td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]<p>
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| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics</p></td>
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| − | <td>[http://www.smu.edu.sg/faculty/profile/9505/Marcus%20LEE Prof. Marcus Lee]<p>
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| − | Assistant Professor of Marketing (Practice), Academic Director for the Institute of Service Excellence at SMU (ISES)</p></td>
| |
| − | </tr>
| |
| − | <tr>
| |
| − | <td>[[Twitter Analytics:Home|Twitter Analytics]]</td>
| |
| − | <td>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</td>
| |
| − | <td>
| |
| − | #[[Fransisca Fortunata]]</td> | |
| − | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]<p>
| |
| − | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics</p></td>
| |
| − | <td>[http://sis.smu.edu.sg/master-it-business/faculty-and-staff/adjunct-faculty/hardoon David Hardoon]<p>
| |
| − | Head, Analytics, SAS Institute Pte Ltd, Singapore</p></td>
| |
| − | </tr> | |
| − | </table> | |
| | | | |
| − | = Grading =
| + | <b>Analytics Foundation</b> is a Pre-Requisite. It is recommended to take the Practicum course after completing 2 to 3 other analytics electives in order to make the most of the Practicum course. Each listed Analytics Practicum project, might have a list of additional recommended courses which are closely associated with the project area and will come in handy if you take up that project. |
| | | | |
| − | ===Project Proposal=== | + | <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>Scope of the Practicum</font></div> |
| − | [[Update of Wikipage]]: 1%<p>
| |
| − | [[Proposal Report]]: 14%
| |
| | | | |
| − | ===Mid-Term Presentation and Report===
| + | Students taking this course are required to form a team of three members. They will work closely with their industry sponsor to identify the business problem, to compile the necessary data, to transform the data into analytics data mart, to perform the analysis by using appropriate analytics techniques and tool(s) and to present their findings to the stake-holder of the project sponsor organisation. Last but not least, the students are required to document the lesson learned through working on the project in the form of practice research paper and present their paper in the Undergraduate Conference of Data Analytics. |
| − | [[Mid-Term Presentation]]: 10%</p><p>
| |
| − | [[Mid-Term Report]]: 15%</p><p>
| |
| − | [[Mid-Term Update of Wikipage]]: 5%</p><p>
| |
| | | | |
| − | ===Final Presentation and Report=== | + | For more information please read the [http://sisapps.smu.edu.sg/CDDR/Courses.aspx?P=104&C=1426&CT=(ANLY482)%20Analytics%20Practicum Course Design Document]<br/> |
| − | [[Final Report]]: 25%</p><p>
| |
| − | [[Final Presentation]]: 15%</p><p>
| |
| − | [[Project Poster]]: 10%</p><p>
| |
| − | [[Final_Update_of_Wikipage|Update of Wikipage]]: 5%
| |
