Student Supervision – Meeting 6 2018

Meeting No 6 for Semester 2 2017/2018
Date : Thursday 8th March 2018
Venue : Dr Suraya Ya’acob Room, 7.30.01, Level 7, MJIIT, UTM-AIS KL

Attendance: Students’ Progress Supervision attendance 8th March

Supervision progress: Students’ Progress Supervision 8th March 2018

Student Supervision – Meeting 5 2018

Meeting No 5 for Semester 2 2017/2018
Date : Thursday 1st March 2018
Venue : Dr Suraya Ya’acob Room, 7.30.01, Level 7, MJIIT, UTM-AIS KL

1. Nurul Hawani – Not Applicable
2. Norazilah – Not Applicable
3. Sharifah Izora – PHD Tahun 3 (semester 5) Defer this semester – 4 pm
4. ZairulAsraf – Msc Informatics Projek 2 ( Final Submission: 14 May) – Lunch Hour
5. Khairunnisa – Msc Assurance Projek 2 ( Final Submission: 14 May) Crucial* 10am
6. Raja Norhaida – Msc Assurance Projek 2 ( Final Submission: 14 May) Crucial* – 230pm
7. Jennifer – Msc BIA Projek 2 (Final Submission: 14 May)
8. Sharida Chan – Msc BIA Project 1 ( Final Submission: 14 May) – 2.30pm
9. Mesam – MANA Project 2 ( Final Submission: 14 May) Crucial* 3:30 pm

Students’ Progress Supervision attendance

Students’ Progress Supervision

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Utilizing Tableau Free Software for students

If you’re a student looking to land an internship or your first full-time job, you probably know that companies are looking for people with data skills. But they’re not just looking for any data talent—they’re specifically looking for people who know how to use Tableau. In fact, Tableau was recently listed as the third fastest growing technical skill in demand.

You’ve taken the first step in joining the community of over 100,000 students who are using Tableau each year! Now that you have your free license, you can begin learning these valuable skills that will help you land a job. Here are three steps to help you navigate the beginning of your Tableau journey:

1. Learn Tableau

The first step to being successful with Tableau is learning the tool itself. Recent grad Matt Atherton states, “Start with tutorial videos – first the Getting Started video on Tableau’s website. When you’re watching these, think about how to visualize your own data”. This short 25-minute video will provide you with an overview of Tableau Desktop from start—connecting to data—to finish—sharing your completed visualizations.

Once you’ve gotten the lay of the land, you can dive deeper into specific functionality with the Starter Kits and on-demand training videos on our website. As a student, Lynda.com is also a great resource, since many schools have subscriptions that allow for free access. Search for Tableau and you’ll find hundreds of videos and courses, many created by experts in the Tableau community.

Speaking of our community…

Our community is part of what makes Tableau so unique. Not only is our community active on our user forums, they also create a bunch of great training content. Check out the Tableau Reference Guide created by one of our Zen Masters, Jeffrey Shaffer.

2. Get inspired and start practicing

Once you start learning the functionality of Tableau, the next step is finding data you want to analyze. We’ve compiled a list of free data resources to help.

Another great way to find data is to check out the viz gallery on Tableau Public. Once you find an interesting viz, many authors allow you to download the workbook (simply click on the download icon in the bottom right-hand corner of the viz). From there, you can reverse engineer the viz to see how the author created it. Or, you can use the data to create your own viz. Here are a few of my favorite vizzes:

That’s not all. Makeover Monday, currently run by Tableau Social Ambassador Eva Murray and Tableau Zen Master Andy Kriebel, is a great way to start honing your data viz skills and get involved in a broader conversation about and with data. Each week a link to a chart and its data is posted online. Your task is to rework the chart and then share it on Twitter. This is a great way to engage with the Tableau community and get feedback on your work. And if that’s not enough, take your Tableau skills to the next level with Workout Wednesday.

3. Share your work

Once you’ve started created your own vizzes, don’t forget to publish them to your Tableau Public profile to start your data portfolio (learn how to do that here). A great example of this is Corey Jones’s profile. He started his data portfolio while he was a student at Saint Joseph’s University. Once you’ve published a few vizzes, you can add your Public profile link to your resume and LinkedIn profile to showcase your skills to future employers and get a leg up on the competition.

I wish you the best of luck on the start of your Tableau journey and can’t wait to see what you create. Don’t forget to enter your viz into our student contest for a chance to win Tableau swag. If you don’t yet have a free student license, request yours today!

learn more from here: https://public.tableau.com/en-us/s/blog/2017/09/3-steps-make-most-your-free-student-license

Predictive Analytics vs Business Intelligence

According to tibco (2017), flat dashboards (err… most probably, they are referring to BI) are killing analytics. When it comes to data visualization technologies, most vendors offer similar insights, along with graphing and storytelling functionality. What you most often see are screens with two or three panels that have a nice looking graph or two. If you click on the graph or adjust the controls, the visualization may change. It’s not bad. You can explore simple data sets, usually those stored in a spreadsheet table. You get fast results. You might even apply a statistical function or two. These dashboards are fundamentally fat. If you had magic virtual reality glasses and could pull the dashboard of the screen and look at the way it was made, you might see an inch or two of data and analytics behind each panel. If you want to change the data used or adjust the analytic, you go back to the spreadsheet or to the statistics package that calculated the analytic.

Flat dashboards provide a limited amount of insight. Usually, when fat dashboard technology is used in a company, it becomes a form of reporting, offering static information. The result is a proliferation of low-value visualizations that analyze small sets of data for individuals or groups. In a typical company, there could be hundreds or thousands of these low-value reports, which leads to a management and maintenance nightmare. Furthermore, because reports are uncoordinated, ad-hoc, and based on tiny slices of whatever-data-is-on-hand, they often lack a level of correctness and completeness, which canlead to incorrect conclusions and business mayhem. The old adage “garbage in, garbage out” too often applies to fat dashboards.

Predictive Analytics vs Business Intelligence

Uncertainties – current trend in visualization 2017

Dear Haida,

Please read and digest an uncertainty element while presenting your weather forecast. It is quite interesting, in trend and relevant to your project

https://eagereyes.org/blog/2017/communicating-uncertainty-when-lives-are-on-the-line#more-10159

Temporal Data and Weather Forecast

Weather data is temporal data. It changes according to time (hours, day, month or years). Since weather is crucial for us human to do our activities, people forecast it. By having forecast data, it helps business and people plan for their outdoor activities. In Malaysia – it is very crucial during kenduri kahwin, some people still believe and hire ‘bomoh hujan’ to forecast and prevent rain during the wedding day. More over, it helps business plan like transportation, construction and farmers for crop irrigation and protection. Eventhough the weather data in Malaysia is not as crucial as in four season country (since it will not help people on how to dress or either to bring extra coat for windy days) but forecast data can help in term of health issue like asthma and heat stress especially for children’sschool activities.

Since the data for Forecast weather is everywhere – from your own handphone, PC, TV and radio. I think for Haida (since you are from MANA – assurance course), it is time for us to check the accuracy between the forecast data and the real one. It will help to prove the accuracy of Jabatan Meteorologi Data. If the comparison has been done, why dont we visualize the comparison to ease the forecast data understanding.

Thus, the objectives for Haida research can be something like this

  1. Compare between forecast and real set of weather data.
  2. Visualize the comparison
  3. Identify the accuracy of the forecast data – reliability/assurance of the data OR maybe we can access how people trust/ the data?  (hmm.. this can give awareness about the credibility of jabatan meteorologi data)

In order to do that, what you need to do this week is:

  1. Identify and get the forecast and real set of weather data (try to get a set for 10 days first)
    1. Type of data – the general one, things like this:

Source: weather underground weather forecast.

  1. Bring that set of data for our next discussion (17 August 2017).
  2. Have a peek on your expected outcomes, something like this (but not necessarily exact):

  1. Read and understand this http://stat4701.github.io/edav/2015/03/25/hafiz-weather-1/very good to get some ideas for your LR (please explain to me your understanding about this article in our discussion later)

How is telecom industry benefiting their data?

How is telecom industry benefiting the big data?

Telecom industries are sitting on a gold mine, as they have plenty of data. But what they require is a proper digging and analysis of both structured and unstructured data to become a valuable asset to the industries.

Big Data from the perspectives of telecommunication industry

Through proper digging, they are able to get deeper insights into customers’:

  • Behaviour – combat fraud
  • Service usage patterns – marketing interest, marketing agility  (related to temporal data)
  • Preferences
  • Real time interests – real time customer insights (related to temporal data)

From Acker et al (2013), the telecom industry must experiment their own data. Demonstrating what they have on hand to see what kinds of connections and correlations it reveals, This process must be carried out iteratively to emerge the more efficient operations and more effective marking.

Source: Acker et al (2013)

References:

  1. http://bigdata-madesimple.com/11-interesting-big-data-case-studies-in-telecom/
  2. Ackers (2013) Benefiting from big data. A new approach for the telecom industry. published by Booz & Company.