The MGS Blog

Tuesday, January 28, 2020

Countries Raked by Attractiveness - Global Services Location Index (A.T. Kearney, 2019)

"
- Globalism is alive and well in information technology outsourcing, business process outsourcing, and voice services
- While India ranks number one, the inclusion of digital resonance metrics shrank its lead and gave advantage to onshore locations such as the United States and United Kingdom
- Automation and cybersecurity are changing the outsourcing landscape...
...The GSLI has traditionally identified locations that can best provide information technology (IT), business process outsourcing (BPO), and voice services based on countries' Financial attractiveness, people skills, availability, and business environment. The 2019 GSLI, however, includes a new digital resonance category to capture the effects of digital transformation, especially automation and cybersecurity, on the global services landscape. 
" (source)

India
China
Malaysia
Indonesia
Vietnam
United States
Thailand
United Kingdom
Brazil
Philippines
Mexico
Estonia
Colombia
Egypt
Germany
Lithuania
Bulgaria
Russia
Peru
Ukraine
Latvia
Chile
United Arab Emirates
Poland
Sri Lanka
Portugal
Canada
Romania
Argentina
Mauritius
Hungary
Bangladesh
Czech Republic
Singapore
Slovakia
Morocco
Pakistan
Panama
Turkey
Uruguay
France
Spain
Kenya
Costa Rica
Ghana
Trinidad and Tobago
Ireland
New Zealand
South Africa
Israel

(from: A.T. Kearney (2019). Digital resonance: The new factor impacting location attractiveness - the 2019 A.T. Kearney global services location index. Technical report, A.T. Kearney. link)

Friday, January 24, 2020

Statistical Analysis, Data Science and Programming 2020

“This class is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalised feedback on every exercise.”


Our student group is using DataCamp to develop their statistical skills to apply to the Outsourcing and Offshoring class and data science skills for the Machine Learning class in the summer.

DataCamp access is by invitation only via your @ucdconnect.ie email address.

The class page is as follows;

https://www.datacamp.com/enterprise/statistical-analysis-data-science-and-programming-2020/

If you get stuck have a look at the DataCamp Community site, there are a plethora of DataCamp Tutorials for Python, R and more to choose from. Each Tutorial provides accessible and easy-to-understand resources for the topic of your preference.

Reading an article and not sure how to write about it yourself?

- readings, precis, impact, application -

Prompting questions:
  • What did I learn from reading this article?
  • What was the intention of the authors?
  • Who is the audience for the article?
  • How could I use the article?

Readings are often difficult to understand or alternatively, to interpret and make sense of. I've often read a paper and said to myself "so what", "it's obvious", "what's the appeal of this stuff?" Sometimes I've been confused, overwhelmed with detail or just don't get the point. I've also read papers that have set off ideas, recalled past experiences, given an outlook that changes something I thought I knew well but now see in a different light.

Writing a precis of a paper turns the whole process back on itself somewhat; I go from being the reader of the paper to being a writer. Writing about a paper demands something of me, not just my impression of the paper and the information contained within, but how I felt about the ideas expressed, how I saw them applied, and reflected on their wider impact.

Written comments on a reading need to be succinct (if you go past a page then perhaps you should be writing a new paper?), and impact-full. Get to the point, don't just summarise, criticise! Refer to other works in a meaningful way (counter examples, supporting examples), and reflect on the bigger picture. If there are implications for practitioners and practice then state them, particularly if they are personal, affecting you.

When criticising a paper you should always attempt to be fair. Criticise it on its terms, not because it doesn't address certain areas that you think are more important; there may be good reasons for a paper's omissions: limited space, out of scope, irrelevance.

Finally, keep to the limits, word counts shouldn't be treated as "targets". If you can say less then say less; less is often more. It takes time to distill your comments and the result is often unexpected, but often in good ways.

Pointers
  • Pick out some aspect of interest from the paper
  • Comment on it (there are no wrong answers, it's just an opinion)
  • Link it back to design processes.
  • And consider linking your argument with pertinent external readings.
Try not go off on a tangent or indulge yourself in a flight of fancy. But if the paper sets off your creative side then explain your logic:
"The reading included a discussion of X which made me consider Y (not in the reading) because...".
Relate it back to the course; to continue:
"...but both X & Y are pertinent to Z which we have seen is a fundamental to the work of analysis, design and development"

The following rubric (a protocol or procedure) on assessing a written work may also be useful...
Audience: Who is the reading directed at? Is a question formulated, is it interesting and clearly put? Did the author clearly explain the purpose of the article?
Structure: Are the thoughts/arguments connected? Is there a logic to the presentation of ideas? Is theory utilised? If so is it treated critically or uncritically (just applied)? Do the authors anticipate and respond to counter-arguments?
Style of evidence: Does the work offer conjecture and possibilities based on the literature? Does the work offer empirical matter? Are assumptions stated? Is a philosophical foundation indicated?
Validity: How is the work positioned such that we understand how to test the extent of its claims, justification, rigour.
Rhetoric: Is the article persuasive? Are the findings, discussion and conclusions convincing? Does the work present implications and impacts? Are there behavioural, managerial, organisational consequences?


Further reading (about reading...no irony in that is there?)

  • R. Subramanyam. Art of reading a journal article: Methodically and effectively. Journal of Oral and Maxillofacial Pathology : JOMFP, 17(1):65–70, Jan-Apr 2013. (link)
  • E. Pain. How to (seriously) read a scientific paper. Science, March 2016. Available online at http://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper (Accessed: 29 November 2017). (link)
  • A. Ruben. How to read a scientific paper. Science, January 2016. Available online at http://www.sciencemag.org/careers/2016/01/how-read-scientific-paper (Accessed: 29 November 2017).

Tuesday, January 21, 2020

DataCamp for data science (R/Python and others)

We encouraged students to use DataCamp last year to develop their statistical skills for the Outsourcing and Offshoring class and data science skills for the Machine Learning class.

We were delighted with the feedback and also the results. DataCamp allowed novice R/Python users to improve their understanding and ability using these technologies. It was like giving students a boost of mental vitamins for technology! Really really useful.

Monday, January 20, 2020

Term-Paper presentation peer-assessment...

Consider using the following criteria for peer-assessing the presentation.

Criteria 1: Motivation

0 = Unclear what drove this research or why it may be important.
1 = Motivation: You can clearly identify 'why' they studied this particular problem and 'why' it may be important.
2 = Clear motivation, plus they clearly identified the problems(s) by initial analysis and/or presented extant knowledge, current hypotheses, and identified gaps in knowledge.

Critera 2: Data

0 = Unclear what research data was gathered or why what was done is relevant.
1 = Data through discovery: They have shown that they have independently researched the problem area(s)
2 = Discovery is evident AND the research data gathered convincingly addresses the problem area, that is, the kind of data gathered is likely to yield insights that may address the problem area.

Criteria 3: Analysis

0 = Unclear how findings or recommendations were arrived at.
1 = Findings are analysed cogently and convincingly; convincing arguments for arriving at findings.
2 = Findings AND responses developed in context of prior knowledge. Synthesis built on findings – clearly addresses the problem(s) and integrates learning from others.

Criteria 4: Engagement/Impact

0 = Very difficult to comprehend, understand, read, hear, etc. I would not watch this presentation again.
1 = A competent presentation, well organised with suitable and meaningful appropriate content.
2 = A really excellent and convincing presentation that conveys clear messages, for example effectively using images, dialogue, humour, shock etc. I would watch it again and encourage others to watch it.


Alternative presentation assessment rubric 

Threshold requirement: Originality and own work; Others' ideas graphics and quotes properly cited, acknowledged and referenced (No Plagarism!).
Equal weighting applied to the following:

  1. Argument demonstrates analytical skills. I was convinced by the evidence and the argument.
  2. Message and conclusions give evidence of reflective thinking and deep engagement with an advanced research topic. I received a clear convincing take-away message.
  3. Overall impression: Is piece competent and polished? Has producer showcased their domain knowledge and professionalism.