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bpierce

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Reply with quote  #1 

For his July/August/September 2015 Visual Business Intelligence Newsletter article, titled A Course of Study in Analytical Thinking, Stephen examines the knowledge and skills that we must possess as prerequisites to successful visual data analysis. He identifies nine topics and explains why each is necessary for a data analyst to understand and recommends useful books for each subject.

What are your thoughts about the article? Eventually, Stephen hopes to develop this material into a course. Do you think there are other subjects that data analysts need to understand to do their jobs effectively? Do you agree or disagree with any of the topics on Stephen's list or his book recommendations? We invite you to post your comments here.

-Bryan

BrahmB

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Reply with quote  #2 
Hi Bryan and Steve - well written article.  A lot of this can be handled by Industrial Engineering and/or Operations Research.

During my studies as a Systems Analyst I learned in my study material that the original Systems Analysts were in fact Industrial Engineers since they were trained in skills for managing technology and human interaction together.  Since then there are many Certifications TOGAF, COBIT, ITIL, CAP, etc for managing specific parts.

A great reference book would be Wayne Winston's book: Operations Research.
It gives simple and sometimes complex solutions to problems that can be solved with a computer. 

I agree totally with Stephen's statement in the news letter. 

Best of regards


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Brahm Bothma
BrahmB

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Reply with quote  #3 
Hi Steve and Bryan -

I like the fact that Steve suggest that one starts early on one studies at University.

Maybe something to look at is CAP (Certified Analytics Professional) for people who want to pursue it further.

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Brahm Bothma
sfew

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Reply with quote  #4 
Brahm,

Are you personally familiar with the CAP program? I've found that most of these credentialing programs focus only on technical skills, which wouldn't address to the need that I've described. If you are familiar with CAP, please tell us how this program addresses the specific thinking skills that I outlined in the article.

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Stephen Few
KevPickering

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Reply with quote  #5 
Many thanks Stephen for addressing a field in which I feel 'techies' would benefit from to make them more 'complete' as a professional.
Ok, I realise I'm pretty competent and confident with the tools required to do my job of handling and presenting data. I openly admit I could certainly expand my knowledge domain to include a greater understanding of analysing data for insight etc.
This article couldn't be more timely.
I have just enrolled with the Open University, starting with Introduction to Statistics, which started this week.
I'd like to tie this in with the books you're outlining in the newsletter. So, with a busy schedule, can they be read randomly or would you recommend reading them in a certain order?

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TomC

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Reply with quote  #6 

Thank you for this article - I went to my library and requested two of the books (and checked out another).

This is definitely a course I would be interested in attending. 

A recent experience has led me to wonder if you couldn't do this in multiple parts:  start with an in person conference; do a middle part with online work, maybe even combining participants into groups and have them complete exercises or review things together; provide coaching via email or phone calls; and culminate in another in person conference.  Have you thought about working with a university to see if your course could be worth credits?  That might make employers more willing to support employee involvement.

I like the content, especially the Data Sensemaking piece.

I recently completed a statistics course as part of a second bachelor's program, but this is an area I would like to explore - I can get lost down the rabbit hole, but I really want to know if I'm in the right hole, or does my hole connect with another. 


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Tom Chervenak
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raval

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Reply with quote  #7 

Great initiative Stephen. And as you asked for feedback, here are a few thoughts:

  1. I suggest to start by addressing the ‘systems’ matter first. Having a good understanding of systems makes people look at their projects in context – a strong attribute in analytical thinking. The starting point can be the crude diagram attached; it exemplifies the principles of control systems. Correction on 9/22: the diagram should have included a Controller block ahead of Process to be explicit.
  2. I would not clutter the curriculum with too much arid theoretical topics, at least not the courses for people already employed in data sensemaking jobs. For instance, in my 20 years as industrial engineer followed by 20 as data architect/decision support systems developer, I do not recall one instance when I had to switch my thinking course from System 1 to System 2 of my brain. :-)
  3. As data can’t exist in a vacuum, I mean that it is always related to a particular field of activity, I would include a chapter about the importance for the data sensemaker to be as knowledgeable as possible about the field to be analyzed.
  4. I would include Malcolm Gladwell’s “Outliers” on the list of recommended readings. And Nate Silver’s FiveThirtyEight blog as a stop for inspiration.

 

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Valentin R, New York

BrahmB

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Reply with quote  #8 

Hi Steve

 

CAP (Certified Analytics Professional) are hosted by INFORMS (Institute for Operations Research and Management Science) and herewith follows a few comments from Robert Benneto from OPSI Systems who recently earned the CAP Credential:

 

Having completed the CAP certification process I can say that it shouldn’t be considered a programme (from a learning perspective). CAP fills a need that industry has to ensure that practitioners meet both the technical and soft-skill requirements. Put simply, the certification process requires a either four-year academic degree from an accredited university or institution with 5 year’s work experience or a two year degree with 10 year’s work experience. The actual examination criteria cover the entire process chain from business problem framing to mathematical techniques. Here is a nice article on the certification along with the breakdown of the examination content (http://www.burtchworks.com/2014/08/19/cap-the-certification-program-for-analytics-professionals/). If you have the skills to obtain CAP, you probably should as INFORMS have ensured a rigorous testing process which maintains a high standard for those who qualify.


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Brahm Bothma
BrahmB

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Reply with quote  #9 
If one read up further on the INFORMS site then one would

Expected to launch in the fall of 2015, the Associate CAP (aCAP) designation is designed specifically for entry level analytics professionals who are educated in the analytics process, but who don't have the extensive experience required to earn the full CAP credential.

Source: https://www.certifiedanalytics.org/associate_cap.php

Maybe this can complement your suggestion.

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Brahm Bothma
heinzel

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Reply with quote  #10 
Just today, I ran into a sponsored link on linkedin for a Master's in Data Science at UC Berkeley. Unfortunately, they don't reveal too much there unless you have them send an email, which I did not feel like doing. However, some googling revealed the site for it:

https://datascience.berkeley.edu/

The program is new, and they have chosen to deliver it online. Their intro video promises to discuss the curriculum, but unfortunately doesn't (or you have to listen to much more video parts than my attention span let me). Worth checking out?
sfew

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Reply with quote  #11 
Heinzel,

Many data science programs have popped up at universities over the last few years. The curricula that I've reviewed are focused on advanced statistics and programming, with little or no coverage of fundamental analytical thinking skills. I've received several requests for desk copies of my books from professors who teach data visualization courses that were created as a part of these programs, few of whom have any actual knowledge of or experience with data visualization. Some decent data science programs might exist, but most of them have been cobbled together quickly and exhibit a curriculum that ignores analytical thinking and skips directly to advanced statistics and technical skills. They are exploiting the current hype about data science for its revenue generating potential. Our universities have become businesses, quickly bringing products to market to grab their share of the revenue pool. I suspect and hope that some exceptions in the form of thoughtful, well-designed, comprehensive programs in analytics exist, but I'm not certain that they do. If you know of one, let us know, but only if you know for sure that it fits the bill.

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Stephen Few
sfew

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Reply with quote  #12 
Valentin,

If you have never shifted from System 1 to System 2 thinking, you have never engaged in data sensemaking. Have I misunderstood what you wrote?

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Stephen Few
sfew

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Reply with quote  #13 
TomC,

Thanks for the suggestion regarding the logistics of teaching this curriculum. I don't think it would work to have three separate in-person sessions that would require participants to travel, but it might work as a course of study that began with a series of bi-weekly online lectures, with participants doing the readings on their own, followed by a single in-person workshop of two or three days to consolidate the information through discussions and exercises. I'll give this some more thought.

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Stephen Few
sfew

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Reply with quote  #14 
KevPickering,

I don't think that the order in which these readings are done will matter a great deal, except in one case: read Kahneman's book before reading Cadsby's, because the latter builds on the former.

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Stephen Few
raval

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Reply with quote  #15 

Stephen,

I welcome the chance to clarify the “shifting from System 1 to System 2 thinking” statement. What I meant is that, in the normal course of cogitating, people do not knowingly switch between the two thinking systems, the shifting comes to us naturally based on the problems we face. The majority of us never heard of this classification of the ways our brains react when a stone vs. a data-analysis project is thrown at us. That’s why I branded such a discussion an arid theoretical topic.

Regards,


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Valentin R, New York
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