Dear Anna,
Thanks for your speedy response.
I carefully read your note, as well as the
Data Science Skills Competency Model.
On that basis, there are several things I'd like to clarify:
- When I went to graduate school the fields "machine learning," "data science," and for that matter, "behavioral economics" did not exist - at least in name. This, however, does not imply they didn't exist in substance.
- I could not have earned my degree without knowing how to identify valid, reliable measures to render hypotheses testable. Moreover, I could not have earned my degree without demonstrating, through original research, that I know how to:
- Construct a credible, well researched, testable, meaningful hypothesis;
- Choose appropriate measures for all variables;
- Design/implement a process for collecting appropriate data in as unbiased a manner as possible;
- Collect, record, and organize the data, taking into account levels of measurement;
- Clean the database;
- Plan, construct, and execute an analysis - using methods appropriate to the type of data, level of measurement, etc. (Yes, I have always done so using computers. As computing speed and power have increase, so has my ability to do more, better, faster, at less expense. And yes, I have been performing such analyses using multivariate methods such as: factor analysis, cluster analysis, regression analysis (all types), path analysis, multidimensional scaling, and etc. ad infinitum et nauseam since the mid- '80's*).
- Just because something was not formally affiliated with an official field when my colleagues and I put it to work does not imply that it was informal. There was, and is, nothing informal about anything we did. We worked in the real world, putting this knowledge to use, when it was almost unheard of. In fact, we did not announce what we were doing because we knew that if we were to do so, it might invite unwanted attention among those who might find it vexing. If you want a real adventure, try explaining to one of your employer's leadership team why your model contains 23 dimensions while he could count, at most, five: length, width, height, depth, and maybe time.
- I have worked in a business setting for my entire career (e.g., since the 1980's). I have worked with data - developing models, testing hypotheses, assessing predictive power, making predictions, finding the meaning in quantitative results, and using what I (and my team) learned to recommend actions. These actions, in turn, were tested by the ultimate arbiter: Reality. Had our work product produced poor results, I assure you, we would not have kept our jobs.
- While I appreciate this advice: "Highlight those skills in your job interviews, bring those examples of how you have used Data Science in your line of work, demonstrate how the skills you have acquired through formal or informal education, and through working with real data were used for better decision making or to unearth previously hidden insight," please be assured that I have done so - to the extent possible. My 'line of work,' incidentally, is management consulting - where numbers matter - greatly.
- I am not suggesting that I know everything there is to know - au contraire. For example, knowing how to 'train' a model, which was part of a field known as "decision science" when I was in school, is an area where I need to build my skills/knowledge. That said, there is no no other area among the seven competencies where I do not have ample experience.
Please understand that my response, above, is not about me. It is about me and people like me, who could bring so much value to any enterprise, but who get ruled out early because hiring managers lack the 'eyes' to 'see' the promise they represent.
Best Regards,
Sara
* Note: in the mid-80's, we did this by renting out the Temple University mainframe on the weekends, at $18 per hour, to process our data.
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Sara Wedeman, PhD
Founder
Behavioral Economics Consulting Group, LLC
Philadelphia PA
+1 (267)825-4044
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Original Message:
Sent: Thu April 02, 2020 04:06 PM
From: Ana Echeverri
Subject: Community Office Hours, April 2
Hi Sara,
The Certificate serves as an introduction to Data Science, but it does not build all the skills required to become a Data Scientist. And it especially may not be enough for most Data Science jobs in which you will be competing with professionals with real world Data Science expertise and/or Graduate Education solely focused on Data Science skills. However in your case it looks like you have very relevant skills acquired throughout your career that may be very relevant to what organizations are looking for. I would like to refer you to this white paper where we discuss Data Science skills in a granular way: Data Science Skills Competency Model. You can use the information on this white paper as a self assessment tool to determine which skills you have and which skills you still need to develop. Finding the first Data Science job is challenging for most individuals that are self training (it is a lot easier for people with Master Degrees in Data Science/ Analytics or with PhDs where they acquired relevant Machine Learning experience), but it is not impossible. I would recommend you focus on assessing which skills you may still need to develop, and on acquiring some real world expertise in your own line of work. Which data do you have? can you build hypothesis and build models that proof/disproof your hypothesis? can you explain how you trained different models, how you evaluated them and came up with the best model for your data, can you interpret those results for application in a business setting? Can you demonstrate how your work has impacted decision making processes? Highlight those skills in your job interviews, bring those examples of how you have used Data Science in your line of work, demonstrate how the skills you have acquired through formal or informal education, and through working with real data were used for better decision making or to unearth previously hidden insight. As a hiring manager that hires data scientists frequently those are the things I look for.
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Ana Echeverri
Original Message:
Sent: Thu April 02, 2020 02:37 PM
From: Sara Wedeman
Subject: Community Office Hours, April 2
I have read a lot about how "anyone can be a data scientist," but my experience suggests this is not entirely credible. I completed the Professional Data Science certification series on Coursera, easily, with a 99.2% overall score for the 9 courses together. I have vast experience with multivariate statistics, for use in decision making, in a business setting - but the second interviews always end up spewing me out of the pipeline. Why? because I struggle with the bizarre - and for an experienced person - demeaning "quizzes" and structured exercises. Often, the 'spewers' know much less than I about data analysis/data science - several times I've been rejected by people whose only experience was 'on the job' training. It's tremendously frustrating. I do not wish to complain - more, I'm looking for ideas about how to surmount this obstacle so I can get on the other side (e.g., employment in this field) and start doing/learning.
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Sara Wedeman, PhD
Founder
Behavioral Economics Consulting Group, LLC
Philadelphia PA
+1 (267)825-4044
Original Message:
Sent: Thu April 02, 2020 12:20 PM
From: Christina Howell
Subject: Community Office Hours, April 2
Hello! Thanks to everyone who registered for our first Data Science Community Office Hours.
Here are some points of discussion that we'd love to hear your feedback on:
- Over the next 3-6 months, what progress do you want to make on your personal data science/AI journey?
- What resources or services are you looking for to help you succeed? (e.g., training, mentoring, study groups... whatever you can think of)
Thanks for sharing your comments in the thread below by hitting the 'Reply' button on this entry.
If you are participating in the live event, please feel free to alternatively add your response in the General Chat or raise your hand to share with the group.
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Christina Howell
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#GlobalAIandDataScience
#GlobalDataScience