Data Scientist, Data Analyst, Business Analyst, Machine Learning Engineer, Data Engineer, BI Analyst, BI professional, Database Engineer, Machine Learning expert, Statistical Analyst, etc... Many titles and much confusion surround these roles.
Because Data Scientists are in such high demand, many job seekers apply this title to themselves. Many employers -- who do not quite know the nuances and differences in roles, end up interviewing many wrong candidates who are not qualified for the role. This is a waste of their time, their employees' time, and the candidate's time.
Meanwhile, universities, on-line courses, bootcamps, and on-line learning forums are all offering a wide variance of material for training. Most programs have little in common with each other. So even the trainers are confused as to what skills are necessary for a data scientist to possess.
How can we fix this confusion by Employers, Educational Programs, and Candidates?
At August 5, 2019 we ran our workshop on defining the variety of roles related to Data Science. In this workshop, we have created an interactive program with short presentations, interactive panel, and discussion sessions to address the challenges facing us all.
You can reach most of workshop materials by clicking here.
The Full Workshop Program was as follows:
1:00pm - Workshop Introduction and Motivation for Initiative for Analytics and Data Science Standards (IADSS) - Usama Fayyad (Twitter: @usamaf), Workshop Chair - Chairman & CEO, Open Insights
1:15pm - IADSS Research Approach and Initial Survey Results - Hamit Hamutcu, Workshop Co-Organizer – Co-Founder, Analytics Center
1:30pm - Short Presentations by Invited Speakers
Ming Li, Research Scientist, Amazon; University of Washington - "Skills to Master End-to-End Data Science Project Cycle & Strategies to Avoid Common Pitfalls"Stacey Schwarcz, Founder, Ariel Analytics - "Analytics Needs and Roles in Operations "Ary Bressane, Head of Data Innovation Lab, mnubo - "How data scientists can bridge the gap between data and business"Greg Makowski, Head of Data Science Services, FogHorn Systems - "Standardizing Data Science to Help Hiring"
2:45pm - Coffee Break
3:00pm - Panel Discussion - Proposed questions for panel are below - Participants:
[Moderator] Usama Fayyad (Twitter: @usamaf), Workshop Chair - Chairman & CEO, Open InsightsYing Li, Chief Scientist, Giving Tech LabsAmy Shi-Nash, Global Head of Analytics & Data Science, HSBC Bank Matt Curcio, VP Data, Ripple
4:00pm - Group Working Discussion - Discuss and propose ideas and roadmap for the development, maintenance and adoption of data science professional standards
5:00pm - Closing Remarks and Proposed Next Steps, Usama Fayyad (Twitter: @usamaf), Workshop Chair - Chairman & CEO, Open Insights
Proposed questions for the Panel Discussion:
For everyone: Brief introduction of yourself and experience
For everyone: What do you think makes a data scientist? How do you distinguish this from an analyst?
For everyone: Can you define a few most common roles you encounter in data science / analytics space and why do you believe we need clarity to differentiate between the roles? (e.g. a data scientist vs. a data analyst vs. ML engineer)
For Amy: You’ve worked in three different geographies in recent years, do you think there are any regional differences in how organizations define roles and manage talent or is this a truly global space now?
For Ying: I know you spend a lot of time in matching the right person to the right job and assessing skills. Can you share some tips and tricks in recruitment of analytics talent? In particular Data Scientists?
For Matt: You have worked across many sectors over the years. Can you talk about the evolution of the analytics professional and if you observe significant differences between industries?
For Amy: What are your thought on the future of the data professionals? How do you think advances in technology will impact the profession?
For Ying: What do we need to do to drive standardization of roles within the space, how can we drive adoption once initiatives like IADSS bring forth standard definitions?
For Matt: I’m sure many people at the beginning of their data related career reach out to you for advice. What are the top few things you tell them?
For Everyone: In one sentence, 10 seconds or less, what parting thought do you have for the audience?
With the permission of invited speakers & panelists, we published the most interesting materials from the workshop. Click here to see presentations from the workshop.
To hear latest updates from the research, you can follow IADSS on one of below channels:
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