Rocky Mountain DataCon
The fine folks with the Boulder/Denver Big Data Users Group are putting on a conference for data engineers, operations, and data science November 10th and 11th. They are calling it Rocky Mountain DataCon. In their words:
Rocky Mountain DataCon is Colorado's first annual conference focused on data engineering, data devops, and data science. Hosted by the Boulder/Denver Big Data Users Group, the conference will bring speakers from across the industry, with a focus on use cases and best practices. We strive to create a vendor-neutral environment where attendees can share ideas and hard-won experience with each other to further our craft.
Sounding like a pretty good time. It also so happens that I will be participating on a panel discussing getting into data science. It's a clever concept and will be in two parts. Part 1 is from the employers perspective. Part 2 is from the practicioner's perspective and that is the part I'll be on. Looking forward to the questions and an interesting conversation.
Description on topics as follows:
Learn the hiring philosophy of Big Data employers. Hear from those who have successfully entered the field. Explore existing and alternate strategies as we discuss the disconnect between the huge publicized need for data science practitioners and the obstacles/barriers to entry for interested practitioners/entrants. If 90% of data science is data prep, why do you need to know Gradient Descent, Matrix Factorization, Regression, etc. in order to enter the field? We are presenting two sequential 45 minute sessions in order to hear both sides of the story. The first panel is 4 employers (hiring managers in Data Science) followed by a separate panel of 4 employees (people who have successfully navigated the Data Science hiring process). Each panel will be moderated. Topics will include the following as well as allowing the discussion to go where the participants lead it.
- How employers decide on required skill sets for data science – especially at the entry level
- How specific roles are bounded (data analyst, data engineer, data scientist, etc.)
- Development of internal talent (career track to data scientist)
- Balance between passion and experience
- Weighting non-data experience in evaluating candidates
- Entry models (grad degree, boot camps, MOOCs, etc.)
- Participant paths into the field
- Gaps between expectations while searching vs. realities once hired, in terms of skill sets, tasks, etc.
- Suggestions on how the process could be improved
- The role of continuing and pre-hired education
- Advice for current job searchers