Our impressions
Given our diversity of impressions we thought it would be more useful to share our impressions. Without further ado, here they are.
P1
I found Mark Meloon’s talk very useful. I have actually started posting more regularly on my own LinkedIn account and it has indeed captured more attention from others. In fact, I have even received emails from companies interested in hiring someone with my expertise. Brandon Rohrer clarified some trends that I had noticed about data science. I identify with the “Detective� role and I see that while I may aspire at times (unsuccessfully) to be a “Generalist – or someday a Unicorn�, my experience as a Detective is very worthwhile as well. I love data visualization and I loved Kate Strachnyi’s talk. I found her tips to be very clear reminders for how to continue with my own visualizations. The talk by Mico Yuk was a good reminder to keep overall goals in mind as you work and to regularly take a step back and assess if your work is really proceeding in the direction and at the rate that you planned.
P2
Mark Meloon’s talk emphasized the use of LinkedIn for networking and job hunting. He interviews job candidates for his company so his viewpoint was a direct reflection of someone who uses the website to find and/or assess job applicants. I liked that he gave both good and bad examples of actual profiles and messages he’s seen on LinkedIn. He also noted that, to get a foot in the door of a job posting, you don’t need to directly know the hiring manager, but reaching out to anyone you know in the company, even if it’s a second- or third-level connection (i.e. friend of a friend), is better than nothing, as long as you do it right. I do wish he had spoken about other social media platforms, such as Twitter, and how they compare to LinkedIn for networking.
I found the breakdown of skills and job types by Brandon Rohrer to be really instructive. It made me reflect on my own interests and skills in a broken down way, and I think it will help to have this framework for both future job hunts and interviews. I particularly like that he emphasized it’s okay/normal to not be great at everything related to data science – it’s a broad field – and that people with a narrower set of expertise are still needed and valuable for specific jobs. His talk also gave me some ideas of skills I may be able to work on and add to my portfolio to round out my skill-set. I would recommend this talk to anyone in the data science or analysis fields that is looking for clarity or definition in their current job or career path!
P3
Kate Strachnyi’s talk was a great reminder of the importance of keeping your audience in mind when presenting information and making sure that visualizations are not just accurate but also easily understood. Her list of common issues was a helpful summary of guidelines I’ve heard before, and I appreciated the examples she used. In particular, I think I often run into the challenge of “information overload� when I present informally to others – I need to remember that it’s not enough for the information to be there, it also needs to be arranged in way that lets people understand it quickly.
Mico Yuk’s talk was probably more applicable to someone working in a corporate field rather than an academic one, but the main idea of framing data as a story and keeping the goal in mind was still relevant to me. Some of the suggestions, like asking the “right questions� of your user, could easily be reworked for research (even if the user is just be me). I haven’t worked with a storyboard before, but it would be interesting to see if that approach could also apply to planning out analyses for a research paper – the goal might be the question we’re asking, KPI the metrics we’re using to answer that question, trends the conclusions we can draw, and actions the next direction of analysis. The translation from business to academic research probably needs some tweaking, but I might try this approach on a future project to help with organization and keeping the bigger story in mind.
P4
Mark Meloon’s talk reminded me that many use LinkedIn for networking which hasn’t been that common in my experience in academia. This is something I would need to keep in mind for advising students in the future that are either unsure of staying in academia or want to go to industry. I do brush up my profile once in a while, and parts of Mark’s advice applies also to CVs (writing them and sending them via email): basically, be genuine and respectful of others.
Brandon Rohrer verbalized distinctions in data science roles that I had either heard of before or had some intuition behind them, but hadn’t actually spent the time to see them as clearly defined as Brandon did. I was also quite curious of everyone’s reaction to his talk and how each of us labelled ourselves. For example, maybe X thought Z was a unicorn, but Z perceived themselves as a beginner. In my case, I think that it’s probably too ambitious to get to the unicorn level. I’m simply aiming to get to (or am at) a level where I can understand most of the terms and conversations, but then go back and research a bit more if I need to as preparation for a follow up meeting. I guess that I’m a generalist.
Kate Strachnyi’s key points are I guess topics that I’ve heard before and loosely follow. I think that her audience is different from mine as she seems to create visualizations that are used in many company presentations. I’m frequently under pressure to get a simple version of a plot done where we can see the trend in the data and only work on polishing a few selected plots that get highlighted in a research paper. Though I guess that I could/should spend a bit more time thinking about the plot design and colors before I make the next one. For that, I would like to learn more about the paletteer R package:
Mico Yuk talked about SMART goals. Hmm… I don’t remember what that stood for, so I clearly would need to re-watch her talk. After skimming through it again I guess that I can only say that it was hard for me to relate to her talk because I haven’t been in a project that involved all planning steps that she talked about. While it wasn’t for me, it might be useful to you, so give it a try!