I’m learning data visualization for 180 days straight because I need to #JFDI. See post explaining how and why I’m doing this.
I did not do too much Data Viz today. I’m moving to NY in less than two weeks so today was a necessary day of tying up loose ends/beginning to pack etc, but I’m looking forward to having put a bunch of worries to rest and having a clean mental slate for tomorrow. Trying to be kind to myself here and acknowledging how exhausting/stressful moving and tying up loose ends in life is in addition to trying to learn a lot in a short amount of time but also getting myself to move things along in an effective and non-exhausting way.
Tomorrow I plan to stack my day: Code Tutorial, Data Reading + Decomposition, putting a time cap on both those tasks, before putting a bulk of time on finishing the data viz I had started working on and making something else too. I also need to get up to speed on Qlikview, a product I’ll be using at my new job.
Visualization Worked On or Created:
-Priority for tomorrow
Decomposition of a Visualization:
Reading and Learning Data Visualization Theoretically/Critically:
- Importance of connecting Data Scientists with decisionmakers.
- “Empowering teams is about removing the burden of reporting and basic data exploration from the shoulders of data scientists so they can focus on more impactful work. Dashboards are a common example of a solution. We’ve also developed a tool to help people author queries (Airpal) against a robust and intuitive data warehouse.
- Decision-Making Structure
- We begin by learning about the context of the problem, putting together a full synopsis of past research and efforts toward addressing the opportunity. This is more of an exploratory process aimed at sizing opportunities, and generating hypotheses that lead to actionable insights.
- That synopsis translates to a plan, which encompasses prioritizing the lever we intend to utilize and forming a hypothesis for the effect of our efforts. Predictive analytics is more relevant in this stage, as we have to make a decision about what path to follow, which is based on where we expect to have the largest impact.
- As the plan gets underway, we design a controlled experiment through which to roll the plan out. A/B testing is very common now, but our collaboration with all sides of the business opens up opportunities to use experimentation in a broader sense — operational market-based tests, as well as more traditional online environments.
- Finally, we measure the results of the experiment, identifying the causal impact of our efforts. If successful, we launch to the whole community; if not, we cycle back to learning why it wasn’t successful and repeat the process.
Visual Information Seeking: Tight Coupling of Dynamic Query Filters