Day 5 of 180 Days of Data Viz Learning

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:

-Break day

Code Learning:

-Break day

Reading and Learning Data Visualization Theoretically/Critically:

At Airbnb, Data Science Belongs Everywhere: Insights from Five Years of Hypergrowth

Three Takeaways:

  1. Importance of connecting Data Scientists with decisionmakers.
  2. “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.
  3. Decision-Making Structure
    1. 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.
    2. 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.
    3. 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.
    4. 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

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