Digitizing course content

"Psychology 101" is on a trajectory to be completely digitized. For every topic, there are lecture slides (an okay option) or a video (an even better option).  It is a suboptimal use of an instructor's limited time to make and deliver introduction to psychology lectures.

However other more difficult material is on a slower digitization trajectory.

When I taught Sensation and Perception, I leveraged all digital resources I could find but still had to make and deliver lectures. Here is my small contribution to digitizing more advanced psychology course content:

You can always quit

Just because you have always done something doesn't mean you always have do it.

Every person has sunk costs. But not every person has the sunk cost fallacy.

It is always possible to change. If even if it is just your internal point of view.

A great visualization (but still a misrepresentation)

This is an example of a great visualization that serves the storytelling. However it confuses income with wealth. Income is money earned. Wealth is money owned. They are separate concepts (but often related). Wealth disparity is much higher than income disparity and is much harder to influence.

On a side note, I am glad I don't have care what the "average" American thinks. In fact it matters to very few people what the "average" American thinks. It might matter if you are a national political candidate or a marketing director for a large brand. Otherwise, it doesn't matter.

People's opinions don't matter. Just like a plane landing, it is what it is. Just because you want a plane landing to be smoother (or the economy to be more "fair"), doesn't make it that way. The forces of physics (and the market) create constraints. You can try to change those constraints but most likely you end up frustrated and tired. I suggest to option in or out. Choose to get on the plane (or engage in the market). Choose to work for an organization that has income disparity (or not).

Choosing how to show up

I choose to show up for my commitments: prepared, on time, and remain fully present.

Preparation is easy. Knowing a little about any person or topic is only a few clicks away.

On time is easy. I don't overbook myself (and leave early).

Fully present is not so easy. I'm easily distracted so I don't tempt myself. I make any possible distraction physically impossible to access.

It changes what I choose to say yes to. If it is worth my time to be there at all, it worth being the best version of myself.

A hierarchy of systems

I want the minimal maintainable systems in my life that allow me to make beautiful and/or useful things. My primary systems focus on time and project management.

Here is my timeline of system development (also rank-ordered for quality of system):

No system

A system I made up

A system collected from random parts of other systems

A complete archaic system

A complete modern system

A complete modern system modified to my personal situation

Take calendars. In the past, I did not have a calendar. I apologize to anyone who had to deal with me back then. I don’t know how I got where I wanted to be at the right time and place. I went through the hierarchy of systems, step-by-step. Right now I use Google calendar with a subcalendar for each of my different roles and responsibilities. Because it works, I spend more time using the system than working on the system.

From counts to models

Almost all data science starts with counts[1]. How many people are clicking which box? How much time are people spending on a particular page?

Only after that stage data science does gets complex (and more interesting).

There is a similar development from descriptive to inferential statistics in other sciences. Measures of central tendency (i.e., mean, median, and mode) and variance are first calculated, then models and model comparisons (e.g., regression and ANOVA) are applied.

Social media is just now entering the count stage.  Most businesses have a social media presence (binary - yes or no). They have reached a critical mass of data and are starting to organize it. The organization is counts and sums. Very few organizations are thinking about moving to the social media strategy stage (model comparisons - making choices based on data).

Many problems have followed the same pattern. Today's data scale is larger, but the analysis has the same stages. The same class of solutions can be applied at each stage.

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1. A data scientist or statistician is usually brought in after data collection is well under way and there is an realization of its potential value. Collecting the best data in the right format probably did not happen. The classic, “I should have been at a much earlier meeting.”  ↩
   

The new gravity of San Francisco

I'm witnessing first hand San Francisco's latest tech wave.

I have been talking to many companies that are in the process of moving their offices to San Francisco proper. Other companies are apologizing for having to take shuttle buses to their palatial Silicon Valley estates.

It is interesting, possibly ironic, that Internet companies which are known for being digital are hamstrung by their physical infrastructure.