Performance indicators

Tyranny of success: Non profits and metrics

Tracking health

Wearable tech

Here are a few reports worth checking out. The first one is the UN’s 2018 report on global warming. You probably saw a headline or two about it last week. It’s worth seeing the whole report and the executive summary (which is 34 pages). In any case, you’ll see the data, the hundreds of authors and contributors and a few visualizations.

The other report is one on data literacy by a company called Qlik, which is a Tableau competitor. It captures business perceptions of analytics and data (and fits in with what we’re talking about the next two classes).

Separately, there’s a Reddit thread called Data is Beautiful and has some interesting visualizations and approaches.

For instance, I came across this story on someone on Reddit visualizing his Tinder experience only to find another person did their 500-day OKCupid outcomes.

Both of the data sets (along worth a bunch of others) are on the Data is Beautiful Reddit. The thread highlights the democratization of visualizing data. Worth checking out for giggles.

Damn Excel (amen to that)

Data credibility

Data corruption tricks

Clean data top 10

For 5 points on your lowest scored exam, here’s the assignment for the Analytics Challenge. This is good practice for the group project as well as an opportunity to compete and maybe win some money. I will look these over the Friday Oct. 26 before the Challenge is due on Oct. 31.

Form teams of anywhere from 2 to 4 and download the challenge specs and data from the site once you choose one.

Here is the study guide for the first midterm exam. Here is also a more detailed version based on notes from class.

NYC Cab Viz

Telling story with Data

Cool infographics Chapter 1 and Chapter 6

Session 4.1:

• Chapter 2: Good Graphics? Handbook of Data Visualization (Unwin—-pages 57-77)

Session 4.2:

• Stephen Few on Data Visualization: 8 Core Principles (Hoven)
• Watch out, Terrorists: Big Data is on the Case (Acohido)

Here’s a good read on Pew on AI and bias. It’s going to be an ongoing topic in your careers well beyond this class.

IBM is also working to minimize AI bias.

The IBM site on Trusted AI is here and here’s the recap.

• Bias in big data
• In data we trust
• Filter bubble

A few bonus reads for those of you interested…

Some of you noted you have a focus communication, PR, advertising and marketing. Here’s an IAB report/outlook on emerging data trends in advertising (as it applies to digital media).

Related riff on whether Facebook and Internet companies are evil. A whole lot of data (and metadata) in those Facebook databases.

And the most recent item on Facebook’s impact on mental health