Learning Tracking September 2017

I’m trying to give myself at least half an hour during the workdays (or at least blocking two hours or so a week at least) to learn something new – namely taking classes on Treehouse, which I still have a membership to, reading job related articles, and reading job-related books. Tracking notables here as a self commitment and to retain in memory.

Treehouse

UX Basics Key Takeaways

  • Gather data about user behaviors, goals, and needs
    • Do this with user interviews, quant data (logs and analytics), and surveys
    • Be sure to analyze behavior types, and not just audience segments
  • Always answer the Q: “What is it the product we are working on provides for this behavior type?”
  • Manage content inventory: What exists (eg form values), gaps, and analyze

Ajax Handling Errors Key Takeaways

  • XHR request object contains important info about errors

Articles + Three Takeaways

Paying Down Your Technical Debt

  1. “If the debt grows large enough, eventually the company will spend more on servicing its debt than it invests in increasing the value of its other assets.”
  2. “Accumulated technical debt becomes a major disincentive to work on a project. It’s a collection of small but annoying things that you have to deal with every time you sit down to write code. But it’s exactly these small annoyances, this sand grinding away in the gears of your workday, that eventually causes you to stop enjoying the project.”
  3. Becomes a source of fear, dread, and loathing for teams so you should periodically service your debt

Evidence Based Scheduling

  1. Break tasks into hours (nothing longer than 16 hours) so it forces you to figure out what to do
  2. Keep timesheets tracking data for historical use
  3. Simulate the future

“But you can never get 4n from n, ever, and if you think you can, please email me the stock symbol for your company so I can short it.”

Reddit and Facebook Veteran On How to Troubleshoot Troublemakers aka “Debugging Coders”

  1. Job is not getting stuff to do people for you, it’s figuring out how to do something together.
  2. ‘The exact behaviors that make it so that the organization can stay alive, move fast, be scrappy can be exactly the same actions that cause a negative disruption later in the life of your company,” says Blount. “Troublemaking brings signs of large tectonic shifts, releasing pressure into the atmosphere. Specific rumblings are almost all borne fundamentally of some kind of frustration: moving too fast, not moving fast enough, taking too few or too many risks. These are signals — and opportunities — to assess underlying changes and growth in an organization.”’
  3. For nostalgia junkies (people who like the company that ‘way it use to be’), focus on the question: “What about next week bothers you?” and for the Trend Chasers – gotta measure the risks, what happens with this route over the next year, deploying it and rolling it out?

How do managers* get stuck?

  1. Failing to manage down: need to delegate, train team, pay attention to process, and say no
  2. Failing to manage sideways: build peer relationships, look for additional tasks, create a vision, become someone you’d like to report to
  3. Failing to manage up: attend to details, complains but doesn’t fix, drags outside of comfort zone, show yourself professionally to higher ups

How do individual contributors get stuck?

  1. “Everyone has at least one area that they tend to get stuck on. An activity that serves as an attractive sidetrack. A task they will do anything to avoid.”
  2. “When you know how people get stuck, you can plan your projects to rely on people for their strengths and provide them help or even completely side-step their weaknesses. You know who is good to ask for which kinds of help, and who hates that particular challenge just as much as you do.”
  3. “Knowing the ways that you get hung up is good because you can choose to either a) get over the fears that are sticking you (lack of knowledge, skills, or confidence), b) avoid such tasks as much as possible, and/or c) be aware of your habits and use extra diligence when faced with tackling these areas.”
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Day 99 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.

See post explaining how and why I’m doing this.

Code Learning:
  • Treehouse Back-to-Basics (again, not taking notes for basic review):
  • Knight D3 Intermediate D3 for Data Journalism
    • Module 1 Discussion and Exercise
Reading and Learning Data Visualization Theoretically/Critically:

Reading Nathan Yau’s book Visualize This

 

220-238 Visualizing Relationships and Spotting Differences

Three Takeaways:

  • General process of creating histograms: load data, filter outliers, set breaks, set layout. p 221
  • Showing small multiples with histograms, eg flipped 90 degrees side by side can be an interesting visual (Rotten Trilogy Finales as example)p 223
  • “Chernoff Faces displays multiple variables at once by positioning parts of the human face, such as ears, hair ,e yes, and nose , based on numbers in  a dataset.  The assumption is that you can read people’s faces easily in real life so you should recognize small differences when they represent data.  That’s a big assumption, but roll with it … useful time to time.”
    p 238

Day 91 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.
 See post explaining how and why I’m doing this.
Code Learning:

Treehouse Back-to-Basics (again, not taking notes for basic review):

Going through Front-End WebDev

Reading and Learning Data Visualization Theoretically/Critically:

Reading Nathan Yau’s book Visualize This

179-188 Visualizing Relationships

Three Takeaways:

  • Remember, correlation is not causation, but correlation should spur “deeper more exploratory analysis” p. 180
  • Remember, correlation is on a scatter plot goes up diagonal right versus negative, diagonal left.  Points that not follow pattern = no correlation p 182
  • Increasing effectiveness of scatterplots with making the dots pop on a background, reduce borders, and a visible curve. p 187

Day 90 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.
 See post explaining how and why I’m doing this.
Halfway point.
Code Learning:

Treehouse Back-to-Basics (again, not taking notes for basic review):

Going through Front-End WebDev

Reading and Learning Data Visualization Theoretically/Critically:

Reading Nathan Yau’s book Visualize This

157-178 Visualizing Proportions

Three Takeaways:

  • Stacked continuous charts are a good way of showing changes in categories over given time p 162
  • If a stacked area graph makes it hard to see trends per group, use a straight-up time series. p 177
  • “Only have a few values? The pie chart might be your best bet.  Use donut charts with care.  If you have several values and several categories, consider the stacked bar chart instead of multiple pie charts.  If you’re looking for patterns over time, look to your friend the stacked area chart or go for the classic time series.  With these steady foundations, your proportions will be good to go.” p. 178

Day 89 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.  See post explaining how and why I’m doing this.
Code Learning:

Treehouse Back-to-Basics (again, not taking notes for basic review):

Going through Front-End WebDev

Reading and Learning Data Visualization Theoretically/Critically:

Reading Nathan Yau’s book Visualize This

127-157 Continuous Data and Visualizing Proportions

Three Takeaways:

  • Too many values for pie/donut charts are not good, but showing them in a series with limited values (<3) can be effective p 150
  • Protovis functions similar to D3 as it was a precursor, helps to think of D3 has creating a whole piece first in some ways, and then attaching each characteristic, eg margin, data, etc. p 156
  • Treemaps came from trying to figure out what was happening in a hard drive -> good for straight-up proportions but best when using hierarchal data p 157

Day 86 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.  See post explaining how and why I’m doing this.
Code Learning:

Treehouse Back-to-Basics (again, not taking notes for basic review):

Going through Front-End WebDev

Reading and Learning Data Visualization Theoretically/Critically:

Reading Nathan Yau’s book Visualize This

91-108 Visualizing Patterns Over Time

Three Takeaways:

  • Zoom in and out on detail to look for what you want and to provide context over time p. 93
  • Discrete = people who pasted test each year -> scores don’t change, test on a date.  Temperature = continuous, always changing overtime.  p 93
  • Charts that help you visualize discrete data over time include:
    • Bar graph, stacked bar charts, point charts p. 113

Day 85 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.  See post explaining how and why I’m doing this.

Reading and Learning Data Visualization Theoretically/Critically:

Reading Nathan Yau’s book Visualize This

80-90 Tools to Visualize Data Chapter

Three Takeaways:

  • Maps that are zoomable and able to be moved and panned easily are known as slippy maps – split into smaller images, or tiles.  Example of this is Google maps  p. 80
  • Mapping resources for data viz include: Google and Microsoft maps, ArcGIS, Modest Maps, Polymaps, GeoCommons, IndieMapper, Spatial Key R p. 86
  • Combining different software or solutions is a key to creating better data viz – as each has its own strengths and thresholds p 89