## VizWiz

Launch, grow, and unlock your career in data

# Notes from the Visual Business Intelligence Workshop: Day 3 – Now you see it

Day 3 of the Visual BI workshop was the day I was looking forward to the most.  I was very interested in hearing Stephen’s approach for analyzing data, which he covers in his book Now you see it.  There was one common theme throughout: keep things simple and clear, but don’t dumb it down.

Here are my key takeaways/notes:

• The word “see” in the title represents the analytical thought process: Search => Examine => Explain (SEE)
• Things to look for in a skilled data analyst: interested in the data, curious, self-motivated, imaginative, open minded and flexible, skeptical, honest, has a sense of what’s worthwhile, attentive, methodical, analytical, synthetical, has an eye for patterns, knowledge of the data, knowledge of effective data analysis practices
• The context we perceive is influenced by the surroundings.
• Exceptions can be a result of:
1. Erroneous data
2. Extraordinary events
3. Extraordinary entities
4. Randomness
• Highlight exceptions that are out of the range of “normal” or “standard”
• Always ask “Compared to what?”
• The tools we use need to make common interactions easy.  The tools should allow the train of thought to continue.
• Cycle plots are useful for cyclical and linear patterns.
• Linear trend lines on time series can be misleading; use with caution!  Consider moving averages as an alternative.
• Log scales are useful for measuring rates of change.  Lines with similar slopes will have similar rates of change.
• When looking for leading and lagging indicators, it can be useful to shift the time of one of the indicators.
• Bump charts are a good way to see how rankings change across different dimensions or measures.  Learn how to build one in Tableau here.
• The mean represents the quantitative center and is highly influenced by outliers.  If you want to look at dispersion around the mean, use standard deviation.
• The median represents the ordinal center and is better than the mean for showing the “typical” value.  If you want to look at dispersion around the median, use percentiles.
• It’s a good to idea to start an analysis by looking at a distribution of all values.  This will help you quickly identify outliers and the overall shape of the data.
• You shouldn’t remove outliers from an analysis until you understand why they are outliers.
• This is a really cool analysis of pay ranges by level and gender.  You could easily include a strip plot on this.

That’s it!  Three days of learning that I’ll never forget.  These courses were easily worth the money.  You’ll be able to apply so much immediately upon returning to your regular job.