TL; DR - True to form, DataSeer’s widely-consumed dataviz 101 learning content and genuine helpfulness overcome all peripheral training issues.


Disclosure

  1. In January, DataSeer invited me to a complimentary seat in this training for this honest review.
  2. Two days before this training, I placed second in the DataSeer Grab Challenge 2017, a nationwide data visualization contest sponsored by DataSeer and Grab Philippines, and ran by the AIM Analytics Club.

You may be thinking I can’t possibly bite the hand that fed me. You may be thinking this review is sanitized. If so, please leave now.

Still there? Thanks for staying.


Without data, you’re just another person with an opinion.
— W. Edwards Deming

I WAS NERVOUSLY surprised when I reached the Data Storytelling for Business (March 02 and 03) training venue at St. Giles Hotel. Around forty seats in seven rows and two columns of tables arranged classroom-style indicated that this wouldn’t be the intimate 15-student student Predictive Analytics workshop that I took last year.

“Could I work on my laptop, handwrite notes, interact with my group, and snack—all at the same time—in a cramped table space?” I thought.

I didn’t want to participate in an eventual Mad Max scramble for power, so I strategically situated myself front and center, against personal habit, within charger’s reach of the most important power outlet in town: the instructor’s table.

I’d been a fan of DataSeer, and I was rooting for this workshop’s success.

 

USER-CENTRICITY. FOCUS on outcomes (meaningful learning and understanding).

There’s something very design-thinking with DataSeer’s framework for data storytelling:

  • Audience
  • Data
  • Visuals
  • Narrative

Why not apply the same framework in evaluating this course?

 

Audience

Pro: lots of people with various backgrounds to meet and learn from

Con: not as intimate as previous DataSeer classes

Thirty-eight strangers elbow-to-elbow, bumping into each other, spilling each other’s coffees, almost dropping one another’s laptops on the floor.

DataSeer founder and lead trainer Isaac Reyes (also the first person I saw on LinkedIn with the job title Data Scientist), said he’d rather use terms appropriate to the understanding of the audience at hand.

He’d rather ask, for instance, ‘How spread out are the heights of the students in class?’ than ‘What is the standard deviation of those heights?’

I mention this because I suspect the data storytelling background of this audience had a high standard deviation.

Data

Pro: lots of real-life datasets

Isaac displayed his academic tendencies through his well-researched concepts, examples, and references, complete with bibliographic citations and web links. (A bibliography of this course’s references would make a great, self-defeating piece of content marketing ‘curation.’)

In fact, DataSeer stayed so true to its sources that it used these sources’ original visuals, which sometimes resulted in a visual hodge-podge of slides. If you’re easily distracted or your focus tends to stray, you should pay extra close attention.

My design sensibilities (i.e. OC-ness) aside, DataSeer relied on its topnotch sample datasets. Topnotch because DataSeer made available to ‘students’ all the spreadsheets of sample data used during the course and because DataSeer used real-world, dirty datasets—missing values and all (that’s a good thing).

Using the following, roughly gathered data during the first two hours of the course:

Mins Elapsed

No. of Anecdotes

No. of Demos

No. of Challenges

No. of Audience Questions

No. of Slides Skipped

No. of Breaks

Remarks

10

1

0

0

0

30

3

0

0

0

50

1

0

Many

71

0

1

1

roast chicken sandwich

120

1

0

2

1

…we arrive at this visualization of the course’s audience engagement level:

DataSeer's Data Storytelling course becomes more variedly engaging as it progresses

And here are ‘visualizations’ of the engagement levels during the group challenge-filled afternoons, when Team Alright (Ferdie, Precious, John, Chei, Rachel, and I) won on both days (fine, we tied with another team on the second day):

 Jay Manahan: calorie non-counter, donut-chart slayer

Jay Manahan: calorie non-counter, donut-chart slayer

 I draw pictures using numbers. Among other things.

I draw pictures using numbers. Among other things.

 Without this quote at the back, you're just another t-shirt.

Without this quote at the back, you're just another t-shirt.

Visuals

Pro: chock full of references

Con: you could get lost if you don’t keep track

This course was so rich in well-cited content. But because of its many varied references, I sometimes lost my way in the diversity of the slides.

I thought some clues at the footers and titles/subtitles of the slides, indicating where we were in the outline, could help the audience stay on track.

Narrative

Pro: brilliant stories from dataviz greats

Whenever I conduct one-on-one or small-group mentoring on data presentation, I always look to my mentees’ own experiences for examples of ineffective charts, and to the web for beautifully effective ones. But I feel I shortchange my mentees whenever I show them other people’s data visualizations instead of my own.

But here was Isaac, showing dataviz examples from giants left and right. And doing so did not diminish his credibility one bit. That’s when it dawned on me: tipping your hat to dataviz greats is a sign not of weakness, but of confidence.

So Isaac rightfully paid tribute to his data visualization and storytelling influences: Hans Rosling, Edward Tufte, Cole Nussbaumer Knaflic, and Stephen Few, to name—literally—a Few.

And some of the other stories he told were:

  • Wrecking Miley Cyrus’ balls is a human-rights violation.
  • Touching bars and touching gaps make rude histograms that have bins so fine. (You had to be there.)
 A rude histogram

A rude histogram


Without another person, you’re just bits of data with an opinion.
— Jay Manahan

WHEN YOU ASSIGN a score to something and make it a competition, magic happens. A class of strangers becomes a group of people.

Let’s evaluate the audience of this Data Storytelling course once again.

Audience (Post-Course)

Pro: lots of people with various backgrounds to meet and learn from

Con (if you can call it that): if you really want to, you can make this course as intimate as other DataSeer classes

Orlan, Ferdie, John, and Precious are in real estate. Chei manages application developers. Keinz is in the middle of a career change. Ian is an actuary. Jo-An used to work with VCs. Lance makes systems for retailers like Rachel. And St. Giles serves good biko (sticky rice cake).

Suddenly, the ‘standard deviation’ among the audience got smaller.

And I could see why this is Isaac’s favorite course to teach.

It may give you pause at the onset, but let it run its course and this Data Storytelling for Business class will be worthwhile.

 


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