Critique a.k.a. “the crit”

STEP 1: Pair up

STEP 2: Show 2 of your visuals (2 that show the same data in different ways) As the viewer, first simply describe what you’re seeing/learning

STEP 2: Show 2 of your visuals (2 that show the same data in different ways) As the viewer, first simply describe what you’re seeing/learning ….then talk about which you like better and why

What it’s like: Helpful Frustrating why can’t they see it?! darn, I thought it was good

STEP 3: Show the visual you’re emulating Describe what you see as similar/different

STEP 4: Switch… STEP 5: Then look at your second set of visuals STEP 6: Then switch partners

Add a description of the feedback you got to your portfolio

http://flowingdata.com/2010/04/29/visualizing-data-ask-a-question-first/

We’ve experienced some of this process… In Project #1 I gave you a very structured process – explore your topic, explore your data, see what others have done Here’s what one professional designer had to say:

http://visitmix.com/Articles/seven-and-a-half-steps-to-successful-infographics

Have an idea [get creative, look for inspiration]

2. Get data [it’s never as easy as it seems like it should be] Here's the chicken and egg part: before you can find data, you have to know a little about what you're looking for. And no, this doesn't mean just google your topic and read the wikipedia entry. Buck up. You have to approach your subject matter academically and do some research. If you can't face that, approach an academic. You’ll be doing this…it’s true

3. Tools, and brandishing them [There’s no such thing as the perfect tool, it’s probably not possible/worthwhile keeping up on everything – a good strategy is to get good at something] Some people work in Excel. Some people work in R. Some of you love your Illustrator to death. Processing, ManyEyes, Swivel, Tableau—it's all good. None of them are perfect, of course. But which is the best? I'll let you in on a trade secret: there's only one program you need to know if you're serious about infographics. This is crucial, so listen up and you too can be a data viz star: Not! We’re learning tools that I’ve found to be useful

4. Scrub your data [data is generally messy and you’ll have to spend time cleaning it up] Because working with data is truly unpredictable, I've developed a form of selective amnesia that allows me to cope. Every time I start a graphic, I'm optimistic that my material will show up perfectly packaged with a lovely bow and some chocolate covered pretzels in a cellophane bag. Instead, it usually arrives like the black sheep cousin at a family wedding—late, disheveled and smelling like something stuck to the bottom of a cat's feet. Before you can visualize anything, you've got to make sure your material is clean, clean, clean and super-organized. If you don't have an obsessive-compulsive personality now is a good time to develop one. Expect to wrestle a bit…but we don’t have to go for perfection here

5. Get Freudian [spend time with the data until you ‘get it’, until you see something interesting worth conveying] If I stare at my spreadsheet (or table or daunting stack of white papers) for a while, I start to get it. I read it in small bits and go forward and backward randomly until something clicks. Once you've got your head wrapped around what you're sitting on (I know, that's not physically possible), you can choose a charting form. This is about exploration

6. Let's play [Look around for inspiration – you don’t need to think it up yourself, no one does] I used to think ideas were supposed to just pop out of your head, or that good artists should be able to draw anything. ("A wheat thresher? Why of course, Bob, it looks something like this...") Then I learned that even my husband, a gifted illustrator, uses reference to spark ideas. EMULATION – I didn’t just make it up

7. Edit thyself [Keep it focused and simple] Frame your idea clearly. Your primary point should be clear and supported by context and detail. The main art should draw us in. Sidebars should be well-focused. Don't spam us with too much information; nobody has time these days.

½. Respect the asterisk [Be seriously detail-oriented] Working with data requires a certain degree of rigor. Just does.* Make sure you know what the asterisk is connected to and suffer through the small type. There's gold in there.