Getting started in data

Continuing on the path I started the other day, here’s another blog post that I first wrote internally back in March of this year.

The question: How do you think you can encourage more women in data?

I think the toughest part of getting started in data is understanding the field and feeling confident working within it. It took me some time to realize that working with data is a mindset. It isn’t a functional area reserved for someone who has amazing coding or technical skills. It is for people who are detail oriented and want to understand the “why” behind things.

Finding the reason or purpose behind causes is one of my favorite components of working in data. Within this field, you get the opportunity to do that everyday. I am also extremely passionate about taking the data and creating a visual story with it.

If you’re interested in data or want to know if it is the right field for you, ask yourself if you like knowing the “why.” I’d also be curious if you are driven to mastery. Within the data field there are daily opportunities to learn and apply that newly found knowledge to your job. If that’s something you like doing, data and data analysis is the right place for you.

I’ve been in several analyst roles and they have meant different things. The technical skill set and applications have always varied widely, but there have been a few universal constants. Those constants are extensions of your personality – being a great problem solver, a critical thinker, and someone who can both reach solid conclusions AND (more importantly) communicate them. These few skills are what make people great analysts. The tools they use are just there to help them broadcast their findings.

If you’re on the fence, I would also encourage you to go find some data available in your daily life. There’s literally mountains of data out there. Find something that you’re interested in knowing more about and analyze it. It is a great exercise to start thinking like a data analyst and I guarantee you that you’ll find something new and interesting.

Once you’ve analyzed one data set, find something totally different. See what commonalities in the structuring of the data you can find. After having received hundreds of different data sets, I can tell you there are several commonalities. The more you practice with any set of data, the better you’re going to get at overall data analysis.

My last comment would be to remember there’s never one right answer in data analysis. It is a creative and interpretive field. Your interpretations should be based on the truth of the data, but realize once you’re in the analysis driver’s seat it’s up to you to showcase what’s important.

Quick and easy – parameters to aggregate dates


(Now with video – video uses different data as an example)

One of my favorite uses of parameters is to dynamically change date aggregations without the pesky drill symbol.  Sometimes I want to just see week or month, quarters tend to be pretty worthless.  Especially if I’m doing something discrete, I really don’t like the way Tableau breaks apart the data view.

The creation of the parameter leaves me with a very clean user experience and ensures users don’t negatively interact with the viz.

Here’s the setup – first create a parameter of data type string and type in your allowed values.  Mine is going to have week and month:

parameter1

(note I changed the casing on the words later on for look/feel)

Next create custom dates based on your primary date field.  Mine is called “Start”:

dates

Right click on the field, go to Create -> Custom date…

For the sake of this viz, I made both a Months and Weeks (both are date value).  At one point I had days (shown as “Exact”, but I abandoned it)

Last step is create the calculated field.  Right click on the parameter at the bottom, create calculated field:

dates2

Place it on your designated shelf.  You may have to do some wrangling on the field to get it to display right.  For the viz I ended up with, the field is set to “Exact Date” and “Discrete.”

Check out the final experience below:

Data Analysis – First Steps

Earlier in the year (March), I participated in a panel discussion for a data+women series at the Phoenix TUG.  As prep I was given a list of questions and asked to pick a few I felt comfortable answering.

To facilitate the sharing of my experiences, I wrote a blog post on some of the more intriguing questions.  What comes next is a blog post I originally published within my company’s social media site on 3/23/16.

What is your first step of investigation when you are given a dataset?

So before investigating a data set, I think it is critical to understand what the data output contains. I think it is important to meet with the business owners or subject matter experts that are responsible for creating, maintaining, or using the data. This helps to ensure you understand what they are looking for and what the different fields mean.

Assuming the pre-work has been done with business owners, I always approach a new dataset the same way. There’s three things I do every time.

The first is to find a categorical dimension that seems to be at the highest level. Typically I’m looking for a category that has less than 20 members (10 to 15 being ideal). This data can set a great foundation to understanding the overall population of records. Once I’ve found that categorical dimension, I immediately create a Pareto chart. Recall that a Pareto chart is a bar chart where the bar volumes are ranked from greatest to smallest. Additionally it has a line chart on the secondary axis displaying the cumulative percentage. The goal of the tool is to see immediately where most (or a majority) of problems are. And it usually follows the 80/20 rule logic.

The next thing I do is plot records over time, usually by month, sometimes by week. This is great for understanding any seasonality and for finding immediate trends. Often systems are changing and new processes are implemented. By plotting records over time, you can immediately take any funny data points back to your business owner and ask for clarity.

The more clarity you have around your dataset, the more successful you’re going to be at understanding the information you can retrieve from it.

The last thing I do is take that categorical dimension and plot each one against time. This really is the combination of the first two to a large extent. From this you’ll be able to further pinpoint any trends that are category specific and find even more potential seasonality or process changes.

 

By creating the three visuals I mentioned above, I usually feel pretty confident about the “vitals” of the dataset. I get a great sense of being able to communicate in the same language and accurately reference volume. I’ve found that this is a great first step when working with almost any set of data. It makes an excellent conversation starter with business owners and also proves itself as a useful communication tool when bringing new team members into a project.

That’s my initial analysis workflow, what’s yours?

#IronViz Entry – Mobile Design

Part of being involved in the Tableau community means publicly publishing visualizations to learn and grow.  It’s also a great way to find inspiration.

As I’ve pushed myself to be more active within the local Phoenix Tableau community and social (Twitter) community, I knew it was time to “step up” and make an Iron Viz.

My design aesthetic tends to be minimal, slightly formal, and geared toward (in my mind) elegance.  I like to make dashboards and visualizations that highlight the data, but don’t jump to many conclusions.  I’m very conclusion agnostic, so leading people too far down a path doesn’t always seem right.

All that being said – I wanted to make an Iron Viz entry, but it needed to be simple.  The deadline is September 18 (today as I’m writing this).  So I wanted to develop something relatively straightforward that got to the heart of mobile design.

The data inspiration for the viz actually came from an animated bar chart .gif showing the “Top 10 Economies” growth from 1969 to 2030 that I saw on Twitter.  I thought it would make a nice bump chart or slope chart, and the conclusion of the data was already compelling.

First the data gathering process – relatively simple on this one.  The .gif referenced the source, a quick Google search led me to the results.  I’m going to loosely promise to publish the excel file at some point.

Next up was diving right in.  I recently made a “micro viz” in Tableau 10, designing it exclusively for a very tiny space.  I actually didn’t use the device designer for this one, instead opting to develop the whole thing with my intended sizing.  With the sizing set, development was similar to what I’ve done in the past in v9.3 (version of Tableau I use at my job).

Transitioning to device specific design was different than I thought.  Since I knew the final product (in my mind) would need to have more emphasis placed on the mobile view.  It is after all a mobile design challenge!

Like I mentioned above, I had a pretty good idea of what I wanted the final viz to look like.  I knew there needed to be a bump chart and I was going to call attention to China and India.  What I didn’t realize is that the device designer is really geared toward creating a “master view” and then augmenting that master for the device.  This makes sense to me as I rethink the way the feature was presented.

What this meant for the creative process?  I wasn’t able to make visualizations (sheets) and quickly drop them on the mobile layout.  For each new “potential viz” I had to first drop it on the overall dashboard and then bring it on to the mobile specific dash.  It made the whole process kind of clunky.

I also struggled a bit with getting to formatting features quickly.  I can’t double click on titles to adjust font sizes in device preview, gotta go back to the default layout.  I’ll have to adjust my thought process next time and really think about starting from the default view and optimizing a mobile version.

I probably cheated the viz out of more depth that could have been added if I had truly started with the default dashboard and then made a mobile design.  It is fair to say that the default dashboard has real estate for more data insight and data depth.  I am still very pleased with the overall final results.

 

Blog post ideas

Ann

  • How Apple Watch has changed my life
  • How tracking calories has changed my life
  • Why is my life always changing (har har)
  • New books
  • Walk through improvements on micro viz
  • Revamp D+W posts
  • Tableau – continuous fields from discrete values for heat maps

Josh

  • Starting a home automation system
  • Hobby photography
  • Stretch project  – IFTTT our data
  • FLIR

Low carb brownies

One of the biggest struggles of a low carbohydrate lifestyle is sweets/desserts.  At least in my mind, sweet is synonymous with carbohydrates.  The easiest way to remedy this is typically by using artificial sweeteners.  But those sweeteners alone don’t usually satisfy food that is traditionally associated with dessert.

Enter eggs into the equation.  Eggs are surprisingly versatile and can actually mimic the structure of things we would typically classify as “bready” or carbs.

So here it is – arguably my favorite recipe of all time.  I eat these for breakfast and feel absolutely no guilt (because they’re essentially cooked eggs mixed with cocoa powder).

Ingredient List

  • Unsalted butter (160 grams)
  • 6 large eggs
  • Cream cheese (120 grams)
  • Cacao powder (60 grams) – can also use cocoa powder
  • Vanilla extract (2 tsp)
  • Baking powder (1/2 tsp)
  • Salt (1/2 tsp)
  • Liquid sucralose (1/2 tsp, measured out as 60 drops) – this is equivalent to Splenda

Oven Prep: 350 degrees F

General Instructions

  1. Melt butter and cream cheese in microwave, 1 minute on soften setting.
  2. Mix eggs, sweetener, butter, and cream cheese in blender.
  3. Add in all powder ingredients and mix thoroughly.  (When doing this, I say that I’m “buzzing” the mix.  Mix very well.)
  4. Line baking dish with parchment paper.  I use an 11 x 13 glass Pyrex dish.
  5. Bake at 350 degrees F for 30 minutes.
  6. Once done, remove from baking dish and cool on cooling rack.
  7. Reuse baking dish as storage vessel.

The resulting dish is amazing.  In my mind it is exactly like brownies.  The mixture of eggs with the leavening of the baking powder perfectly mimics the cake-y, slightly heavy texture you’d expect with a brownie.  And the cacao powder (and likely the butter) add a level of decadence that ensures you won’t over indulge.

The home automation mindset

Home automation is something that I have been following for several years.  I think we all have seen automation on TV.  Bill Gates’ house with music and pictures that follow you around in the 90s.  For me, it is about getting to Star Trek level of voice control with automation that does what I want with minimal interaction.  Getting to that ideal state is one of the most thought intensive projects I have taken on.  To use a meme, one does not simply automate a house. It requires a mindset.

The first thing to keep in mind is that the home automation landscape is like any other technology ecosystem but even more complex.  The one thing that makes it more complex than gaming, or photography is that home automation is so open.  I know that I can ignore news on PlayStation because I don’t have that console and I know I won’t ever be able to use it.  I also know that gaming has a limited scope that won’t impact much outside of gaming.  With automation, anything can be made compatible with almost anything and it affects my entire life.  For example, I’m thinking about getting a wi-fi souve machine.  How well does that work with SmartThings or any of the other platforms I have?  If my fridge broke tomorrow, I would have to consider automation in my replacement decision.

This openness is great because it means that I can do anything I put my mind, and time, to.  Literally, I could write an app that uses the accelerometer in my iPhone to change light colors.  I could then paint with all the colors that Hue can produce, while I dance around the living room.  I don’t have the time to do that, but that is really all that is stopping me.  Thankfully, others have used their time productively to produce tools that are more useful. But, of course, that means searching and testing the work of other hobbyists.

With the help of talented coders around the world, I can get better and more effective automation.  But that brings up the next thing to keep in mind: what do I even want to happen?  When I come home, what lights should be on?  Should it be the same lights during the day and during the night?  When does “night” start inside a house?  Essentially, I am always trying to determine what I usually do with lights and fans and temperature, and thinking about how I can communicate that to the automation systems.

All of that is the basis for the mindset needed to do home automation.  It is always on my mind.  If I’m out, I am thinking about what the house should be doing.  Should I communicate my distance to home?  What would I want the house to do with that?  When I am home, I think about what could be better.  What lights are working? Should the house be adjusting to my actions? And what would those adjustments look like? While thinking about this stuff, I also have to keep in mind how much time any solution would take, is the problem already solved, and if it is worth it.

The worst part of all of this is that the biggest hurdle faced in home automation is getting past the automation vendors themselves.  They want to take this complex and involved mind set and make it one button.  That would be fine, but nobody is that good at it yet, and the least good are the people that make the products.

So why even bother? Because of the way it feels when it works.  The feeling of a giant complex machine that culminates in a fan turning off. It’s basically magic, with magical tools, and me as the magician.

Low carb turkey veggie deluxe pizza

One thing that Josh and I like to do on a regular basis is comb through recipes of popular foods with a low carbohydrate slant.  Especially when I’m stressed out the idea of “comfort food” is really appealing.  This has lead to our newest creation – a low carbohydrate, low(ish) calorie, veggie heavy pizza.  One that makes you feel immediately comforted.

Ingredients List

  • mozzarella cheese (84 grams)
  • almond flour (84 grams)
  • 1 large egg
  • cream cheese (28 grams)
  • tomato sauce (61 grams)
  • turkey pepperoni (60 grams)
  • pepperoncinis (60 grams)
  • red onion (1/3 cup)
  • bell peppers (1/3 cup)
  • black olives (14 grams)
  • jalapenos (28 grams)

The key to this recipe is really in the crust.  The toppings are really mix and match to your liking.  Above is what I like to put on the pizza.  And in all honesty, it is probably too much turkey pepperoni.  I use a calorie counting app to track my daily food.  Since I’m particular about the data, I have been too lazy to reduce the volume of pepperonis and adjust the total calorie content.

Oven Prep: 400 degrees F

Crust Instructions:

  1. Mix 28 grams of mozzarella cheese (remainder is for topping) with cream cheese.  Melt in microwave.  We typically set the microwave to soften for 1 minute, stopping at 30 seconds and stirring.
  2. Add in almond flour and egg to cheese mixture.  We use a plastic mixing spoon to prevent the crust mixture from sticking.
  3. Once fully mixed, place crust mixture on parchment paper.  Add another piece of parchment to the top and roll out with a rolling pin.
  4. When desired crust thickness/diameter is reached, remove top parchment.  Pierce crust with fork.
  5. Bake the crust for 7 minutes at 400 degrees F.  At 7 minutes flip the crust over and bake for an additional 7 to 8 minutes.
  6. Remove crust and start placing toppings.
  7. Bake an additional 15 minutes at 350-400 degrees F.  If there are tons of veggies, consider a lower temperature and then broiling to crisp up the pepperonis at the very end.

The math on my recipe clocks in right at 1100 calories for the entire pie.  Easily goes the distance of 2 to 3 filling meals.