Data Scientists: The Next Generation

As I’m sure you all have noticed, the data business is booming right now. (Are you tired of the term “big data” yet?) The fact that 90% of the data in world today has been created in the last two years is a great example of the growth trajectory of data. All of this data provides new opportunities for discovery for those who are willing to analyze it. Enter the data scientist.

 “Data Scientist” isn’t even listed as a career by the US Government’s Bureau of Labor Statistics yet, but it’s already been named the sexiest job of the 21^st century by Harvard Business Review. With a growth pattern similar to that of data itself, it’s safe to say that data scientists are going to be in high demand. Among other skills, being a practitioner of data science requires analytical thinking, mathematical/statistical ability, a knack for communicating results to non-data people, and creativity. This combination of business acumen and technical skill isn’t easy to come by, and new graduate programs with an emphasis on data science seem to be cropping up daily to fill the gaps. One article from the New York Times recently asserted that the United States will need to increase the number of graduates with data science skills by as much as 60% to keep up with demand.  So, when you’re looking for new data scientists, where do you turn? To a generation who’s grown up with data science all around them – through Netflix recommendations, Google search results, and even at the movie theater à la Moneyball.

I was recently asked to participate in a “Job Hop Day” for a local elementary school. The idea was to expose 4-6 graders to different jobs that are available in the Mount Washington Valley in NH. It was a good opportunity to spend a fund day with elementary school students while exposing them to world of data science (and the idea that people actually get paid for doing it!). In preparing for our session, I realized that as thrilling as an hour-long lecture on data science might be for some, 10-year-olds probably wouldn’t be so interested. After ruling out a product demo and a slideshow, my coworkers and I thought about other ways to engage them. We decided the best approach for them to learn about being a data scientist was to do it themselves (in the guise of a game). 

When creating the game, we thought about some of the skills we wanted to reinforce, which were things like data mining, basic math, and the ability to make predictions. From there, we got creative – we wanted to pick a subject that kids would be interested in, and since vampires are on the brink of cliché, we settled on werewolves. The game we came up with was a variation of a Family Feud board that involved an initial data-mining phase to glean the characteristics of a werewolf.

To start, I gave the kids ten descriptions of people on color-coded index cards, five of which were designated as “werewolves” and five of which were “non-werewolves”. (Coming up with the descriptions was a good exercise for us as well, we tried to make  sure the clues weren’t too obvious, and had to plan them so that some characteristics were more popular than others. An example: three of the werewolves were vacationing in London this summer, but all five of them played some kind of sport). Each data scientist had a whiteboard to write down their descriptions as they went, and we stopped the “data mining” portion of the game once they all felt like they had come up with as many characteristics as they could. The Family Feud board I mentioned earlier had the ten characteristics listed in order of the number of times they came up, and the kids took turns guessing what was on the board.

Over the course of the day, three groups of students played the game, and all three groups seemed to really enjoy it. After we finished the game, we talked about the different uses of data and predictive modeling, covering examples spanning test scores to baseball. They were knee-deep in baseball season and pretty excited when I told them about a baseball scout’s presentation I saw at DRIVE, and how they used statistics to predict what might happen in each game. It was evident from our conversations that the kids had some knowledge of the amount of data around them and were interested in examining the world from a data-driven viewpoint. (I should probably mention here that the kids who chose to attend our session knew it would be math-related, so our sample was a bit biased.) Most of them had never heard of a data scientist or a statistical analyst before, but they were interested in the type of thinking we’d done. A few days later, a student’s mom told me that her son “loved the game” and “was so excited that it was an actual job that he could shoot for”.

Overall, our ad hoc approach to the data scientist experience seemed to go over well, but there’s always room for improvement. I’m interested in any ideas or experiences you guys have might regarding young data scientists, and would love to hear about them in the comments below. In the meantime, if you’ve had a sneaking suspicion about a certain neighbor around a full moon, or just want to have a little fun, I’d recommend trying out your own version of the game. 

-Caitlin Garrett is a Statistical Analyst at Rapid Insight

Campaign Pyramids: Brick by Brick

Recently, I got to chat with Chelsea Drake and James Dye, who are both Data Analysts at the College of William & Mary, about the work they've been doing on campaign pyramids. For a more in-depth look at the functions that their campaign pyramids serve, and their process for building them, be sure to check out their presentation at our user conference or stay tuned for a webinar rebroadcast in July. 

What is a campaign pyramid’s function in your office?

CD: Right now we’re using the pyramids as a donor-centric list of prospects. To give some background on the pyramids, we did a massive data mining project to determine where our donors’ interests were. The end result is a dynamic pyramid that updates as new gifts come in and as we get new information about where their philanthropic interest lie. We use them as accurate prospect lists.

JD: We had a bunch of people in our prospect pool and needed to know where their interests were. For example, if they’re into Athletics but graduated from the Business school, do we want to go after a split gift, or do we say that their primary interest is athletics, so they should be doing the ask? The pyramids help us decide which one we should try to raise money for. They also help to set goals for each department and each school. So we’ll set a goal and ask a question like ‘how many gifts do we need at different levels, and prospects do we need to make up that pool and reach our goal?’

How do you set the goals for each pyramid?

CD: We’re able create pyramids to test high, medium, and low goals to see which one is most feasible for each unit and each campaign overall.

JD: Each unit has three pyramids – they have a high goal, say 120M if 100M is the medium or mid-range goal, and a low goal, which might be something like 80M. The mid-range goal should be something they can accomplish without too much effort and the low goal is what we think they’d get if they only asked people we already knew. This allows us to see how much stretch we need to do and how many people we need to identify in order to hit certain monetary goals. The idea behind the project was to figure out where our prospect pool’s interests were and where we need to do work and identify new prospects to fill in gaps and holes.

What triggered your interest in campaign pyramids?

CD: We started last summer, our Assistant VP of Operations wanted to make sure we were being as donor-centric as possible. She knew we had some information on interests but that we didn’t have a reporting tool that identified which prospects should go with each interest. She knew I had an analytical background and that’s how she chose to bring the project to me.

JD: Previous pyramids had been done at the university level. For the college, we wanted to know who we had out there and how much money that would bring in with specific gift ratings. But we were also asking things like ‘How much can we get for athletics?’ and ‘Who are the people who are interested in athletics?’. That’s where it spawned into a donor-centric thing. We wanted to know what our donors’ interests were, what they’ve given to in the past, and on a program and unit based levels, who were the donors for each area.

Who builds the pyramids in your office? How did you decide that?

CD: James and I do, and that was decided based on our backgrounds. James has a programming and computer science background and I have a background in research and analytics.

JD: We’re the programming and analytic people in our office and were already working on data pools, but were brought onto this project based on our skillset. Within our department, we’re the ones who generally work with the data.

What’s your administration’s take on the pyramids?

JD: They like them a lot. It gives them an idea of monetary goals for each unit and school to stretch for and concrete lists of names. We can show them the people we’ve identified, and if we sum up all of things we have in a pyramid, we can see if the goal set for a department is realistic. It helps them to see who’s out there and who’s in our database. They also use it to present to a board of visitors in a slideshow on where we stand in a campaign and how our numbers are at any given point. They can tell how many people we’ve already identified and how many new people we need to identify to meet a goal.

What advice would you have for someone looking to undertake a project like this?

CD: One of the things that was really helpful for us as the project started was having a good relationship with IT to fine tune what the data files we get from them would look like. The key to doing this type of analysis effectively is to have the best data that you’re able to get from your system in the most consistent way possible. Also, you should absolutely plan out what your goals are for the project before you get started.

JD: You have to know which data points out there you can pull from and what would be relevant for your goal. Depending on the size of the school, you might want to focus on a single unit pyramid to narrow down the scope of what you want to do. You could start with a major gifts or annual fund pyramid, for example. It’s about first defining your question, then looking at the data to figure out which people to target and looking at the numbers to establish what your monetary goals should be. It helps to nail out a template of what you want the end result to look like before you start programming.  We knew what we wanted our end result to be, so then when we were programming forward, the question became ‘how do I fill out these blanks where these numbers should be?’. This way, when you start building, you’re able to visualize how to compile everything correctly according to your template. Also make sure that you have a good team working on the project, and that team members know what their role in the project is.

CD: Anytime you’re taking on a project like this, you want to have the ability to talk to the managers or executives of your department to make sure that your end result matches what they feel they need.

JD: Make sure it’s helpful for them. We’re numbers people. We can make a page full of numbers and look at it and understand it, but management might need something a little bit more nice looking. So the sheet we create for them outputs to a single page with colors so that when we turn it over to them, the information is logical and easy to read. It comes down to knowing your audience. 

Bookworming

Looking for a book to read this summer? We're proud to present our second annual list of Rapid Insight staff-recommended books for your perusal:

Walden by Henry David Thoreau (Mike Laracy, CEO)

When I first read this I started highlighting all of the sentences and paragraphs that were brilliant. When I realized that I was highlighting mostly everything I put away my highlighter. 

Naked Statistics by Charles Wheelan (Jon MacMillan, Data Analyst)

This book focuses on statistical analysis including inference, correlation, and regression analysis. As boring as that sounds to some, Charles Wheelan does an amazing job of keeping the book engaging and interesting. 

Inferno by Dan Brown (John Paiva, Account Management Team)

For any lovers of Florence, Italy, this is a definite must-read. While not reaching the level of some of his previous groups, Inferno is definitely a page turner. It’s not a scientific read but there are some great questions posted by the characters that force you to consider the basic math behind humanities’ survival or demise. 

Train Your Mind, Change Your Brain by Sharon Begley (Jeff Fleischer, Director of Client Operations)

Though it sounds like a self-help book, it’s actually a non-fiction work for the layman describing recent discoveries in the field of neuroscience. The author spoke at a Behavioral Healthcare conference I attended a few years ago. I liked the talk so much, I bought the book! Fascinating, yet easy read. 

Poisson Un Poisson Deux Poisson Rouge Poisson Blue by Theodor Geisel (Scott Steesy, Chief Software Architect)

For some light mathematics.

To Sell is Human by Daniel H. Pink (Sheryl Kovalik, Director of Sales & Business Development – Higher Ed)

Being a big fan of sales, I was naturally excited to read a book that gave me excellent perspective on how access to information has changed the buyer/seller relationship; I also enjoyed Pink’s march through the history of sales and his advice on how to adapt for the new environment. But the best part of the book was the way in which the author helps one to see that all of us are sales folks in some way. If you’re in fundraising, admissions, technology, or service positions, there is something here for you – we’re all trying to sway others to our side!

The Signal and the Noise by Nate Silver (Caitlin Garrett, Statistical Analyst)

Named Amazon's #1 Best NonFiction Book for 2012, I'd say this book is proof positive that predictive analytics is going more mainstream. As a thought leader in the field, Silver's book is chalk full of examples of the practical applications of predictive modeling. A great read for the technical and non-technical alike.




Have you read anything good lately? We're always looking for recommendations - tell us about it in the comments. 

Using Predictive Modeling to Drive Fundraising Efforts

In preparation for their presentation at our upcoming User Conference, "Using Predictive Modeling to Focus your Fundraising Efforts", I got the chance to chat with Bridget Mendoza and Brianna Lowndes from the Whitney Museum of American Art. Bridget, the Director of Development Records, and Bri, Director of Membership and Annual Fund, have been working together for the past year and a half to bring predictive modeling in-house for the Whitney Museum. 

Here are their thoughts on building their skillsets, modeling challenges, and how the process is going so far:

Bridget Mendoza

What triggered your interest in predictive modeling for the Whitney Museum?

BM – We started by thinking about how to enhance our prospecting as we lead up to our new building. Our research team routinely identifies ‘hidden’ people with higher capacities giving at an entry levels. Anecdotally we compared these prospects to active upper level donors and started seeing patterns in some of their giving and membership histories. We’d previously completed a modeling exercise with an outside company, but the problem with outsourcing was that once we got the model we didn’t have ownership and couldn’t adjust it. We know our data better than anyone else, and when we looked at some of the underlying information, we wanted the ability to alter and refine the model. As our goals are ambitious, we needed to continually grow our prospect base and predictive modeling helps us create a solid foundation for doing so.

How did you decide internally who would take on the predictive modeling project?

Brianna Lowndes

BL - We formed a committee of about ten who were involved in conversations on what we hoped to get out of a predictive modeling software or service and what our goals would be. As conversations progressed and we decided on the Rapid Insights tools it made sense from a resource perspective to deploy Bridget and I, who already work closely with the data and provide different perspectives.  Being close to the exports and metrics and being aware of nuances in member lifecycles has played really nicely into the work we’re doing in predictive modeling. The larger group meets quarterly and that cross-departmental approach helps keep us on track and engaged with the bigger picture.

How did you build your predictive modeling skillset?

BM – We started by attending conferences like MARC and the Rapid Insight modeling course at Brown. Once we made the decision to work with Rapid Insight we had the opportunity to work closely with their team and to become more familiar with their software and with basic modeling practices.  Bri and I also took a Business Statistics for Management class as a refresher.

What modeling challenges have you found that are unique to a museum?

BM –Museums are not as far along in leveraging predictive modeling as our Higher Education counterparts.  While attending the RI User Conference, we heard a really interesting presentation about student retention which sparked our thinking on how to apply what they’ve done to the museum setting.  Like many museum membership programs, our acquisitions in a given year are connected to the exhibition schedule. These cyclical patterns make it more complicated to isolate the data around the health of the program. We are excited to leverage predictive modeling tools to better understand those trends. 

Do you have any advice for non-profits who are thinking about predictive modeling in-house?

BM – There’s a learning curve, but don’t let that discourage you. That’s what a lot of our webinar will be about. Even though we haven’t been modeling for five or ten years, there’s a lot that that can be accomplished in that first year especially with the help from a partner like Rapid Insight.

BL – It’s important to have senior leadership support and take a cross-departmental approach. This ensures we are always thinking about the larger institutional needs. I’d also say that taking the stats class was helpful for us. The software does a lot of the heavy lifting for you so it’s important to get up to speed so that you feel like you’re engaging critically and asking good questions. 

BM – Having a vendor who had built this type of model before and had reliable expertise in the both the non-profit and for-profit fields has been really helpful for us. It was good to be able to collaborate with our software’s support team to build up our own knowledge of data prep and predictive modeling. Rapid Insight has been a real partner through this first year of modeling and we are excited to continue and expand this great work.   

**

If you're interested in hearing more about how to use predictive modeling to focus your fundraising efforts, Bridget and Bri are presenting at our upcoming User Conference. For more information, or to register, click here. Both users and non-users are welcome to attend.

If you have a tip you'd like to share on using predictive models to drive your fundraising efforts, please leave it as a comment below :)

Tips for Charting in Veera

Being able to collect and identify valuable data is important for making sound decisions to meet long-term goals, but collection and identification are only part of the process. Just as important is the ability to provide visual representations to transform your raw data into actionable information. This guide will help to create basic graphs in Veera and to improve them by making them more clear and eye-catching.

First and foremost, before you get started, you’ll need to identify what data you’d like to represent in your chart. You may need to whittle down your data so that it isn’t overwhelming but still fairly represents your population. For this purpose, you might consider using a Filter node to filter down to just the entries you’re truly interested in, or using a Cleanse node to create an “other” category to concatenate some of the smaller categories.

Once you have the dataset you’d like to use and have opened the chart node, your first step will be to select the chart type (pie chart, bar chart, etc.). Once you’ve done that, you’ll want to fill in all necessary fields on the right side of the Chart Data window, such as deciding which variables should be your x and y axes. Once everything has been labeled, you can edit the look of your chart by clicking on the colored icon in the top right of the window. 

As you begin to navigate through the Chart Style Editor window, here are some things to keep in mind:

1. The chart type does not automatically identify the chart type that was selected in the previous window – you’ll want to update this and select the appropriate chart type.

2. The sample chart you see on the right will update to reflect any changes you make in the chart editor. Use it to evaluate your aesthetic decisions as you go.

3. Below the sample chart you can choose whether or not to include data labels and adjust the font, size, style, color, and chart background.

4. When charting binary or categorical variables, you may not see a need to include a legend. If you select the ‘Legend’ tab and uncheck the ‘Display Legend’ box, you can remove the legend. If you do remove the legend, be sure the chart is labeled accurately in the first window.

5. If you’d like to make any of your charts 3D, you can do so by going to the ‘3D’ tab and checking the ‘Display in 3D’ box. There you can also change the inclination, depth, rotation, etc.

6. Once you’ve edited the background color and text color of your chart in (below the sample chart), you can choose a color palette for the rest of the chart by going to the ‘Color Palette’ tab. There you can select a pre-programmed palette or create your own custom palette.

7. One of the more subtle functions in the chart node is the ability to shade ranges within a chart – found in the ‘Axis’ tab. Here you can choose to assign colors to ranges within a chart to better visualize certain areas of interest in your data. This is especially nice when looking at retention rates or variables represented as percentages because the ranges are easy to define & interpret.

8. Another use of the ‘Axis’ tab is to help label your data. Too many times I’ve gone to chart my data but found that half my data points are unlabeled in my graph. Here, choose the axis that should contain those missing labels and unselect the ‘Auto Label’ box. Now under the ‘Major Tick Marks’ heading, unselect the ‘Auto Interval’ box and set the interval to be 1 to insert all missing data point labels. It should look something like this (note that I’ve chosen the y-axis):

9. Now that you know how to edit each chart individually, you might find that you like a particular style and want to save it to use later. You can do this within the Chart Node by clicking on the ‘…’ button next to the ‘Chart Style’ drop-down. Here, click the green plus button to add a new chart style – give it a name and you’ll find yourself in the ‘Chart Style Editor’ window. Once you’ve finished editing, you’ll be able to access your saved chart style within the Chart Node from the Chart Style drop-down menu.

10. Once you’ve created a chart type that you love, you can also export it to the Collaborative Cloud so that others can use it – if you have a minute, check out the cloud and search for chart styles for the opportunity to download styles that other users have created.

Do you have any tips for charting, or questions about how to use the charting node? Leave them in the comments below :)

Happy charting!

-by Jon MacMillan, Data Analyst at Rapid Insight