Would You Invest One Hour to Save 10?
The benefits of developing skills in analytics are numerous.
Recently, Mirabai Auer and Xiaohong Zhang provided examples of boundary-pushing tactics to help identify new prospects, while Renée Teate showed how analytics can extend familiar work. Analytics automates repetitive tasks and streamlines processes, providing us with more time to creatively discover and communicate actionable insights. So, are we willing to invest some time now to save much more later?
What Can You Really Learn in One Hour?
Most learning follows the pattern in the image below. [Source: The Learning Curve Equation, by Louis Leon Thurstone]
At first, it may take much more than an hour to begin to see meaningful results. This “at first” is the point at which many give up. Although we know that sticking with it is worthwhile, in the early stages it can be difficult to stay motivated. Luckily, there are some tricks to help push through this initial portion of the learning curve.
Develop a Bias Toward Action
When learning a new skill within analytics, you will retain much more when you actually use your new knowledge. It can be tempting to want to start with reading about your chosen learning topic, and books are certainly important for getting a high-level understanding and for reference. But deliberate practice is critical at this stage. Look for books with examples, questions or challenges. And in addition to books, blogs, interactive tutorials and online classes are excellent sources of educational content when you are just getting started.
When you play and observe while working toward a goal, you will progress faster and have more fun in the process.
Make Learning Relevant
There are an increasing number of quality resources for learning analytics within the context of fundraising. These include “Data Science for Fundraising: Build Data-Driven Solutions Using R” by Ashutosh Nandeshwar and Rodger Devine, and “Cool Data: A how-to guide for predictive modeling for higher education advancement and nonprofits using multiple linear regression in Data Desk” by Kevin MacDonell.
However, the vast majority of examples and tutorials use data from different domains. These resources can still be valuable to us; we just need to employ a little creativity and transpose them so they apply to our work.
There are some data sets that are commonly used for many R tutorials. One of these data sets is called “mtcars,” which provides some basic statistics about different types of cars. At first, we might look at an image like the one above and wonder how this data about cars can possibly be relevant to our fundraising work. However, with a few subtle changes, we arrive at the plot below:
When we learn to see in this way, we can more easily substitute the values we find in tutorials with more familiar data. This is not only a road to quick wins, but also a way to increase comprehension by framing new concepts within a known environment.
Learn With Others
You don’t have to learn alone; there are a number of opportunities for joining data analytics-focused communities and gatherings.
Conferences, for example, provide a way to quickly collect new ideas and build our network. This year, Apra has applied feedback from past Data Analytics Symposium (DAS) events to provide even more opportunities to achieve these ends.
- DAS attendees will have access to a community hosted by Apra to network, share resources and ask questions ahead of the conference.
- DAS will feature lunch networking groups organized by topic so attendees can meet others with similar goals, as well as those who already have knowledge in a given area.
- “Office hours” will also be provided with select presenters to discuss specific questions with subject experts.
Learn more about this year's DAS here.
In addition to DAS, there are analytics-focused communities that can help you learn year-round. One of the best examples was started by DAS presenter Jesse Mostipak. If you are hoping to learn R, check out the R for Data Science learning community. Kaggle is another platform for connecting with other learners using R and Python. In addition to online communities, search in your area for Meetup groups. These will vary by area, but may include user groups for Tableau and other resources in addition to R and Python.
Back to the Curve
As you work through the first grueling section of the learning curve, try to utilize one or all of the steps above. Notice as you begin to understand previously unknown concepts and are able to complete certain tasks faster. While there will be frustrations, this is a time to focus on all the positive gains you have made while remembering that your learning rate will only continue to accelerate as you progress.
What about the plateau after the climb? Is this like going back to the start? It is not.
Imagine the dots below the curve represent a subject matter unit or group, such as visualizations. Learning to make your first plot was the hard part. Now, as you tweak and modify to make a given image interactive or animated, all the fundamentals and syntax are still in use. While the gains may not be as transformative, they are achieved at a faster pace and built on an already powerful base.
In addition, you now know the best sources for finding quality information and are possibly part of a community that is ready and willing to help. As a result of the strong foundation you have put in place through an ongoing commitment to practice and the learning process, you can now learn more advanced skills quickly. This is when you can begin to spend one hour to save 10.
Michael Pawlus is currently a data scientist at University of Southern California. Prior to this, he was the director of prospect development at The Trust for Public Land, and before that the assistant director of prospect research and development at Grand Valley State University.
Michael is chair of the 2018 Data Analytics Symposium planning committee. He has volunteered with Apra in a number of other capacities throughout his time in this profession, most recently as one of the faculty members for the 2017 Apra OverDRIVE/ conference. He also served as programming chair on the APRA-Michigan Board and as a member of the first ARC planning committee.
Michael has a master’s degree in librarianship from the University of Sheffield and a bachelor’s degree from Grand Valley State University. He lives in Greater Los Angeles with his wife, two kids and an orange tabby cat from Korea.
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