Big data and data science are hot topics everywhere these days, and the social good sector is no exception. As nonprofit organizations continue to increase their use of data to answer questions about donors and fundraising and drive performance improvements, it’s important to understand as much as possible about data science.
In today’s episode, I’ll speak to Carrie Cobb, Vice President of Data Science at Blackbaud. We’ll do a deep dive into the subject of data science. Listen to the episode to hear what Carrie has to say about the specifics of what a data scientist does, the techniques they use, and the variety of ways that data science is applicable in the social good community.
Topics Discussed in This Episode:
- The differences between what a data scientist does compared to other types of scientists who deal with data, like statisticians
- How data can help reveal answers to questions about why something does or doesn’t happen
- The background and education common to data scientists
- The techniques used by data scientists to try to find answers from a large amount of data
- Whether it's helpful to separate what data scientists are trying to do from how they’re trying to do it
- How data scientist deal with eliminating bias, error, and unknown information
- What happens when the answer the data shows is disappointing
- How data science is applied in the social good community for reasons beyond fundraising
- The frequency with which predictive modeling should be done
- Where data science trends in nonprofit organizations are headed over the next few years
“When you’re a data scientist you kind of dive into the unknown to find patterns and build connections and make predictions.”
“From a technical perspective, data scientists are highly educated. Almost 90% have at least a master’s degree, and almost 50% have Ph.D.’s.”
“I would say it is an art and a science put together. Depending on your paint and your canvas and what you’re trying to display, you’re going to choose different tools to get you there.”