Like most of us in prospect development, Kenny Tavares is a life-long learner, but I’ve always thought that Kenny is more next-level than the next guy. I’m always interested to hear what he’s learning about, because I know it’s going to be something I should be paying attention to. This week, Kenny shares his journey of self-teaching and learning, and provides tips, tricks, and actual resources for anyone interested in following his path of discovery. ~Helen
When data science started to take off in our profession, I was one of many curious researchers who wanted to understand how we could optimize our work. I would attend conference sessions hoping that the facilitator would sprinkle their special magic on me, so I could go back ready to add these tools to my collection. However, I always found myself struggling to keep up with each idea and, eventually, the speaker’s voice would transform into the muted trombone we all remember from the Peanuts specials of our childhood.
It’s silly to think that one could master data science, a subject for which there are degree programs, over the course of a handful of sessions. However, we are all capable of learning it or any other subject. After all, we’re prospect researchers. We have arrived at this profession from a wide range of educational backgrounds. Still, the further we get from our college experience and the more entrenched we get into our day-to-day activities, the less able we feel to embrace self-learning. So, how do we take control of our own education, acquire new skills and knowledge, and adapt to change?
Design your learning environment
When I started learning about data science, I assumed nothing and started with the most introductory view possible. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel is great primer on how our lives are already affected by data science, while Predictive Analytics for Dummies by Dr. Anasse Bari, Mohamed Chaouchi and Tommy Jung delves further into techniques.
When I was ready to start coding, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund provided a structured, hands-on overview of R programming, while Data Science for Fundraisers: Build Data-Driven Solutions Using R by Ashustosh Nandeshwar and Rodger Devine offered tutorials in the language of fundraising, an important bridge for applying the concepts I had learned.
I know what you’re thinking: Do I have time to read all these books? The answer depends on your preferences. Take stock of your past learning experience and create a personalized environment as you proceed. These preferences could include finding the right materials (books, videos, online courses), choosing an ideal time and location, or finding a supportive community (study group, online forums). Stack Overflow is a great online forum with knowledgeable coders. There’s a good chance that most of your coding questions have already been answered there. These strategies will help optimize the learning process and will reduce frustration.
Engage in active learning practices
The books R for Data Science and Data Science for Fundraisers were so important for me because of the endless number of examples included, which allowed me to practice transforming and analyzing data. Finding the right materials is fine, but instead of passively absorbing them, try to actively engage with the material. This could involve taking notes, asking yourself questions, or summarizing the material in your own words. More importantly, try to apply what you have learned to real-world situations. Breaking down projects into smaller tasks will make it easier to get started, especially at work, where time for learning is limited.
Focus on effort, not results
This idea, expressed by Carol S. Dweck, Ph.D. in the book Mindset: The New Psychology of Success, is essential to self-learning. There will be times when you feel discouraged. During these moments, it is important to challenge negative thoughts. Some concepts take more time to understand. Trust the process and keep trying. You are going to make mistakes. See them as an opportunity to improve, not as an assessment of your intelligence. In time, you’ll become more comfortable with the subject matter and your effort will be rewarded.
Self-learning is a journey. Always remember to be deliberate in your practice, actively engaged in the process, and, above all, kind to yourself. You may not reach your goal as quickly as you want, but the time it takes to improve yourself will be well-spent. I hope you’ll take the opportunity to learn something new and that you’ll share own tips for self-learning in the comments below. Surely, the next big topic in our profession is available to those willing to learn about it.
Maybe that person is you.