Step-by-step guide for starting a data science career in fundraising

Updated: Dec 2, 2019

Learning data science can be scary, especially when every online resource adds tons of materials every day. And, while every new data science concept claims to harnesses the power of your data like no other algorithm, it is hard not to feel overwhelmed and lost. The challenge, however, is not learning those data science concepts. The problem is applying them with your business knowledge. The same algorithm's output would mean different in Fundraising vs. Manufacturing vs. Banking. So, remember, the real value of data science is not learning just it but equally keeping oneself updated with your industry knowledge (in this case, staying updated with new trends in fundraising).

This guide lays the steps of how you can begin your journey in Fundraising data science.

Step 1: Know where you are and what you need

Simply starting with learning R or Python, because most online resources said so, is not a good idea. Your first step needs to be assessing how comfortable you are with your data analysis skills as of today and why you want to learn it. The comfort level would indicate where to begin and why would prioritize what to learn. Use this table below to determine your next step based on your why and comfort level. This table assumes that your why is not on critical priority to be completed within the next 5–7 days and that this is just the foundation steps. You are, of course, encouraged to continue learning beyond the items mentioned here:

*data analysis, at the very minimum, refers to looking at tabled data in an excel spreadsheet and performing functions like mathematical or logical operations.

**BI tool refers to Business Intelligence tools like PowerBI, Tableau, Qlik, etc.

***Time availability refers to the inherent time to learn the tools. An open-source tool like R might take longer than drag and drop BI tools like Tableau. Read my blog on open source vs. licensed tools to learn more.

Step 2: Find online and reading resources

Once you know where you are and your starting point, use Google for some good online tutorials. You will find options for reading materials, available to buy or rent from bookselling/renting platforms.

Some online platforms to find self-paced courses are:

- Coursera

- Udemy

- EdX

- AnalyticsVidhya

These courses generally range from USD 10 to 90 based on the level. And the best part? Some of them offer certificates on completion, and the materials are available for your lifetime. Finalizing a certificate-based course is a better option than a non-certificate based course unless you have a task at priority for which you need help. Remember, your login is share-able in your team. That means you and anyone in your organization can take these courses together. Of course, should you be working for a certificate, that would require separate credentials.

In terms of books, there are many accomplished authors for data mining and statistics. One of the most important ones is ISLR (Introduction to Statistical Learning): With Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.

Step 3: Prepare a realistic timeline and stick to it

Did you notice the two keywords in the above line? Timeline and Realistic. You don't want to start with full enthusiasm and lose your energy by overburdening yourself. Go through your current workload and prepare a realistic timeline. For example, spending 3 hours per week, Thu/Fri afternoon, to learn these skills. Make it as detailed as possible, as in what you would be learning. Throughout the week, keep a running list of all the questions, thoughts, examples, or any applications you can think from your learning. The purpose of this running list is to let your brain give you these thoughts after it has soaked some great information. Spend the first 30 mins in going over the list and then continue with your learning.

Step 4: Track your progress

As much as we are life long learners, yet, in the real world, we need to have specific outcomes of our learnings. Make clear objectives to be achieved at the end of 15 days/month/3 months and track if you are making progress or not. Remember, what you cannot track, you cannot measure. And what you cannot measure, you cannot improve.

Step 5: Build a professional network

Once you have some knowledge under your belt, reach out to people in the same boat. Update your LinkedIn profile with your interests, follow relevant groups and posts, connect with professionals of similar interests. There might even be Meetup groups around you discussing data science in fundraising! Connect with them in person. Join professional bodies like APRA, AFP, and NTEN that offers various data science programs for Nonprofits. So, Network, network, network!

Share here how you started your data science journey in the field of philanthropy. You never know how your story can become an inspiration for someone else.

Until next post — fail fast, learn fast.