3 Practical Ways to Become a Citizen Data Scientist and Learn Machine Learning

"The first decision in decision making is when to make a decision," proclaimed Mike Hayes, the chief digital transformation officer of VMWare and former commanding officer for SEAL Team. I was attending Rev 3 last week (May/2022), a conference on ModelOps, and, more generally, a conference for data scientists to learn and network about their practices in experimenting and delivering machine learning models that drive business impact. 

Citizen Data Science an Learning Machine Learning

It was at The Marriott Marquis on Broadway in NYC, and the ballroom was filled with really smart, mostly young, and very eager aspiring Digital Trailblazers

But for me, I was actually following another one of Hayes' recommendations, "Slow down and carve out that think time." Yes sir. And my recommendation - check out Mike's book, Never Enough: A Navy SEAL Commander on Living a Life of Excellence, Agility, and Meaning.

At the conference and long before it, I have been thinking about how more people can improve their data literacy and, more specifically, become more hands-on working in citizen data science programs. I learned some recommendations at the conference and in speaking with experts.

1. Learn from SMEs, ask questions, challenge the status quo

Two more great pieces of advice came straight during the opening session. Hayes recommended to the audience, "What matters is who is closest to the problem and suited based on experience to solve it." He was sharing one of his best practices around decision-making  - people who understand the problem, risks, and ramifications are the ones who should see the data to help guide decisions. In Hayes' case, that may be pulling the trigger on a special ops. 

Later in the opening session, Linda Avery, chief data & analytics officer at Verizon, shared how they challenged their core operationally-minded culture. She said, "With an execution-oriented operation comes a very directive culture. We have to move away from that to a culture where it's ok to question."

This advice applies even more to aspiring data leaders, and it reminds me of a post I wrote nine years ago on asking smart big data questions. That post came after a town hall where several business leaders showed off their data visualizations and other data tools to analyze customer and operational data. That was my first citizen data science group, and their journey didn't start with learning new tools. It started by being inquisitive and learning about what data was available to make more informed decisions. 

2. Bring your questions and data to a learning program that works for you

Once you have a question and some data, you are better equipped to learn data practices and tools. It's ok to start with the examples from tools, books, and courses, but their exercises often gloss over the realities of working through data quality and sourcing issues. 

Speaking with Rosaria Silipo before the conference, principal data scientist and head of evangelism at KNIME, suggests these five ways to learn data science practices and tools:

  1. The practical way: Find a problem to solve, learn and apply different techniques until you solve it
  2. The classic way: Get a book, study the algorithms, and do the exercises recommended in the book
  3. The virtual way: Register for a quick online course. Follow the explanations, do the exercises, and get your certificate (if any)
  4. The comprehensive way: Attend a data science program at a college/university 
  5. The mentoring way: Work with a colleague who is nice enough to introduce you to the world of machine learning

My suggestion: Learning is an investment in time and often money, so pick an approach that works best for your learning style and what you can afford. To go from basics to mastery, you're probably going to have to apply multiple approaches. For example, you might apply "the classic way" to learn statistics, "the virtual way" when learning a data viz tool, and "the comprehensive way" when you're ready to learn the math, science, and implementation behind machine learning algorithms. 

Lauren Clayberg, software engineer at mabl, also sent me these suggestions regardless of how you go about your learning. She says, "Learning machine learning starts with understanding its core components: the math behind optimization, how to find high-quality data, and the benefits of different performance metrics. Since machine learning is just automated optimization, building a solid understanding of these pillars allows people to dive deeper into specific models and how to use them."

3. Join an analytics team, deliver a business-impacting model

Rev 3 was filled with top data scientists that wanted to expand their skillsets, and Nick Elprin, CEO & co-founder of Domino Data Lab, shared this key revelation with them. He said, "Playtime is over for data science. If your work is relegated to an innovation lab, then you are already behind."  

That's also great advice to aspiring data scientists and citizen data scientists. Bringing data visualizations, analytics, and machine learning models to production, delivering business impact, and providing support around the modelops is not a one-person effort. It takes a team of subject matter experts who know the business, dataops engineers, citizen data scientists, data governance specialists, and data scientists to collaborate on the problem, experiments, and working solutions. To collaborate as a team, I shared why data Science, dataops, and data governance need agile methodologies and three ways to apply agile in data science and dataops in previous posts.

Going back to Hayes' remarks, "The first decision in decision making is when to make a decision." It's time to make a decision and develop your data science skills!


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