4 Things In Data Science Today that Cost $0 and Have 0 Negative Impacts
A special article for reaching 400 followers! I can’t thank you all enough for your support and I hope I can continue to grow alongside all of you in our quest to become better Data Scientists.
Today I wanted to celebrate reaching 400 followers with a 4–0–0 theme. Here are 4 things in Data Science today that have $0 cost to you and 0 impacts on you in your development towards becoming an expert data scientist.
1. Failure will happen. Track It and Learn
I was scared to fail, and quite frankly, I still have those feelings of fear every so often when attempting to solve a complex problem. As the old saying goes, If you’re not failing, you’re not trying. With that being said, failure is actually what leads to growth and is a key metric for gauging whether we actually learning. So, how should we handle failure?
Track it.
Begin by adding milestones or goals within your next data science project. For each goal you fail, write down that failure. Accept that failure, but do not let that failure break you. Right down all of the reasons why you failed, and then reach out to others. Research if whether what you were attempting to accomplish was either impossible or you just adopted the wrong process. I learned that by writing down my failures, I remember them much better and it helps me not make the same mistake next time. I have failed before by either overpromising a customer a capability or not delivering the right analysis. Accept that you aren’t always right, because you are willing to accept you are fallible, as all humans are.
Tracking your failures will remove all excuses for that same failure again. It will force you to learn from your mistakes and progress toward being an expert data scientist. Add the end of the day, it always helps to laugh at failing, and just remember that another solution is right around the corner.
2. Try something new. At least once a month
Is that Coursera class still sitting there unfinished? Have you written that Medium article yet? No? Well just do it! Fear is the biggest driver for procrastination. Having the courage to take that first step will open doors you can not even imagine. Do not be worried about what others think, or be scared about the projects you have not started. The hardest part is beginning that initial step, then the ball gets rolling. I debated writing a Medium article for months, and once I did, I was hooked. Do they have to be any good? No, but to me it’s not always about being the best. It’s continuous efforts in Data Science that will help develop your skills and progress your career. As we speak I am working on building a dashboard and a time series analysis solely because I was putting them off and wanted to get better at those two skills. My biggest recommendation? Find a weakness you have, and each month choose some sort of data science task (whether it’s simply signing up for Kaggle or creating an end-to-end ML pipeline) and execute it to attack that weakness.
Getting 1% better each month will exponentially increase your capabilities as a data scientist in the future.
3. Artificial Intelligence and Machine Learning are cool, BUT…
Don’t get me wrong, AI/ML, especially the recent explosion with Large Language Models (LLM), is super cool but is not always the answer. People will ask for AI/ML because they believe it s some futuristic robot that will solve all of their problems when really it’s just a collection of mathematical algorithms. Don’t get me wrong, I am a huge proponent of AI/ML, especially using LLMs to supplement your day-to-day tasks. I have had multiple requests from customers specifically asking for AI/ML or LLMs when there were simpler approaches that would perfectly solve their problem. Sometimes simple is better, and just make sure you do your research before jumping ahead and saying “Just use ChatGPT!” when, in fact, just fixing a few business practices would solve the problem at hand.
4. Read about other industries
I am an avid reader. One book I recently read, Range by David Epstein, discusses why Generalists, a person who knows a little bit of knowledge about a lot of different domains, can be very successful in their careers and lives. In no way am I saying that you should not become an expert in the domain you work in, but having knowledge in various different industries will help you create cross-domain solutions which can solve a multitude of problems.
While books may cost money, you can research any industry out there and read various articles and journals about current efforts being conducted within the said industry.
BONUS: Here are 5 books that I have read which have helped my data science career (and truly transformed my life as a whole).
- Atomic Habits by James Clear -Helped me create habits for doing more data science-type tasks every day
- The Magic of Thinking Big by David J. Schwartz- Motivated me to think big and attempt those more difficult problems.
- Supoerforecasting: The Art and Science of Prediction by Phillip E. Tetlock- This book taught me it’s always important to assess your work and change it over time when given new information.
- Weapons of Math Destruction How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil- A really interesting book about data ethics and making sure we are developing the right ML model.
- Resilience: Hard-Won Wisdom For Living a Better Life by Eri Greitens- Seriously, this book taught me to push forward, no matter how hard or boring a task is at hand (Trust me, this will happen a lot).
Thank you to 400!
When I wrote my first Medium article, it was awful and I still am trying to produce great content each month. With that being said, thank you to the 400+ people who have found value in what I have publshed. If you are on the fence about writing that first article, do it. I promise you, you will not regret it!
If you enjoyed today’s reading, PLEASE give me a follow and let me know if there is another topic you would like me to explore! If you do not have a Medium account, sign up through my link here (I receive a small commission when you do this)! Additionally, add me on LinkedIn, or feel free to reach out! Thanks for reading!
