Why Data Science Is One Reason I Love My Life


It took me over 20 years of schooling, 30 years of professional work experience, and over 50 years of imagination to reach this point in my life. Almost suddenly, it seems, I can clearly see that I love living my life for various reasons. One of the reasons for this is somewhat unexpected and it has to do with the topic of data science.


I could write a long article here about how my background prepared me to write this article. I’m not going to do that.

I am only going to summarize what I see in my own rear-view mirror, looking backwards in time, to help you understand how I arrived at the keyboard very early this morning to write this piece.

  • I fell in love with math a long time ago and began a life-long study of the topic
  • Much the same could be said for me about the topic of natural sciences
  • I learned to apply the language of science (which is math) to a variety of topics and these experiences tied these two topics together
  • I fell in love with computers and I wrote what seemed like a zillion quantitative programs in many different computer languages
  • These programs taught me about data in every conceivable way including how to gather it, populate it, structure and restructure it, work with it, understand its limitations, and do computations with it
  • I spent 20 years creating computational models based on real-world data for making predictions that were needed to help restore the health of our groundwater (Figure 1) and surface water  resources
  • I learned how to take the predictive numbers and make pictures and movies of the results so that people could understand the work we did
  • I spent 10 years learning about how businesses operate and how to improve them by making data-driven decisions through experimentation
  • During these 10 years, I learned to use Alteryx and Tableau to analyze and visualize data
  • That last bullet was the game-changer for me.

Now all of this experience, coupled with Alteryx and Tableau, has created the perfect storm in my brain. This storm is what I call data science.


Figure 1 – Governing equations for groundwater flow and contaminant transport. I spent 20 years creating approximate solutions to these equations.

Thinking About Athletes, Performance, and Longevity

The best athletes can be identified at an early age. I now know this from having spent the past 20 years working with kids as a coach in soccer, basketball, baseball, volleyball, etc. Certain people are born with physical skills, appropriate brain wiring, and natural drive and ambition to be rise above others when it comes to playing sports.

Sure, certain kids will be late-bloomers and their skills will be developed over time, but it isn’t possible to teach someone how to jump 38 vertical inches. That ability is principally an innate physical characteristic and gives a person an advantage when it comes to playing sports that require excellence in jumping to be successful.

Athletes compete from the time they are young until they reach their physical peak at 30 years old (+/- a few years). Beyond 35 years old, it becomes harder to compete with the younger athletes for many reasons. That is why almost all professional athletes retire by the age of 35 to 40.

Athletic careers end because of physical injuries and diminishing performance, not typically due to mental limitations. For many players, their knowledge of the game gets better with age. With experience, they understand game situations, how to formulate strategies for winning, and how to execute these strategies.

For certain players, they will take this knowledge forward and become coaches if they have the drive, determination, and appropriate personality to become leaders. In other words, certain athletes take what they learned as players and carry it forward to the next generation or two, to help those players play the game more effectively.

Why Is Data Science A Big Part of Why I Love My Life?

Contrary to being an athlete, when you work with data, age is a good thing. You will get better with age.

A lifetime of experience helps you quickly formulate ideas on how to conceptualize, characterize and ultimately solve problems using data. Decades of study, work, and continuous learning, coupled with new software tools allow you to do things that were essentially impossible earlier in your life.

As a data scientist, you can keep getting better at what you do all throughout your career because physical limitations do not really interfere with your skill development. You can put your brain through many “work-outs” without breaking it down.

When I work with younger employees, I can see that they have the training to do great things with data. Many of them are skilled in math, science, engineering, programming or some other discipline, and sometimes they remind me of myself when I was younger.

Many younger employees have told me that they know what they want to do with the data, but they just don’t know how to get to the point in which they can fulfill their vision by solving the problem. Maybe the data does not have the right structure, or maybe the formats are wrong, or maybe they do not have the right tools for the job.

In any event, they cannot complete what they want to do because their lack of experience is a hindrance. These are the times where I can use my experience to help them get to where they want to go. This is one of the best aspects of doing data science as you get older.

The good news for them is that if they stick with their careers and continue to push through barriers, they will learn to solve these types of problems. Experience will make them get better with age, and data operations will become easier and more automatic. These improved abilities will allow them to work more quickly and to solve more challenging problems as they age.

That is one of the reasons why I love data science, especially in the form of self-service analytics. I get to do everything, from gathering and building the data sets to solving the problems. Alteryx and Tableau allow me to do these things to solve just about any type of problem I encounter. This type of work is so much fun to do!

No matter what type of data science you do, you need to be able to work with and visualize all types of data. If you are smart, you will find a way to learn Alteryx and Tableau because these two tools together can allow you to do anything you want to do. You will be able to break through any barrier thrown your way. Each day, you will get better at uncovering the stories hidden in data.

Final Thoughts

I have no clue why I wanted to write this piece. I awoke one morning and this story was just waiting to escape my brain. I can’t tell you where the motivation came from or how the analogies were created. This story just happened, although it took a long time for me to understand the insights in the story.




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