As I mention on the home page, I'm an avid creator and consumer of many types of data.

Of course, in today's day and age that in and of itself is nothing special. Us humans and our devices are continually creating a steady stream of data—our exhaust as we conduct our increasingly digital activities. Thus far, however, I believe much of this is going to waste. Or, worse yet, being mismanaged or used to deceive.

We have yet to really scratch the surface of what can be done with the data we can create.

Broadly, this is what inspires me and drives me to study data science. I'm fascinated by and passionate about exploring all the potential benefits that technology (much of it dependent on data) can bring to humanity, and beyond.

(Just to be clear, I'm using "data science" here as an umbrella term that includes most forms of what many would call "artificial intelligence", such as machine learning and neural networks, along with data engineering, visualization, and analysis.)

Data science (and good data, of course) can be used to optimize processes, describe complex phenomenon, learn about the past, and make inferences into the unknown. I see it as a way of augmenting what our brains have been doing all along.

I believe the innovations to come from the field of data science will allow us to learn a great deal more about ourselves and the universe in which we live, making life better for everyone along the way. Indeed, it is difficult to think of a field or industry that would not benefit from certain innovations in data science.

I want to take part in that innovation.

As with any tool, it can be just as powerful a force for bad as for good, if not more so. The important innovations to come will change us. Whether it hurts us or helps us is mostly up to us (there's always a bit of luck involved; uncertainty).

I want to do my part in moving the needle toward the latter.

That was all very high-level and grandiose. Now it's time to get a little more specific with the tools I use, the experiences I've had, and the skills I've picked up along the way.

A Lifelong Endeavor

I was born and raised by the beach in Santa Barbara, CA, also known as Paradise. Amazingly, my parents, both busy and successful doctors, found the time and energy to homeschool my two brothers and I throughout our childhood and into our teen years. They did not do this for any other reason than to give us the education they thought we deserved—one which they did not believe public school could provide.

We went on frequent family field trips to destinations domestic and foreign in order to supplement—and help guide—our education.

The positive memories of driving our trusty RV up and down and around the West Coast are the foundation upon which much of my early life was built. We would be gone for weeks at a time, visiting friends in Northern California or family in British Columbia, camping with other homeschooler families, skiing in the Sierras (my dad put me on skis as soon as I could stand up on my own, and probably even before that), and/or surfing in Mexico or wherever else we could find waves.

Our old RV, Smooky, had a bumper sticker that stated, "The world is our classroom". As cliché as it may sound, this is how my family has always lived, instilling in me a deep passion for learning.

My brothers and I grew up learning for the sake of learning; learning to sate our curiosities, whatever they may be. Learning was all around me at all times during my early formative years, and as a result, learning continues to be all around me at all times, regardless of if I'm in school or not.

For me, learning truly is a life-long endeavor.

Resource Planning

I got a BS in Economics from Cal Poly, SLO, where I competed as a student-athlete on the Swimming & Diving team (I was a diver). After I graduated, I worked for a couple of years at IQMS, a company that develops enterprise resource planning (ERP) software for manufacturers. I completed their 10-week training program and started as a tech support guru, moving into a position as an on-site implementation consultant within a year.

My job was to ensure each of my client's transition from their legacy to the IQMS system was as smooth as possible. To do so, I had to understand their business from many different angles, learning and documenting their data flows. This position gave me a great deal of very practical experience working with data and managing teams. The whole purpose of an ERP system is to make processes more efficient using data, and I was responsible for making it do just that.

I recently completed a full-time, 9-month intensive training program in data science, data engineering, machine learning, deep learning, and computer science. I worked on several projects throughout those 9 months (which you can also check out in my Workshop):

I am currently looking for a position as a data scientist, software engineer, data engineer, and/or machine learning engineer.

A list of my skills can be found below.

  • Languages
    • Python
    • SQL
    • HTML/CSS/Sass
    • Jinja2
    • Markdown
    • JavaScript
    • Bash
    • YAML
    • JSON
  • Data manipulation
    • Pandas
    • NumPy
  • Data visualization
    • Matplotlib
    • Seaborn
    • Plotly
      • Dash
  • Machine learning
    • Scikit-learn
    • XGBoost
  • Deep learning
    • TensorFlow
      • Keras
      • Mask R-CNN
    • PyTorch
      • fastai
      • Detectron2
  • Data storage and access
    • SQL
    • PostgreSQL
    • AWS RDS
    • JSON
    • MongoDB
  • Apps and APIs
    • Flask
    • Django
      • Wagtail
    • Plotly Dash
  • Imaging/Computer Vision
    • OpenCV
    • YOLO
    • Pillow
    • Scikit-image
  • Platforms and tools
    • Git and GitHub
    • Jupyter
    • Docker
      • Docker-Compose
    • Environments
      • Anaconda
      • Pip and Pipenv
      • Docker
    • AWS
      • Sagemaker
      • Elastic Beanstalk
      • EC2
      • RDS
    • Heroku
    • Google
      • Colaboratory
      • Analytics
      • GCP
      • GSuite
    • Weights & Biases
  • Algorithms and data structures
    • Sorting
    • Searching
    • Linked lists
    • Hash tables
    • Stacks and Queues