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Writing on the internet has been good to me. Now here come some numbers and models.

Hello. Here’s my first post on my revamped personal website.

Since about 2007, I have been writing on the internet. From personal blogs to freelance gigs to writing professionally, I’ve put a lot of digital ink to digital paper over the years.

In the past few years, I’ve also been trying to build up more technical skills – data analysis in Python and SQL, machine learning with scikit-learn, and lately learning to build data pipelines and data engineering.

So where does this leave this personal blog? Probably somewhere in between.

Writing

My writing these days is behind a paywall, so I can’t exactly link to what I’m writing about day to day. So I’ll probably step back and occasionally write about bigger picture things. Writers like Josh Barro and Scott Galloway and Tracy Alloway and Allison Schrager are sort of my template here.

I’ve had several iterations of a Substack over the past year, but found that wasn’t really the model for me. More often than not, I’m writing for my own edifice – exploring my own ideas, organizing my thoughts, and learning more about something as I write about it. That’s just easier to do (for me, at least) in a good old fashioned 2008-style blog.

Data engineering and machine learning

I’m also hoping to share some of my data projects, and updates on my path as I dive deeper into my foray into analytics, machine learning, and data engineering. I worked for several years as a data analyst, but there’s a wide, wide world out there that I want to explore.

So what am I doing right now?

Courses:

AWS Cloud Practitioner Essentials

I’m doing this as a stepping stone to next do either:

I’m leaning toward the latter, though I would guess I’d have more professional use for the former. Let’s get through the Essentials training, and we’ll see where that goes.

I also have more to write about how I got to this point, and why I’m not focusing on Azure or GCP. Look out for another post soon.

Udacity: AWS Machine Learning Fundamentals

I have some thoughts on this course. First, I received a scholarship for this nanodegree program. The top 400 performers then have the chance to earn a scholarship for the AWS Machine Learning Engineer nanodegree program.

But so far, I’m incredibly disappointed by the AWS Machine Learning Fundamentals program. It’s just a quick rundown for some of the AWS machine learning tools that are available – but doesn’t include any labs or really anything professionally applicable to learning machine learning on the platform. There’s some Python coding on it, but another free Udacity program I’ve previously completed – Introduction to Python Programming – is much more valuable, even as somebody who has been coding in Python for a couple of years.

I’m not sure I’ll spend much more time on this program, and I certainly won’t bother trying to hard to get Udacity’s Machine Learning Engineer scholarship. The program provided directly by AWS (Machine Learning – Specialty) seems to be much more thorough – plus I’d much rather work to get AWS’s certification rather than Udacity’s.

Outside of those trainings, I’ve just completed my company’s Python certification, and I am working toward our internal API certification.

The path ahead

Here’s an incomplete list of what I’d like to improve/learn in the months ahead:

  • Git
  • Looker
  • Airflow
  • Dbt
  • Docker
  • PyTorch
  • AutoML

Project Portfolio

Here’s a link to my GitHub. I need to update each of the projects listed there, as they’ve all seen fairly significant updates since I last pushed my code there. I also have a few more projects that I’m hoping to put up there soon, so consider my linking to it as a way to publicly put pressure on myself to actually get to it sooner than later.