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October Update – Python, AWS. Next up, Blockchain?

My output on this blog has been less than ideal. I’d still like to use this as:

  1. A way to keep track of my own progress in developing new skills and ideas (and putting it into public view as a sort of motivating factor)
  2. A portfolio of some of my work

But I feel like I have a good excuse, at least this time (though I should remember to set aside time every week to regularly reflect on where I am and where I want to go).

A productive two months

It has been almost two months since my last entry. But it has been a very productive stretch. A rundown of my last 6 weeks:

Earned: Python certification

I received my company’s internal Python certification. It was built mostly around data analysis using Pandas/Numpy and building data visualizations with Seaborn.

A lot of the prep was done with LinkedIn Learning courses, which I thought were fairly high level, but fine. It definitely wasn’t as in depth as something like the Python for Data Science and Machine Learning Bootcamp course by Jose Portilla on Udemy, which I used to jump into Python for data a few years ago and thought was excellent.

This was stuff I’ve had experience with before, but good to know I’ve cleared my company’s benchmark.

Pending: API certification

I also completed all of the work to complete my company’s API certification as well – I’m just waiting on being judged.

This program was centered on building tools using our set of prebuilt APIs for clients. It was as much or more geared as a background refresher for sales staff as it was technical.

I have less experience in this area than with data analysis in Python. But I’ve also separately been building some tools to pull US government economic data through APIs (and also working on some ETL functions).

Earned: Amazon AWS cloud practitioner crrtification

I also passed the AWS cloud practitioner certification. This was a pretty high-level overview of AWS and some of the products AWS offfers.

I wanted to do this as a jump-off to the AWS Machine Learning specialty certification. I was less than impressed with some previous AWS machine learning training I’ve done (more in that below), so I’m hoping the AWS-provided training complemented with some LinkedIn Learning courses is a better path.

Quit: Udacity AWS Machine Learning Foundations course

I received a scholarship to this class, and it just wasn’t cutting it for me. There was minimal background around the kinds of AWS machine learning tools I’d use professionally. All I want is a couple labs to learn Amazon SageMaker and a sandbox environment to test some basic models.

The Python part of the course was a good refresher though.

Overall I’m pretty disappointed. And it makes me seriously question actually spending money on other Udacity courses. I was interested in the Data Engineering course, but I’ll almost certainly take the AWS or Azure or GCP equivalent instead.

Next up

AWS Machine Learning specialty certification

Like I said I jumped into AWS to prepare for the AWS Certified Machine Learning certification immediately after finishing the cloud practitioner certification. I’d like to finish this in the next six-to-eight weeks, so I’ll put a soft deadline of Thanksgiving on this one.

But I may have to push that back a bit because I’m starting…

Build a Web3 App on Ethereum with Solidity + Smart Contracts

I own literally zero crypto. And aside from a brief period of buying bitcoin (around $19K) and selling about a week later (that was a mistake), I am at zero experience with anything crypto/blockchain.

I’ve been reading a lot lately about some new DeFi products though, and I think a lot of trends in the space are very interesting. And I thought what better way to learn than to build.

So this is a two-week course offered by _buildspace, starting 10-Oct.

I’m pretty jazzed about this one. Look for the comprehensive review and my own line of NFTs soon.

Categories
Blog

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.