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