Progress Update – Python, Projects & Job-Market Insights
It’s been about 10 days since my last update, so in the off-chance anyone is following my progress, I wanted to share a quick overview of what I’ve been up to.
After finishing the SQL course and posting a couple of updates around that, I’ve now shifted my focus to Python. It’s one of the most frequently referenced skills in Data Analyst job descriptions (more on that shortly), so I need to get to grips with it sharp-ish.
My Python journey began with the highly-rated “Complete Python Bootcamp From Zero to Hero” on Udemy by Pierian Data—the same team behind the SQL course I completed. I got about 10% in before realising it wasn’t geared toward Data Science in the way I’d hoped. So I spent some time on YouTube learning the basics, then returned to Udemy for “Data Analysis with Pandas and Python,” which is far more aligned with what I need. I’m currently around 10–15% through it.
Although the course is around 20 hours long, it takes much longer in practice because of the number of tests and hands-on exercises. That said, I’m expecting to wrap it up early in the week commencing Monday 1st December. There’s some overlap with what I learned in the original Python course and on YouTube, but honestly, there’s no harm in reinforcing the fundamentals a few times.
Earlier this week I wrote on LinkedIn about my learning approach: 3–4 hours of structured learning, followed by a couple of hours of more intuitive experimentation.
It’s been a long time since I’ve done “structured studying” and it’s intense, and there’s definitely a point of diminishing returns, particularly with concentration and retention.
That said, I’ve learned a huge amount from the more playful side of learning: experimenting with APIs, building small charts, and exploring tools inside VS Code that I hadn’t used before—like Data Wrangler. This hands-on, exploratory phase really suits me, but I’m conscious it might lead to gaps, so a blend of structured learning (for now, weighted slightly more heavily) feels like the right approach.
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My Current Project: Analysing UK Data Analyst Job Posts
Using what I’ve learned so far, I’ve started a more substantial project:
• Pulling job-post data from the Reed.co.uk API using Python
• Cleaning and transforming it in Pandas
• Saving it into a Postgres database
• Bringing it into Microsoft Fabric
• And finally surfacing the insights in a Power BI dashboard
Reed has a call limit, so my plan is to pull as many of the most recent job posts as allowed related to data analysis and examine employers, roles, salaries, and crucially skills. The aim is to identify where I should focus my upskilling efforts.
From the sample dataset I’ve collected so far, here’s a snapshot of the most commonly requested skills:
• SQL 35%
• Excel 32%
• Python 26%
• Power BI 24%
• Azure 14%
I can’t show cumulative skill combinations here (since each job can include multiple skills), but I’ll be analysing those pairs and clusters next.
This data is actually really encouraging—it reinforces that I’m putting my effort in exactly the right places: SQL, Python, and Azure. I’ve either completed or planned structured learning for all three.
As for Excel and Power BI, I’m already pretty comfortable with them, so instead of formal courses I’m incorporating them into my portfolio projects as a way to demonstrate real-world application rather than certification.
I must now crack on with my learning and I’ll report back soon!