Day 39 of improving my Data Science skills

#python #devjournal #learning #datascience

A silent struggle in data work that frustrates a lot of people (and no one talks about it): "I'm learning a lot… but everything feels disconnected."

Today reminded me that the problem isn't learning too little, it's not seeing how the pieces fit while you're learning.
So here's how my today went, 4+ hours deep into data, and honestly, it was beautiful.

I started with Introduction to Data Science in Python.
The course frames learning as solving mysteries with data, which made even the basics interesting again:
importing and using modules

creating variables
setting up the foundation for analysis

Nothing brand new here, but context made it click differently.

Then I continued Introduction to Importing Data in Python
I picked up from yesterday and worked more with relational databases:
querying databases in Python

learning that any SELECT statement can be ordered by any column using ORDER BY

These are small SQL detail but big impact when working with real systems.

I moved into data visualization, this time with Seaborn
Here I learned:
how to create scatterplots

how to use count plots

how Seaborn works seamlessly with pandas DataFrames

It reinforced something important: visualization is more about asking better questions of your data rather than just displaying charts.

I started Introduction to Functions in Python, and I looked at, functions with and without parameters

Again, not all new, but clearer.

Under Python Toolbox, I learned about Iterators
This part surprised me:
the difference between iterables and iterators

how the next() method actually works

and yes, file connections are iterables too

That explained a lot of "magic" I'd previously taken for granted.

Finally, Cleaning Data in Python
This might have been my favorite part today:
unique constraints

handling complete vs incomplete duplicates deciding when to: drop duplicates or use groupby with meaningful summary statistics

This is the part of data work that quietly determines whether insights are trusted or ignored.

What today taught me? Some things weren't new. Some things were. But everything connected.
And that's the part people struggle with:
Learning tools is easy. Learning how they fit together takes intention.
I'm continuing the year doing exactly that, learning deeply, documenting honestly, and respecting the unglamorous parts of data work.
And yeah, I completed another chapter today !