I recently finished my Olist e-commerce SQL project and published it on GitHub. Most of that project involved writing SQL queries, checking data quality, and building a star schema in MySQL.
For this stage, I’m focusing on using Python to work with data. I’m keeping the scope small: learn the tools properly, practice with datasets, and understand the basic steps of ETL before trying to build a larger pipeline.
My setup
I’m using Python in a virtual environment on my Mac, with Jupyter Notebook for practice. I have installed Pandas and verified that I can import it in a notebook.
What I’m learning
| Topic | What I want to practice | Status |
|---|---|---|
| Python environment | Virtual environments, installing packages, Jupyter Notebook | Done |
| NumPy basics | Arrays, indexing, data types, operations, filtering | Learning now |
| Pandas basics | Series, DataFrames, selecting and filtering data | Next |
| Data cleaning with Pandas | Missing values, duplicates, type conversions | Planned |
| Data transformations | Sorting, grouping, merging, creating columns | Planned |
| Reading data | CSV and JSON files, basic API responses | Planned |
| Mini practice exercise | Read, inspect, clean, and export a dataset | Planned |
I’m not trying to learn every feature in NumPy or Pandas. I want to understand the parts I can actually use when handling data.
How I’m studying
I’m following video lessons, including CampusX, and practicing each concept in Jupyter Notebook. My approach is simple: learn one topic, try a small example, and write the code myself before moving on.
For example, I’m starting with NumPy arrays and how operations on arrays differ from normal Python lists. After that, I’ll move to Pandas DataFrames and learn how to inspect and clean a dataset.
My goal for this stage
By the end of this part of my journey, I want to be able to:
- Read a CSV or JSON file using Python.
- Inspect rows, columns, data types, and missing values.
- Clean duplicates and handle missing data appropriately.
- Filter, merge, and aggregate data with Pandas.
- Save the processed data to a new file.
- Explain the basic difference between ETL and ELT.
A small working exercise matters more to me right now than adding lots of technologies to a roadmap.
Where I am right now
My SQL project is complete and on GitHub. My Python virtual environment and Jupyter Notebook are set up, and I’m currently working through NumPy fundamentals. Pandas is next. I’ll update this post as I complete the exercises.
