Starting Data Science journey with Python - What is the next step after lists?

Hi Surhan​,

Good foundation to start from. Here's a practical path forward once you've got lists down:

Python fundamentals to round out next:

• Dictionaries, tuples, and sets, you'll use dictionaries constantly once you get into data work, especially for handling JSON-like structures
• List/dict comprehensions, these come up everywhere in real data science code
• Functions and basic error handling (try/except), since you'll need this for clean data pipelines
• File I/O (reading CSVs, JSON) before moving into libraries

Then move into the core data science stack, in this order:

1. NumPy – arrays and vectorized operations, this is the foundation everything else builds on
2. Pandas – this is where most of your actual day-to-day data work will happen: cleaning, filtering, grouping, merging datasets
3. Matplotlib/Seaborn – basic visualization so you can actually see what your data is telling you
4. Basic statistics – mean, median, distributions, correlation, you don't need heavy math yet, just enough to interpret data sensibly

Once Pandas feels comfortable, that's usually the right point to start touching scikit-learn for basic ML concepts (regression, classification), rather than jumping there too early.

On practicing with Microsoft Fabric specifically:

Fabric is actually a great environment to learn in because it removes a lot of the setup friction beginners get stuck on:

• Use a Fabric notebook connected to a Lakehouse, this gives you a real Spark/Python environment without installing anything locally
• Start by uploading small CSV datasets into a Lakehouse and practicing Pandas operations on them directly in the notebook
• Once comfortable, try the same operations using PySpark syntax instead of Pandas, Fabric notebooks support both, and this is a good early exposure to distributed data processing, which matters a lot in real-world data science roles
• Fabric's Data Wrangler tool (built into notebooks) is genuinely useful for beginners, it lets you clean and transform data through a UI while showing you the equivalent Pandas code, which is a good way to learn syntax by seeing it generated
• Try building a small end-to-end mini project: ingest a dataset into a Lakehouse, clean it in a notebook, then visualize a summary, that one small workflow touches most of what you'll be doing early in your career

A good first project idea: pick any public dataset (Kaggle has plenty), load it into a Fabric Lakehouse, clean it with Pandas, and produce three or four basic visualizations answering specific questions about the data. That single exercise will teach you more than any tutorial series.