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

Hi Surhan​ 

A lot of good roadmap advice is already covered here. One thing I’d add is to start thinking in terms of small end-to-end data workflows, not just individual Python topics.

For example:

CSV → Fabric Lakehouse → Notebook → Pandas/PySpark cleaning → simple EDA → save curated data → visualize results

That gives you practice with Python, data handling, Fabric notebooks, and basic data engineering at the same time.

Once that feels comfortable, move into scikit-learn, model evaluation, and MLflow rather than jumping straight into advanced AI libraries.

Use Claude as your instructor, it will monitor your weak areas and teach you accordingly, with project based learning. You don't even need an internship or to submit a resume for any internship . Do project-based learning add them on your linkedin. 

Hi Surhan,

Plenty of good advice above, so a few small additions. Start using Git and GitHub early, even for tiny notebooks, since it builds a habit you will need on any team. Learn to read error tracebacks and debug calmly, because beginners spend a lot of time on errors and this skill pays off quickly. In Fabric, a Python notebook is a good fit for small files and pandas practice, and you can move to Spark notebooks once your data gets too big for one machine. Keep each exercise tiny, for example answer five questions about one CSV, and then repeat it with a different dataset so the patterns stick.

AI-assisted drafting: AI was used to help structure and phrase this response. I reviewed and validated the technical content before posting.

Hi data_quantum,

Welcome to the Data Science journey! After learning Python lists, I would recommend progressing step by step rather than trying to learn everything at once.

A good learning path would be:

1. Python fundamentals

Tuples, sets, and dictionaries

if/else and loops

Functions

List/dictionary comprehensions

Exception handling

Basic file handling

2. Python for Data Science

NumPy for numerical operations

Pandas for data manipulation

Matplotlib and Seaborn for visualization

Jupyter/Fabric Notebooks for hands-on practice

3. Start practicing with real datasets

Instead of only solving syntax exercises, try loading a dataset, cleaning missing values, checking duplicates, performing basic EDA, and creating visualizations. Small projects are a great way to connect Python concepts with real Data Science workflows.

For Microsoft Fabric, you can start with a Lakehouse + Notebook workflow. Upload a small dataset to the Lakehouse, access it from a notebook, use Pandas/PySpark for analysis, and gradually learn how data moves from ingestion to analysis.

You don't need to learn Spark or machine learning immediately. Build confidence with Python → Pandas/NumPy → EDA → Fabric Notebooks → PySpark → Machine Learning.

Most importantly, practice consistently. Even a small dataset and a simple analysis can teach you more than memorizing Python syntax. Best of luck with your Data Science journey!

Microsoft Learn has very good resources 

https://learn.microsoft.com/en-us/training/modules/explore-analyze-data-with-python/ you can follow this learning path 

https://learn.microsoft.com/en-us/training/paths/create-machine-learn-models/

Regards

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.

If you’ve already learned Python basics and lists, you’re at a good starting point for Data Science. I’d recommend following this progression:

1. Strengthen Python fundamentals

- Tuples, sets, and dictionaries

- Loops and comprehensions

- Functions and lambda functions

- Exception handling

- File handling

- Basic OOP concepts

2. Move into Data Science libraries

Start with:

- NumPy – arrays and numerical operations

- Pandas – data cleaning, transformation, filtering, grouping, and analysis

- Matplotlib/Seaborn – data visualization

Pandas should be your next major focus because you'll use it extensively for working with real-world datasets.

3. Start practicing with Microsoft Fabric

For a beginner, don't try to learn every Fabric component at once. Start with a Lakehouse and practice a simple end-to-end workflow:

"CSV/Excel → Lakehouse → Notebook → Pandas/Spark → Data Cleaning → Visualization"

Create a small project—for example, analyze sales data. Load the dataset into a Fabric Lakehouse, open a notebook, use Python/Pandas to clean and explore the data, and then create some visualizations.

4. After that, learn

- Statistics and probability

- SQL

- Exploratory Data Analysis (EDA)

- Machine Learning with scikit-learn

- Eventually, Spark/PySpark for larger datasets

My biggest recommendation: don't spend too much time watching tutorials. Build small projects while learning. Even a simple sales, customer, or employee dataset can teach you much more than just completing Python exercises.

A good roadmap would be:

Python → NumPy → Pandas → Visualization → SQL → Statistics → EDA → Machine Learning → PySpark/Fabric

Take it step by step and focus on building projects along the way. Good luck with your Data Science journey!

Good luck !

Hi,

Good place to be starting from. After lists, I'd move to dictionaries and tuples next, they come up constantly once you're working with real datasets, then loops and functions if you haven't covered those yet. After that, jump into pandas early, honestly more useful for data science than deep diving into pure Python data structures for too long.

For Fabric specifically, spin up a free trial workspace, create a Lakehouse, and just practice loading a CSV into a notebook using pandas or PySpark, that combo covers most of what you'll actually use day to day.

If this helped, feel free to give it a kudos or mark it as solution, helps others find it too.

You need to learn a lot about python and how to use it, more than that you need to have strong mathematic especially in statistics and probability to be a good data scientist.

To be honest, I don't see that python is the only programming language for data science, there's many programming languages, what makes python as popular programming language for data science is that it contains a lot of libraries that helps data scientist and a large community that contribute in the open source projects.

But in python many libraries can be helpful for you as data scientist like NumPy, pandas, Matplotlib, Seaborn, Plotly, scikit-learn, TensorFlow, PyTorch,PySpark, Transformers like "Hugging Face".

You need to learn a lot about python and how to use it, more than that you need to have strong mathematic especially in statistics and probability to be a good data scientist.

To be honest, I don't see that python is the only programming language for data science, there's many programming languages, what makes python as popular programming language for data science is that it contains a lot of libraries that helps data scientist and a large community that contribute in the open source projects.

But in python many libraries can be helpful for you as data scientist like NumPy, pandas, Matplotlib, Seaborn, Plotly, scikit-learn, TensorFlow, PyTorch,PySpark, Transformers like "Hugging Face".