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

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".

Hi Surhan​ 

Lists are a good starting point. From there, I would continue with the parts of Python that make it possible to solve complete problems rather than learning each feature in isolation.

My next steps would be:

• Dictionaries, tuples and sets
• if statements and loops
• Functions and parameters
• Reading and writing files
• Exception handling
• Modules and packages
• List and dictionary comprehensions

I would also practise working with strings, dates and missing values, because those appear frequently when working with real datasets.

Once I felt comfortable with those fundamentals, I would move into the main Python libraries used for data work:

• NumPy for arrays and numerical operations
• pandas for loading, cleaning, filtering and transforming tabular data
• Matplotlib for basic visualisation
• scikit-learn for introductory machine learning

Microsoft’s Explore and analyse data with Python module is a useful next resource because it introduces NumPy, pandas and Matplotlib through common data-analysis tasks.

I would learn these alongside some basic statistics rather than waiting until all the Python theory is complete. Topics such as averages, distributions, correlation, sampling and train/test splits will help the code make more sense.

For Microsoft Fabric, I would start with a small notebook project rather than going directly into advanced machine learning. For example, I would:

1. Create a Fabric workspace and Lakehouse.
2. Upload a small CSV dataset.
3. Open a notebook and attach the Lakehouse.
4. Load the data into a DataFrame.
5. Inspect the columns and data types.
6. Handle missing or duplicate values.
7. Calculate a few summary statistics.
8. Create two or three visualisations.
9. Save the cleaned data back to the Lakehouse.

Microsoft documents how to use Python notebooks in Fabric, and its Data Science end-to-end tutorial provides a guided path from loading and exploring data through to training and scoring a model.

After completing one or two small analysis projects, I would then move into:

• Exploratory data analysis
• Feature preparation
• Supervised learning
• Model evaluation
• Experiment tracking with MLflow

Microsoft also has a focused Data Science and machine learning learning path for Fabric that covers preprocessing, model training and MLflow.

My main advice would be to keep building small projects while learning. I find that concepts become much easier to remember when I use them to answer a real question with data.

A simple first project could be analysing sales, student results, weather data or customer behaviour and producing a short notebook that explains what I discovered.

Good luck with your journey!

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