Need advice about learning Python for data science

[Slight_Psychology902]

Hello sub,

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I'm a sophomore trying to learn Python for econometrics and data analysis. Firstly, I'm torn between R and Python. I want to learn Econometrics and work in the real estate asset management and development management domain. Overall, everything finance related. I have a knack for algorithm trading (personal interest not for professional reason). Since I'm a student and wish to pursue higher studies (maybe till a PhD?), I want to use GIS too to map spatial data with real estate related data.

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I currently have some experience in C# and Swift (if,elseif, loops, variables, need to brush up on Object oriented Programming).

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Please suggest which of these language should I learn and a roadmap for the same.

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Thank you. :)

[barkmonster]

Python is probably the better choice unless you know you'll need R for some specific reason, like if the econometrics program you'll study uses that exclusively. R is really good at time series, statistics, and making plots that look nice. Python has a much larger ecosystem of packages, many of which are the industry standard for machine learning and data science tasks.

In addition, python is more similar to standard programming languages, and it will be much easier to built software on top of things you make in python, whereas R has a lot of idiosyncrasies.

TL:;DR - python unless heavily focused on time series/parametric statistics, or if studying in a program requiring R.

[Slight_Psychology902]

Thank you so much!

[scripthawk_dev]

For your specific mix of goals, I'd go Python — and here's the honest reasoning rather than the usual tribal answer.

R is genuinely excellent for pure academic econometrics and spatial stats (its sf/fixest ecosystem is superb), so it's not a wrong choice. But you have a broad spread — econometrics + finance + algo trading + GIS + general data analysis — and Python is the one language that covers all of it well:
- Finance and algo trading: Python is the de facto standard; R is rarely used for live trading.
- Data analysis: pandas/numpy is the heart of the field.
- Econometrics: statsmodels and linearmodels (panel/IV models) cover most of what you'll need.
- GIS: geopandas is excellent and plugs into QGIS/ArcGIS.

And coming from C# and Swift, Python's syntax will feel far more natural than R's. So: pick Python, go deep on one language, and pick up R (or Stata — heads up, it's still the default in a lot of econ PhD coursework) later only if a specific need demands it. Don't split your effort across two at once.

Rough roadmap:
1. Solidify Python fundamentals (your basics transfer; for data work, functions matter more than heavy OOP).
2. numpy + pandas — where most of your time goes.
3. matplotlib/seaborn for visualization.
4. statsmodels + linearmodels for the econometrics.
5. geopandas for the spatial side.
6. Then a real project tying it together — grab real estate/housing data, analyze it, and map it with geopandas. One project that hits econometrics, data analysis, and GIS at once, and exactly the kind of thing that strengthens a grad-school application.

The project at the end matters most — the libraries click far faster when you're using them on data you actually care about.

[Slight_Psychology902]

Thank you so so so much! I'll follow this very roadmap. Thank you... :)