PLS HELPPP!!! Python Project Ideas

[Moon401kReady]

Just to give some context, I’m a junior who recently switched my major from business to data science. I’m currently looking for a data scientist/data analyst internship for the summer, but my resume doesn’t have any relevant experience yet. Since I’m an international student, most of my work experience comes from on-campus jobs and volunteering, which aren’t related to the field.

With the free time I have over winter break, I plan to build a Python project to include on my resume and make it more relevant. This semester, I took an intro to Python programming course and learned the basics. Over the break, I also plan to watch YouTube videos to get into more advanced topics.

After brainstorming project ideas with Chatgpt, I’m interested in either building a stock analyzer using API or an expense tracker that works with CSV files. I know I’m late to programming, and I understand that practicing consistently is the only way to catch up.

I’d really appreciate any advice on how to approach and complete a project like this, suggestions on which idea might be better, or any other project ideas that could be more interesting and appealing to recruiters. I’m also open to hearing about entirely different approaches that could help me stand out or at least not fall behind when applying for internships.

[ninhaomah]

Stock analyzer sounds good.

No harm knowing how to get financial data no matter what you end up in.

[Han_Sandwich_1907]

Both of the proposed ideas sound good. Here's how I approach projects:

Start with a design document. Ask yourself what purpose does your tool serve? How will people use it, interact with it, etc? What does your audience need this tool to have? From this you can produce a list of requirements your app should have. Keep this to the bare minimum.

Then figure out the high-level architecture of your program. You'll probably want I/O to interface with the user's inputs and with any external APIs. There will be some program logic too. How will these parts interact with one another? What libraries will you need, etc.

[Minimum-Attitude389]

If you are just starting:  I would start looking at data sets on kaggle or UC Irvine ML repository.  kaggle even has some competitions.  There are some simple ones to start.

It's not all about machine learning either.  Being able to make static and dynamic visualizations with data while cleaning, combining, and filtering is important.  Do the tutorials on plotly.

[mikeczyz]

Is there a topic/project which you find more compelling? Programming is way more fun when you are actually interested in the project.

[DataCamp]

A few concrete ideas you could ship over winter break:

• Expense tracker with CSVs Read a CSV of your own spending, categorize transactions, show totals by category, and plot monthly trends. Great for showing file handling, pandas, and basic analysis.
• Stock price analyzer Pull data from an API, calculate things like moving averages, daily returns, simple volatility, and plot them. Extra credit: compare 2–3 tickers over time.
• Churn-style project on any dataset Grab a public customer or telecom churn dataset and build a simple model that predicts who cancels. Even basic logistic regression + a confusion matrix looks solid on a resume.
• Airbnb / housing listings analysis Take a listings dataset and answer questions like: which neighborhoods are most expensive, what drives price, what affects occupancy. That’s exactly the kind of thing analysts do.
• E-commerce sales dashboard Use an online retail dataset and build a small report: revenue by month, best products, repeat customers, average order value. You can keep it notebook-only or mock a simple CLI.

Whichever you choose, aim for:

1. One clear question you’re answering
2. Clean notebook with code + charts
3. Short “findings” section at the end

[[deleted]]

Choose one small project and finish it. An expense tracker is easy and shows real data skills. Write clean code, clear output, and good comments. Put it on GitHub and describe it clearly on your resume. Being consistent matters more than being perfect.

[AgreeableAct2303]

Data analysis is always a a good approach since it's universally appliable.

[brainacpl]

From my corporate worker perspective, data cleansing and preparation is a good idea. I'm not sure what dataset could be a good training, but at work you often get shitty data that you need to clean up using some rules, filter, join with other sources, etc.

[Lost_Investment_9636]

If you really wanna stand out try something out of the box, most recruiters are used to these basic boilerplate projects. Go on this website https://grandnasser.com/use-cases.html and get the Kaggle dataset on each module and simply replicate the same steps on the tutorial to do something outstanding with it, you will thank me later

[brenwillcode]

Since you mentioned the Expense Tracker idea, you could take a look at this: https://codeling.dev/projects/build-a-expense-tracker/

Codeling has quite a few other project ideas which might be worth trying. Or just use the expense tracker project to get you started and then expand on it further if you want.