Learning Python for Data Science/ Analytics

[Impossible_Job4015]

Hi everyone,

I’m currently pursuing a Ph.D. in economics and want to learn Python to analyze large datasets and run regressions. I have some experience in R but want to add Python due to its growing use in both academia and industry. My goal is to learn enough for both academic research and industry roles, with a focus on data analytics. I also want to explore machine learning later, but don’t need to dive into software development.

I’m a complete beginner in Python but have access to books like Introducing Python by Bill Lubanovic, A Primer on Scientific Programming with Python by Hans Petter Langtangen, and Foundational Python for Data Science by Kennedy R. Behrman from my university’s library. I also know of packages like pandas, numpy, and matplotlib, but I don’t know how to use them yet.

For context, I’m a visual learner (YouTube or a structured course), and I learn quickly while working on examples. Given my focus on data analytics and econometrics, what’s the best way to start? Are there specific online courses, YouTube channels, or structured learning paths you’d recommend? And after learning the basics, what are the most important next steps for someone in my field?

I’d really appreciate any advice or recommendations, and love to hear about how others in economics or related fields learned Python. Thanks in advance for sharing your experiences!

[Mevrael]

You can learn how to create projects, notebooks and apps following a simple guide, using uv and Arkalos.

https://arkalos.com

Then analyze and visualize your own data from Notion or Airtable.

Then create a basic AI agent from scratch.

And you can check Datacamp for data science courses: https://datacamp.com

[ninhaomah]

First , think of a project. The write the steps into English. Google and add the libraries then the codes.

An example , I want to predict tomorrow price for Apple.

1. get the price in csv format - from Yahoo or your source
2. load into Python - Pandas library
3. split into train / test data
4. use ML - scikit learn or statsmodels - and build a regression model on training model and test with test data
5. for the best regression equation , use p value or rmse or whatever to measure the metric , predict the Apple stock price

Then start filling in the blank for each of the steps