I am a fundamentalist analyst at heart, but I’ve heard great things about R speeding up analysis. Want to learn it, but don’t know which course to take - there are several, with differing times to completion (ex: learn R in 4-6 weeks or 3-6 months).
Has anyone done this before? How long could it take if, say, at minimum, I cover R on the weekends for 6-8h a day, and I am a noob?
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Based on the WSO Academy offerings, I recommend checking out the WSO Elite Modeling Package, which includes comprehensive financial modeling courses. While it primarily focuses on Excel, the skills and analytical techniques you learn can be a solid foundation for understanding data manipulation and analysis, which are crucial when learning R.
For learning R specifically, you might want to explore external resources like DataCamp, which you mentioned, or Coursera and Udemy, which offer courses specifically tailored to learning R. These platforms often have courses that range from beginner to advanced levels and can be completed at your own pace. Given your schedule of dedicating 6-8 hours on weekends, a beginner course could typically take anywhere from 4 to 6 weeks to complete, depending on the course structure and depth.
Remember, the key to making the lessons "stick" is consistent practice and application of what you learn in real-world scenarios or projects. This hands-on approach will help reinforce your learning and improve your proficiency in R.
Sources: Benefits of learning R language, Tips for Learning R, Am I screwed for the CFA?
Would recommend python, but I’m just an intern and haven’t seen a ton. It’s more popular and so if you don’t know how to code that’ll be a good starting point as there’s probably information online about it than any other language. You can do the same analysis as you would in R, so you wouldn’t be losing anything. I’d recommend a coursera course. There’s a few under names like Python for ML, python for data science, etc. These will teach you python foundations but will also teach you to use standard data analysis packages like pandas, numpy, matplot, sklearn, etc. If you run into errors chances are, someone else has already asked about the issues and you can find a solution online. Also GPT is a good tool for this as you can ask it to explain the purpose of a code snippet and it’s reasonably accurate for this use case.
Could take around 3-4 weekends and you’ll be reasonably comfortable. I taught myself python in middle school and it took me 5 weeks. A capable adult should probably reach a reasonable level of familiarity with less time
thanks for the quick and thorough reply! Will be even better if Python (more flexible, wider scope). High time I learn it in order to improve my craft and differentiate myself from others. Happy to know it doesn’t take long to learn it, free time has been scarce recently.
R is more for people who want to compute statistics. Python is better for doing less rigorous analysis on data. If you’re a fundamental guy and want to continue doing fundamental analysis then Python and/or SQL are probably your two best bets. You only ahould need R if you’re trying to become a technical quant who needs to do a lot of statistical analysis across big data sets.
As someone with a quantitative background, I’d recommend python as well due to its ease of learning and the available tools for a beginner. If you’re set on R, I’d check out Swirl, where you can learn R in R.
Thanks for the quick reply! I will go Python then, and go from there as to decide whether I will learn R or not
What use cases have you heard of?
A top HF manager saying that people using R/Python make data analysis super quickly, blows his (and other vets) minds. So essentially, automatizing data processes such as data scraping.
I mean, it won’t make you a better analyst per se, but it is a tool (like the CFA material) that you can leverage to try and be more accurate
Engineer by training here.
While python is more used (as others say here), R was literally made by data scientists and statisticians to make data analysis convenient.
Python is more used because it is versatile - can do anything from websites to games to deep learning. Nevertheless, I would personally recommend R for data analysis for finance.
It is very easy to learn (like Python), and a lot of resources exist. There are some very good packages that make data wrangling and analysis very smooth-flowing with easy baby steps and plain English.
I personally did a Data Science program from Harvard on edX while in college. (I think you can audit all for free, or if you want a certificate the fee per course is cheap). Highly recommend!
It covers basic R, data visualization, probability and statistics, data wrangling, leading up to machine learning along with setting you up with very convenient packages along the way. Self paced, great real life cases, interactive platform, follow along programming. Looks like 9 courses, but it's small bite-sized chunk covering basic aspects. You could learn just from YouTube too, but a lot of videos skip the stats - so you could be garbage in garbage out and not know.
In 7-8 weekends, you'll be pretty smooth. Any non-techie is gonna have their mind blown. You'll stand out.
thanks for the quick reply! you make a solid case for R, it is exactly what I aspire to.
I will try to learn both, but perhaps start with R. I mainly intend to do data analysis (I am a very qualitative analyst at heart, want to unify the quantitative aspect more) for idea generation and speed up certain processes.
You're welcome. If you want to do trading or sth, then learn Python. But to help with data analysis/statistics at funds, R all the way.
R is awesome for data analysis. Not just stats anymore, although that used to be the case. As long as you use “tidyverse” packages, its almost like a second language. Non-tidyverse R is a PIA imo and inferior to python.
This book is the ticket, written by tidyverse creator:
https://r4ds.hadley.nz/
These days, the only issues R has compared to python is less widespread adoption, cloud deployment can be more difficult, and IDE support is not as good. But this all just boils down to R having a smaller community.
If you’re doing analysis and models on your laptop pseudo-manually (don’t need to automate in cloud), its way better than python imo. It is just optimized for that.
Coming from quant PM perspective that’s used both R and Python- I’d say with no hesitation to go Python route even though R historically’s been a great language. Think it’ll serve you better in the long run, and ramp-up will be much easier / quicker too.
In my mind almost anything you can do with R you’re able to do with Python. That includes data analysis, stats/ML, viz, etc. And there are a lot more people doing package dev/upkeep for Python, so that positive net versatility trend over R is only going to continue over next 5-10y.
On top of that, much more resources to learn from for Python than R. You can find good textbooks for both, but don’t think they’re going to do you well for either language if you really want to leverage for actual day-to-day work. A lot more online resources available for Python is going to make things 20x easier for you for learning/debugging/etc.
R has been a great stats resource, but so has Python, and in 2024 to me R is past its prime… so take that FWIW
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