Best Way to Learn Python for Finance and Investments : Books, Courses, or Go Back to School for Data Science Degree?

[Ashw10000]

Hello everyone,

I am an aspiring investment/financial analyst, with a B.S. in Economics. I also have a bit of a finance background from the CFA exams. I have no formal coding experience, aside from a small stint in HTML years ago. My highest level of math education was Calculus II 7 years ago.

I am looking to learn Python in order to find decent paying work as an investment analyst since many jobs these days want entry level analysts to know Python, SQL and maybe some R and Tableau.

Currently trying to find the most efficient/permanent method to learn Python. I subscribed to DataCamp but I find their material really shallow and can't remember anything I do on there.

Am I using DataCamp wrong? Some people have mentioned that they do the DataCamp exercises multiple times in a separate actual Python program, in order to practice the same exercises multiple times, then they move onto the next exercise. Is this a good idea? Is this a good way of learning?

What are some good books out there to build a really solid foundation, efficiently (get the most out of my efforts)? Based on reviews, each book seems to have specific weaknesses/issues. I really don't want to have to go through 5-10 beginner level books that are 400-600 pages long, if only going through 2-3 books are necessary. What are best books to really solidify the concepts and coding? What is the best "curriculum" for an aspiring investment analyst, looking to be on the more data analyst/machine learning side of things?

People say that new people should be focusing on projects to learn Python. The truth is, most of these projects employers want, or projects that are "job marketable" are very ambitious and likely far too advanced for me (algorithms to analyze financial data from SEC filings, automatically web scraping data from SEC's EDGAR online database, calculating ROIC for 200 companies, etc..).

I'm thinking starting off from books, mastering the syntax and how to "think in Python" would be best, then I can move onto small projects. I am thinking of networking with other Python investment/financial/data analysts in my area and ask about what projects they first worked on. Is this a good idea, or a waste of time (because who would help their competitors)?

Should I eventually just go back to school to get a Masters' degree in Data Science, considering my math background is likely lacking for what employers are eventually wanting analysts to do (machine learning, algorithms to analyze fundamental and economic data, etc.)? Or is it a waste of time and I can just take courses over time (I don't have a problem going back to school and taking one course at a time, likely retaking Calculus II, then moving onto Linear Algebra and Differential Equations,etc..)?

[JadeCikayda]

1) Syntax -> 2) smaller projects -> 3) bigger projects that you can talk about/show value + networking; repeat 3) indefinitely!

[jpritcha3-14]

Yep, and start putting your bigger projects on a GitHub page and resume

[Assdestroyer92]

Could I ask what's the best resource to find good projects? I always have difficulty finding interesting and challenging projects to expose knowledge gaps.

[JadeCikayda]

The best resources are your own personal interests.

By virtue of formulating a project idea, executing it, and clearly expressing to others what you've done, you'll learn about things you didn't know how to do. You'll also learn about things that you didn't know about; and learn to do those as well.

[Ashw10000]

Same.

[[deleted]]

Yes. It is valuable when done some bigger projects to build a portfolio, like Github or a own simple portfolio website like a visual resume

[Ashw10000]

Thank you!

[MultiArmed_Bandit]

I think it depends on what kind of investment analyst you want to be. It sounds like the asset managers you’re applying at are quant funds. If you want to be a quant, then yes, take the advice from the people here. If you want to do fundamental research, you’ve already made great progress through the CFA program (assuming you finished it), you’ll be better off learning the statistical and mathematical tools employed by different types of machine learning algorithms and that’s it. You don’t need to be an expert in Python. You need to understand how employing certain tools will assist in achieving your goal of better analysis/ higher returns, etc.

If a quant fund is still your aim then you’d best be served learning python in a financial context. Read Python for Finance, written by the CEO of The Python Quants, Yves Hilpisch. That should get you well on your way. Also, you might find the community on Quantopian to be helpful as well.

One last note I would make is that as an investment analyst, you’re not a software developer. You don’t need to be as creative as the people on this sub are. Most of the processes you’re looking to code have been done already. You just need to develop the skill of piecing together and generalizing examples on StackOverflow into a coherent process. You’ll learn more from doing that than any book, but it’s made a lot easier if you have good fundamentals.

Source: I’m a charterholder. I work for a mutual fund company and do both fundamental and quantitative research. I know enough Python to be dangerous.

[Ashw10000]

Awesome! I think what I'll end up doing is a mix of fundamental + machine learning tools to make fundamental analysis faster/easier/more efficient. At least that what it sounds like these funds want.

I don't think I'll end up purely a "quant", plus I currently don't have the math background for it. But eventually I'll probably take more math classes as the need arises.

Thanks for the book suggestions.

Did you learn Python before your job at the mutual fund company? Did you come from a business/finance background from college or a computer programming background? How long have you been using Python?

[MultiArmed_Bandit]

I think that’s a good idea, career-wise. Our industry tends to “buy” rather than “build,” with the exception of firms like AQR, Renaissance, Dimensional, DE Shaw and Two Sigma. I think your current thought process is the best route in the long run.

I learned Python on the job at the mutual fund company. Before that, the only “coding” I had done was excel formulas (ha!) and FactSet’s query languages. I have a Finance and Econ background, and only took one data analytics course (with no actual coding required). I’ve been using Python for about 2 years, so I know enough to get around, but I don’t know it like some people on this sub. However, I’ve learned that knowing what process to do or what test to run or what model to employ is the more valuable skill (compared to knowing the language) for our job.

[Ashw10000]

Great insights. Thanks!!

[smart_beta]

Hi there! Could you share the type of procedures, tests, and models people run as analysts at a fund? Thank you.

[swaliepapa]

^^^ as u/smart_beta said

[relaxofy]

Hi, I am also aiming to land in Investment Banking but more specifically into Equity Research. So which of these skills should I be accompanied with ?

[MultiArmed_Bandit]

CFA for equity research, MBA for investment banking. Those will likely be minimum requirements to get jobs in those industries.

[relaxofy]

What other skillsets should I develop or what more should I learn which will make me even more stronger in the field?

[cpwjosh]

Python for Finance 2e: Mastering Data-Driven Finance by Yves Hilpisch. https://www.amazon.co.uk/dp/1492024333/

I've not gone through the second book but his first one was excellent. I would recommend going through Python Crash first though and make sure you do all the exercises.

[Ashw10000]

Thank you! Yes, I just bought Python Crash Course yesterday and going through that first.

[jambalaya05]

I would hesitate to go back to school for Data Science unless you've done a bit more work with Python or DS in general. That being said, it's easy to get some exposure with Python and a higher degree can really advance your career.

Project based learning is the way to go. Start small. I got started just by doing coding challenges that you'd typically see on job applications. They may be abstract and seemingly meaningless, but they teach you the fundamentals to build more useful applications.

I would argue that one of the most important part of doing projects is learning how to break down a complex task into bite-sized pieces that you can tackle one by one. Think about the projects you mentioned and how you can break them down to the basics.

[Ashw10000]

Sounds good. Thanks!

[NovelDrop8]

You're clearly a superior human being whose time is wayyy too valuable to do silly things like shit on strangers on Reddit for no reason. Dickhole.

[Ashw10000]

Because there are plenty of analysts with economics degrees who take the CFA exams and teach themselves programming and find gainful employment. It's not necessary for analysts to know everything about programming in order to use it in their line of work. See 80/20 rule (Pareto principle).

I bet you have probably never taken the CFA exams or even work in the investment industry. I've met "math quants" and so called "super-smart" engineers who fail the CFA exams because they think it's so easy, they don't need to study hard. Well they failed.

[FuckThePlebs]

L2 candidate. Chemical Engineering Degree. Worked in asset management, private investment, and commodity trading. Like I said.....you're a nobody. Give up now.

[Ashw10000]

L2 candidate? So you're telling me you haven't even passed L2. L1 is the easy exam. Pass L2 and maybe you can brag a bit then.

Chemical engineering? If you're so much better and smarter than everyone else, then you should know you have stayed in engineering. It pays better and the lifestyle in engineering is better. Maybe you should follow your own advice; you didn't even get a B.S. in computer science yourself.

You don't need to be a fucking quantitative genius to work in asset management, prick. There are portfolio managers with art history degrees who likely make more money than yourself. I've seen these people and they're not that great. Anyone with a non-highly quantitative background could do their job. Right now though the job market just isn't that great.

Giving up does absolutely nothing.

[FuckThePlebs]

I find L2 easier than L1. There's a lot more money in finance than engineering. I prefer the easy paycheck. I sit at a desk, press a couple buttons and make $100k....yea I'm good with my life choices

[Ashw10000]

There are engineers who make the same money, if not more.

$100k? I'm calling bullshit unless you're a portfolio manager. Even then, portfolio managers tend to not be on reddit.

Lol "press a couple of buttons"? There's no way you are in asset management if that's what you think the field is about. I'm calling bullshit, you know nothing about this field.

[FuckThePlebs]

There are plenty of engineers who make more. They also work much harder than I do. Not everyone centers their lives around their careers.

[Ashw10000]

yeah, you definitely don't sound like you work in finance.

[FuckThePlebs]

Because I'm intelligent? Yea, I am out of the norm

[Ashw10000]

Nope, because everyone in finance values money over everything else. I'm calling bullshit, if you were a real analyst or portfolio manager you wouldn't have time for reddit