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Assuming that you want to be at a hedge fund, I'd definitely go with Python. Check out the Python for Finance from Yuxing Yan. I loved the book as it is not academic but very practical and explains things very well - it is written by a financier and not a programmer. Then, there are couple of great specializations on Coursera - one from EDHEC, which reached you how to do quant analysis using Python, and a specialization on algo from NYIF, also on Coursera.

Tableaus is a great tool and I have done a lot with it back when I worked for a stock exchange, but for quants there is nothing like Python, if you ask me. Also, Tableau is a piece of cake - it will just take you a few days to learn how to create stuff life candlestick charts, heatmaps, and work with geodata, and even make beautiful dashboards.

“Destiny is a gift. Some go their entire lives, living existences of quiet desperation, never learning the truth that what feels as though a burden pushing down upon their shoulders is really a sense of purpose that lifts us to greater heights. Never forget that fear is but the precursor to valor, that to strive and triumph in the face of fear is what it means to be a hero. Don’t think. Become.”
 

I would actually say that for what you need Python, decent knowledge of mathematics (calculus, linear algebra, time series, stochatics) and statistics (all about distributions etc) is much more important than the knowledge of programming itself. You should first understand what you are doing, and only then go with how you do it. The book that I mentioned is teaching you Python from the basics, but you may want to take a couple of foundational courses on Python for Data Science - ones I have personally taken are from Coursera - IBM Data Science Professional certificate. They will also give you a refresher on statistics and matrix algebra.

“Destiny is a gift. Some go their entire lives, living existences of quiet desperation, never learning the truth that what feels as though a burden pushing down upon their shoulders is really a sense of purpose that lifts us to greater heights. Never forget that fear is but the precursor to valor, that to strive and triumph in the face of fear is what it means to be a hero. Don’t think. Become.”
 

C++ is significantly faster than Python, which is why it's used for HFT trading (since speed to buy/sell is extremely important0.

However, that comes at the tradeoff of extremely cryptic syntax (Python is basically regular English) and more code that has to be written.

An example of additional code is the following:

Suppose you want to purchase a chicken burger at Chick Fil A.

Normally you would go up to the cashier, and tell them you want Order 1, (Classic chicken burger). From that the meal preparer is able to immediately know which toppings and meat should go on the burger. This would represent python

However, for C++ you would need to say that you want Order 1, Classic chicken burger and specify what the ingredients of said burger should be (chicken, lettuce, mayo, ,bread, etc.)

Clearly the first case is easier to dictate, while the second is worse, but with coding the first runs a decent bit slower because the ingredients necessary have to be determined from the burger you wanted, whereas in the second case you explicitly said everything you wanted. 

Because of this Python is basically used whenever C++ isn't necessary. 

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Just my $.02, but really both.  Python is where you do the calculations / modeling / merging / all things data analysis / forecasting / that kind of stuff.  Tableau is where you can take the output and make it digestible.  Python has some libraries for making graphs / prettier output, but I think they are a PITA, and tableau is literally designed for building pretty reports.  Python is for when you're doing the difficult stuff, Tableau is for presenting.  It's a similar dynamic to excel and PowerPoint.   

 

Tableau and Python to me are very different tools that accomplish different things. Python has some data visualization capabilities that are excellent, but, especially for business-oriented roles, you can get pretty much all the way there with PowerBI and Tableau. Additionally, DAX (the language behind PowerBI calculations known for being clunky) is way easier to understand than Python for people that have 0 coding experience. Tableau is even more intuitive and easier to pick up IMO.

I consider Python a sort of "Queen of All Trades" type of tool, if you will, because it does a lot things really, really well but some would argue that other tools might be slightly better or more digestible for certain tasks. For example, Numpy/Pandas is phenomenal for a lot of spreadsheet manipulation and automating the creation of reports, but some might argue that Alteryx is simpler, and therefore faster, for the majority of data cleansing and ETL needs. For example, a really common stack in data analytics is SQL + PowerBI or Tableau or Lookr (some visualization tool essentially) + maybe Alteryx for automation of ETL. I'm working in analytics and rarely touch Python, honestly, because 90% of the time, the use case isn't there and/or the client doesn't understand wtf I did.

All of that is to say, OP, that it's not really an either/or, if you want to learn analytics. Depending on how deep you want to go though, and taking the average need case for the typical user of this website, I'd say spend way more time on Tableau and potentially SQL. I don't think Python is that useful for most business cases yet TBQH. I think Python is fun, but frankly, I've found my time I spent learning perhaps could have been better spent on PowerBI and SQL.

 

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