So basically i am also now taking a Python course (but its not aimed at Data Science). Its more basic programming for practice and basics.
I wanted to ask you if you think I should just pickup some data science course next? or is it better to ‘solidify’ my base because right now i just learnt it in 13 hours of facetime over 2 weeks. (but havent had much practice)
I also took courses and read books and tutorials about Python and R. The problem is once you go past the basic stuff it’s hard to keep practicing and improving if you don’t constantly analyze data as you would in a job with real life problems and data. I always end up forgetting most of it till I take another course but I never reach a good level.
I took basic class first then data science for finance. The learning curve was steeper for the second class compared to the first but it was also more interesting. I try to keep up by going on to Youtube… I have also been trying but not successfully to somehow use python at work - goal for now is to extract data from either 10k or bbg terminal to my excel spreadsheet. Then on the fun side, do some statistical analysis on the market.
make clones of sites/software that you like and use, not for profit, but just for the practice
My problem is with data science. I don’t have ideas to analyze data everyday
9-week MIT course that starts today:
https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-11
P.S. And if you find the learning curve too steep, supplement by:
https://www.edx.org/course/introduction-python-absolute-beginner-microsoft-dev236x
and
https://www.edx.org/course/introduction-python-fundamentals-microsoft-dev274x
Depends what you’re comparing it too I guess. 70k-80k is above national average, if you’re “good” i.e can talk through your ass without getting caught you’ll get a 100k easy.
If you’re slightly better than good i.e know what algorithm to use for what purpose and the difference b/w a decision tree and a random forest and how to use things like Spark (more efficient R) you’re looking at 140-170k. If you have enough soft skills to be the bridge as well 200k. Start managing people and 200-400k.
It doesn’t compare to Finance obviously where people are getting 400k before 30 but not many things do.
I also fail to understand why Data Science is going to be such a big disruptor to this sector, it’s basically linear algebra except you don’t even need to know th working behind it because one line of code in Python or R does it for you. Quants have been using these techniques and languages in addition to more advanced techniques like Stochastic Modeling for the better part of a decade.
thought you were already rollin in dough by the way you smacked down studdmuffin in the other thread 
I currently roll in “ok” amount of dough because I kept going forward in the past starting from high school, to college, and to the present…Why stop now?
Well, people who want to get CFA designation should consider study computer science instead.
Just because someone can program, or is a “data scientist” / “quant” doesnt mean that you can beat the market. Investing is a relative game, and almost no-one consistently outperforms all the time. Thats why quant funds arent doing well this year, and they as of yet dont have a clear track record of outperforming discretionary managers either. Sure, passive will be larger. Quant maybe (but currently its more of a marketing pitch that performance chasing institutions will pick up). But there will always be space for long-term concentrated public investors with a PE mindset. In a downside scenario discretionary stock picking will be back down to small niche shops with the $100bn+ managers only being passive/beta. But given the fee structure (% based, automatically get rising profits just by aum / market growth) + operating leverage (fixed costs, no incremental capital, can do everything with 1-2 investment staff) this business can continue to be highly lucrative if you enjoy it. Theres a lot of fat in this industry that is long overdue trimming, kinda ridiculous that sell side analysts are still making multiples of most other jobs including tech. Almost any mega-fund cant justify their fees and the sell side is almost entirely useless and bloated.
* i would also add that most of the quant strategies suck. there are a handful of oligopolies that can find unique true alpha factors (renaissance, two sigma, aqr) and the rest is just data mining nonsense. mostly just sticking to basic factors with sufficiently large cross sectional datasets work best (quality, momentum, value, carry). you dont need to be a uber quant to implement those. vanguard can do it easily, just need the distribution and technological infrastructure. long story short, you dont get into renaissance by being a cfa and then doing programming/stats because its the next hot thing that is on wsj. you have to actually have enough passion to do a 4y phd in these fields to have a real competitive edge that can allow you to make real money.
errrr MOST active managers suck.
Real competitive edge in quant strats come from how cutting edge technology your firm uses not your educational attainment or your codes.