Boaler, a colleage and friend of Carol Dweck (of Growth Mindset fame), is a professor of mathematics education at Stanford University. In this book she teaches people how to think about Math education, and about approaching problem solving in general, positively without limiting oneself. I found this quite eye-opening, somewhat comforting, and honestly quite inspirational.
The author of this book discusses a framework he has used to improve his life situation. 7 brief but pertinent questions asked, and decisions made helped him move from being a homeless teenager to a better station in life. The prose is honest, and resonates with the reader. I liked this book, learned from it, and think it could benefit others as well.
This is a beautiful and very powerful book with stories on neuroplasticity or the ability of the brain to evolve to 'fix itself'. The stories of people with various brain-related issues and how they manage to recover are all very touching, and give you hope that you too can evolve your way to a brighter future no matter what troubles you might be facing.
Jack Welch, erstwhile CEO of General Electric, the company founded by none other than Thomas Alva Edison (of light-bulb invention fame, though two others also invented it at about the same time, but were less successful commercializing it), along with his wife Suzy Welch present some MBA-style lessons on areas ranging from strategy to finance, coloring these with real life examples. I really enjoyed this book. There are videos embedded in the digital version which can add to learning enjoyment for the discerning student. Recommended.
Stuart Diamond knocks this one out of the park. I've taken negotiation courses in B-school, and have read and practiced the skill extensively in real life. But his methodology and the way he teaches it is absolutely phenomenal. The examples are nicely presented, and the website that goes with this book (you can search for it online) showcases the model brilliantly. Like he says, (my paraphrase) like it or not, all of us negotiate for something every day of our lives. If we know how to do this well, we and those around us will have better lives. Must read.
Over the years, I have often wondered what the difference is between quants and data scientists. These are both popular roles in Financial Institutions of various kinds.
I've worked in both roles at large global banks, as a quant analyst on a FICC desk on the buy side, and as Head of Data Science doing global securities research on the sell side. I've also spoken with a number of smart people in Finance about the difference, and usually the answer is that there isn't too much to distinguish the two roles.
Similarities
The similarities are obvious:
Both roles require a deep understanding of advanced mathematics, statistics, finance, etc.
Both require an ability to code, and code well, potentially in Python, R, C/C++/C#, maybe Julia
Both require that you are able to model things to reflect real world realities with good fidelity
Both require that you are able to articulate and explain things to non-quantitative audiences well
Both have some of the hardest interviews to get in
But what about the differences?
As a data scientist, I've given talks at the Quant Forum (gathering of all quants) at a bank on data science methods, and it was the greatest fun I had giving a presentation. I felt right at home there. And received several collaboration requests from that session. So the overlap cannot be understated. But these two roles are different, and the differences only became clear to me as I read this book:
Dave Epstein discusses the central thesis of his book in this video
The Concept of Range, and the Different Types of Domains
In this book, the author explains that there are some "kind" domains that allow for deep specialization, where experience helps you make better decisions and more effectively deliver value. The more time you spend in the field, the better you get at it.
There are also "unkind" or "wild" domains where the opposite holds true. In some of these, having more experience might give you more confidence, but unless you are humble, leveraging old learnings in new problem contexts can lead to more errors i.e. experience doesn't really help in problem solving.
He illustrates this idea with several examples - from sports using golf and Tiger Woods as an example of a "kind" domain, and with tennis and Roger Federer as an illustration of the "wild" one. He further underscores his point with Chess - grandmasters can reproduce a board with several pieces exactly with only 3 seconds to see it IF the pieces are placed such that the scenario can occur in a real chess game. However, if random pieces are randomly placed, they cannot replicate the board unless given significantly more time to study it, and in the latter case total chess novices can do a better job.
Another example he cites is Music. Did you know that Yo-Yo Ma, the world-renowned cellist tried several other instruments before settling on the cello? This works because music is a "wild" field - you can pick up broad patterns that might work generally, but then again, might not. Then when you specialize in a narrow "kind"-er domain (that of playing the cello in this case), you try those patterns to go deep and excel, keeping what works and discarding the rest.
So how is all this relevant?
As a data scientist, even if you pick just one field, say finance, you develop and use lateral skills a lot. You may work with consumer spend data and basket analysis for retail, with Fed Beige Book data for global macro, with satellite data for real estate or oil/energy markets, with news data for other verticals, etc. You have a wide and deep knowledge, but you pick up sector knowledge as you go, working closely with specialists.
As a quant, you (usually) work on one desk, and specialize in one or two markets. You learn all you can possibly learn about those markets, and go as deep as you possibly can to identify, isolate (where possible), examine, model, and forecast particular variables and the relationships between them. You are a specialist in your sector.
A data scientist may not need stochastics, but should have the intellectual capacity to learn it. Most quants should know enough stochastics to be able to do their jobs well.
In Conclusion...
Both the quant as well as the data scientist roles in finance offer stimulating, intellectually challenging work. I guess in the end, your ability to make a strong positive contribution comes down to your preparation, expectations, firm culture, and the competence and quality of the team you work with.
How does this post match with your views? Please share in the comments below.
