If you're thinking of getting a data science job in an investment bank this year, it will help if you know how to code in Python. But it will also help a lot if you are intimately familiar with Excel.
Boris Serebrinskiy, a Morgan Stanley distinguished engineer and cloud data analytics and warehousing architect, has begun writing his own blog titled 'Data engineering by a pragmatist.' Alongside musings about skewed data and the patterns that impact the ease of parking a car in Brooklyn, Serebrinskiy reflects that Excel is still a crucial part of the data scientist's toolkit.
"I am using the most practical tool of any data engineer for Big-ish Data — Excel :)", he says as he tackles a list of names of 18,728 football players in order to analyze the skew towards names beginning with particular letters. Using Excel, Serebrinskiy is able to hash and modulo the data so that the skew is barely noticeable. "And, yes, I know and use Python too," he points out.
In his other blogs, Serebrinskiy argues that data engineering is an art and that the "ability to find brilliant solutions and discover them in unconventional ways is nothing short of the creativity of an artist."
He also points out that working in big data for a big bank can be more innovative than most people might suppose. Part of his job involves looking for and testing new databases, says Serebrinskiy. Within some limitations, Morgan Stanley can be quite open to new ideas.
"Being a large bank, we are influenced by several critical factors," says Serebrinskiy: data protection and security must be very high and open source software must always be backed by a commercial distributor. But Morgan Stanley can also be a first mover when it comes to adopting new database technology. "We often invest, develop, and actually use software from startup database companies one may never heard of until they go public," says Serebrinskiy, adding that working for a bank can be advantageous: "We have a very strong technology research department focusing on the startup space. "
When he's choosing a new database, Serebrinskiy says he has a fantasy rule of thumb: "10 times better [than the existing database] on the same hardware or 50x better on the new hardware.”
Serebrinskiy isn't the only one to acknowledge the importance of Excel and VBA. One incoming graduate hire at a U.S. bank told us familiarity with Excel, VBA and Python during his internship was a game changer. "Those three things will really make a difference to your ability to do real work."
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