TOP 3 skills necessary for a successful Data Scientist
The shortage of data scientist is becoming a serious constraint in some sectors. — Harvard Business Review (2012)
This is the quote from the 2012 Harvard Business Review article by Thomas H. Davenport and D.J. Patil. This article starts with the story that took place on LinkedIn late in the 2000s. HBR article clearly depicts the importance of data scientists in a company’s & startup’s ability to stay competitive enough in the big-data era. As you will start reading that HBR article, you will start catching some patterns of time intervals — you will find out that for the 1980s, the sexy job was quants, in 1990s computer scientists, and with the rise of big-data trend, the title of the sexiest job is getting Data Scientist.
Data Scientist Rockstar—Who is he?
Let's start with the fact that the Big-Data era has just started to gain momentum and not all universities have presented ML/DS programs. As we already know - formal education is not what makes someone a Data Scientist Rockstar, but however, it builds a foundation in understanding fundamental things in Machine Learning and Data Science. But according to different resources and especially this article, over more than half of the Data Scientists (88%) have at least a Master’s degree and about 46% of them have a PhDs. This fact speaks for itself.
Of course, in the rapidly developing world, formal education becomes a very long-term investment/dedication as compared to some existing online 6–12 months of Data Science Bootcamps. But still, formal education is what makes someone aberrate from normal-good data scientists. Moreover, the theory is not everything and most data scientists go beyond class homework and grab some practical projects, and start to implement them.
Let’s assume that the data scientist’s lowest bar is to ability to code and understanding the math. That’s what data scientists should be ready to enter with to market. But ask yourself again, what does a data scientist do? Code? Explore useful insights within the dataset? Communicates with stakeholders? Does he advise business owners? All together? Yes! All together! And again remind yourself that the lowest bar is the ability to code and understanding math behind DS, but what puts a data scientist in a positive light is that its ability to explain what they found within the given dataset to their business owners and stakeholders. Important to notice: today, knowledge and experience in different frameworks and technologies are becoming standard skill sets that recruiters hunt for.
Data Scientist work is more creative work than scientific or technical. Here is some supportive argument to this:
The knowledge of framework/math itself does not give a competitive advantage in the market - curiosity is the key. Swimming through vast amount of datasets requires extraordinary curiosity. No another way.
Today, most of top Data Scientist emerged from other science fields like finances, biology, physics, or from other technology backgrounds like Software Development. The reason of why some of these guys jumped to Data Science field may lie in the similarity of some features of their work with the work of a data scientist. Just imagine yourself — what does experimental biologist do? He also designs some hypothesis test, gathers data, conduct several experiments and communicate the results.
TOP 3 skills needed for Rockstar Data Scientist
I will not include coding and math experience as it is assumed must-have skill set, but instead I will list the qualities that most recruiters hunt for:
1 — Communication skills
Short As data scientist start swimming in dataset, he probably will find some insights that potentially can help company make more revenue, but what he will find is important to explain to business owners and explain why some patterns in dataset led to less revenue and why some patterns will lead to more revenue. So here, data scientist acts as consultants and making the last decision remains with business owners.
2 — Curiosity
Curiosity is what data scientist helps to keep on track — motivates to find more patterns and asking fundamental questions trying to dig deeper to the root of problem. And thus
3 — Clear Understanding of Business aspects of startup/company.
Here you need to understand that every suggestion or interpretation based on insights data scientist explore, have significant impact on company’s near future. Usually the data has all answer to the questions have been stated, that's where data scientist is helpful — find those answers/patterns within data. And any bad predictions or analysis based on dataset can potentially harm the company’s growth and revenue plan. Data Scientist is expected to understand all standard business processes and understand their impact on each other.