Can Data Scientists Profit from the Market...Without Trading?
Before I dive into how to become a data scientist, let me first establish a few basic facts. The first of which is, what does a data scientist even do? The simplest definition is that a Data Scientist engages in the practice of collecting, analyzing, and interpreting data – aided by technology. That last bit is key, because advances in technology – particularly AI – are fundamentally changing how data scientists and statisticians process and analyze data.
So, where do data scientists work? And how can you make money as a data scientist?
One thing is for sure – data scientists are in high demand, and that demand looks only set to grow from here. As it stands today, data scientists are primarily found in the tech industry or in companies with a well-developed IT component, since those are generally the types of companies that collect, store, and analyze huge volumes of data. Traditional companies have not quite caught up in the world of large-scale data analysis, but most companies that want to survive in the 21st century know that they need to do so fast. Data is king.
There is another way people can make money as data scientists – using data analysis to invent your own ideas, strategies, and insights. And then selling those ideas. This actually happens more often than you might think. Many initial coin offerings (ICOs) in the cryptocurrency world are issued by teams of developers, computer scientists, and data scientists who have invented a new process, protocol, or strategy for performing a function better and more efficiently.
In the world of finance, Tickeron offers a platform for data scientists to sell their ideas, in a marketplace called MALL. Imagine a situation where a data scientist creates an algorithm that generates trade ideas based on analysis of say, a massive data set of corporate earnings. The data scientist then keeps statistics on the performance of their algorithm, and finds that it is successful 60% of the time. That is an idea worth selling! In Tickeron’s MALL, a data scientist can do just that.
What Do I Need to Learn to Become a Data Scientist?
Interestingly, the profession of Data Scientist is so new that many Computer Science degrees at major universities do not yet offer Data Science as a major. As such, many Data Scientists are self-taught and it is possible to become a Data Scientist without a formal degree.
First off, if statistics isn’t something you’re interested in, maybe Data Scientist isn’t a great career choice for you. While being a Data Scientist and a Statistician are not the same thing, a strong understanding of statistics is probably the most important skillset for Data Scientists. To put it simply, all of the programming, mathematical, and software skills in the world will not help you if you don't understand how to analyze and report on statistics accurately and fairly. If you don't understand the theory behind confidence intervals, appropriate sample size, and statistical significance, you are not likely to make accurate assessments and claims.
If you enjoy statistics and have a deep interest in understanding all of its nuances, then from there you’ll want to deep-dive into each of the following subjects:
· Linear algebra, including multivariate calculus
· Regression, including the ability to handle both linear and nonlinear models
· Probability theory, including Bayes' Law and Central Limit Theorem
· Numerical analysis, including time series analysis and forecasting
· Core machine learning methods, including clustering, decision trees, and k-NN
· Programming, with an emphasis on writing scripts to automate the process of cleaning and preparing data for analysis. Python is a dominant language in the programming field, followed by the R programming language.
The best part is, most of these functions can be learned online through free or low-cost courses. You just have to be willing and committed to put in the time.
What are Some of the Day-to-Day Functions of a Data Scientist?
Data scientists can work in a variety of fields, and can have functions that range from simulating the spread of an epidemic, to analyzing stock charts for a hedge fund in search of technical trading patterns, to using data, models, and analytics to more effectively market a product on the web or social media. Anywhere that data is collected, it can be used to more effectively carry out a process – period.
As I mentioned before, the role of Data Scientist is becoming more and more prevalent in the world today, as data is recognized as the most effective tool needed to run a business more efficiently. Data scientists are the ones who take the data, analyze it, and interpret it so the company can get better and do better. They’re fundamentally changing the way businesses operate.
In the world of finance, given the near endless amount of data available in the capital markets, data scientists have the ability to invent new trading techniques and strategies that can change the way people invest. And Tickeron wants to give these innovative data scientists the opportunity to share their discoveries and ideas to the world.
Contributor
Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics from Moscow University and has extensive experience as an entrepreneur, investor, manager, and mathematician. His professional expertise is in applied mathematics, mathematical modeling, system and pattern analysis, and software and hardware system integration. He has served as the CEO of several hi-tech start-up companies and nonprofit organizations, which has given him proven capabilities in business strategy for high-tech start-up companies, market assessment, company formation, team building, product development, marketing, and sales. He has published numerous articles in journals and magazines on related fields. As a retail investor, he spent 15 years developing his proprietary trading and quantitative algorithms (now Tickeron’s A.I.), which brought him significant returns in trading the stock market. His current work and goal in founding Tickeron is to bring professional, sophisticated stock market analysis capabilities to retail investors via an easy-to-use interface.
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