Why Data Scientists are Needed Everywhere?
Last Updated on May 24, 2022 by Editorial Team
Author(s): Ibrahim Israfilov
Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.
I have long been asking myself, why data science is one of the hottest jobs of our century? I found the answer to this question while discussing linear regression with a Ph.D. researcher in Chemistry who is conducting research to develop a bio-plastic. So the answer lays in the scalability of the tools and techniques used by statisticians. In our case, I will take an example the linear regression and its power!!!
I would reformulate the title of the article as “What Chemistry has in common with Finance?”. The answer is data! Each of the sectors has the data to study and the data is studied through statistical tools.
How do chemistry scientists use data and how does a financial analyst do?
Imagine you want to find out the reaction temperature of the material and want to save energy by heating the temperature less and obtaining the same results. You have your relative observations. You run your statistical software (Usually R) to plot the observations and draw the OLS (Ordinary Least Squares: Is the line that minimizes the error between observation data and the estimated data.). You find out the regression is linear and create a linear model. Then you find what is the least value of temperature acceptable to get the same so you could use less energy.
Another example is to find what variables trigger the excess return in a stock market with a regression.
The formula to find it is Ri=α+βrm+e
Ri- Excess return on security
α– return when the market is not volatile (Intercept)
β- Is the market volatility (Slope)
rm- explanatory variable (The factor that mainly influences the Ri)
e- unexplained return (Residual)
This example shows how the observations are structured and what the variables mean for a financial analyst.
This is an example of the R-generated code where we see the CAPM model used in Portfolio Management. We have an intercept (alpha). This is when the market is not influenced by any movements and it’s a kind of risk-free zone. The positive Alpha is attractive for investors and the security with positive alpha is called underpriced.
We also have rmrf (market risk premium= expected market return-risk free rate) which is our beta of 0.66 which is a slope of our regression.
For an investor, this means when the variable rises by 1% we are going to get the return of an extra 0.66%, and similarly when it falls by 1% we are going to get a -0.66% of drop-in return.
Problems are different but the solutions are similar
So in the end, even if the problems were completely different in each field but the tool used for the solution is the same. Perhaps, that’s why the data scientist is the sexiest job of the 21st century.
Note: I don’t have a background in chemistry and can’t check the correctness of information about chemistry problems provided by a Ph.d. If you think there should be corrections done please let me know in the comments.
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