Got Any Questions?
Get in touch via email
Data Science helps businesses make more informed and data-driven decisions.
With or without your knowledge, data science has sneaked into our lives to the point of being essential. But not many know what this word, or the job of data scientists, actually entails.
Before delving into the meaning right away, you might want to know that data scientists have one of the most sought after and high paying jobs in the world. As it is now, businesses are still only scratching the surface of data science for their benefit.
The demand for data scientists is not something that is going to die away in the near future. All of these points add on to make one thing clear: pursuing a career in data science is well worth the effort. So, let’s dig deeper into data science to figure out if it is the right career for you.
First, I’ll give the textbook definition of data science: data science analyzes data to draw insights to assist in decision making. Now, if you haven’t stumbled across the term data science even by an accident before, this definition wouldn’t make much sense. That is why, to really understand what data science is, you have to understand the reason data science came into play at all.
Think about how Google shows personalized ads to its billions of users. Google personalizes the ads depending on your browsing history, how you interacted with ads in the past, and the ads users with similar interests like yours found interesting. This personalization extends to every single Google user. Even if you consider the amount of data that has to be processed, this would become an unintelligible task with only traditional analytical methods at hand. This is where data science becomes useful.
It takes a large amount of work involved with data processing and analyzing off human hands and automate the process, and at the same time, it helps to identify the best methods to analyze and extrapolate these data. So, as long as data science works its magic on this large amount of data, all we have to do is feed the data to the system and wait on the other end for the results to come out.
A lot of people, sometimes even those with a technological background, make the mistake of assuming data science is equivalent to machine learning. Yes, machine learning is a part of data science, but there is more to it than just machine learning. Data science is a combination of computer science, mathematics and statistics, modeling, and a bit of knowledge in business as well. Everything considered, it’s safe to say data science is rather a large field that has a variety of uses and applications anywhere a large amount of data can be collected.
Remember how I mentioned that data science takes work off human hands? The job of data scientists is to apply data science to problems at hand and come up with solutions that take the work of human hands. Most people wrongly interpret this as just writing code, but it is not. The job description of data scientists expands far wider than just writing code.
Netflix analyzes user interests based on their watch history and uses the discovered insights to develop Netflix originals and its movie recommendation system. Way back in 2008, Google processed 20 petabytes of data per day to provide the optimum results for search queries (Imagine how much data Google processes now). Instagram analyzes user data to determine the targeted audience for sponsored posts. All these are nothing but the end result of what data scientists do with data science.
In terms of personality, data scientists are an investigative, rational, analytical, and logical bunch. Education-wise, having a grasp on computer science, mathematics, statistics, and business fields is essential to become a data scientist.
According to Burtch Works recruiting firm, 94% of data scientists hold at least a Master’s degree, 47% hold PhDs. Higher education seems to be a requirement to go all the way in the field. You can start with a degree in mathematics, computer science, or economics and continue up the education ladder. But none of this is set in stone. You can pursue a career in data science in your own style with a bit more research to help your way up.
The most common programming languages in data science are Python and R. Other languages like Java, Julia, and Scala are also used and useful in the field. Knowledge of SQL and writing SQL queries is imperative to deal with databases to store collected data.
Today, most organizations operate online in some capacity. This has made the task of collecting large amounts of data rather an easier task. When you look at the benefits of using data science to analyze these data for better decision making, it’s safe to say we are looking at a future where most companies, tech-related or not, will look to incorporate data science for maximum profits and productivity. Tech, e-commerce, security, and healthcare are some of the fields that are currently on the frontline with data science integrations.
In 2012, Harward Business Review named data scientists’ job as the “Sexiest Job of the 21st century”, considering the job demand and pay. Glassdoor lists data scientist in the 3rd place of “50 best jobs in America for 2020” with a median base salary of $107,801. Again, considering how most organizations are still scratching the surface of data science, a career in data science can only become more appealing in the future.
Because of the growing amount of data available for analyzing and the evolution of people’s needs and wants, data science has become a field that presents endless possibilities to the ones who love to discover patterns and structures in a mass of unstructured data. As long as you are curious enough to question why something is the way it is, you have the capacity to become a successful data scientist.
Given how enticing the prospects of data science are to organizations, you are very unlikely to be left stranded when pursuing a career as a data scientist. The cherry on top of the cake is, of course, the high salary data scientists earn. So, I would say that it is high time that you start planning to become a data scientist and discover the secrets hidden in masses of data.