Dark Secrets of Data Science Which You Should Know
Data Science is now hailed as the sexiest job of the 21st century with hundreds of people having the desire to become a data scientists. Although data science, is a buzzword, very few people know this technology to the core or understand it in its true sense. This is the reason we have curated this article that will take you through all the dark secrets of data science.
Data Science is a revolutionary technology that grabbed several eyeballs in the recent few years and has become one of the most desired fields to make a career in. Everyone is talking about it or at least knows about the usefulness of data science in both the current world as well as the future.
Despite a lot of people showing interest in data science, it is very important to understand all sides of data science. Indeed all the benefits, advantages, and capabilities are discussed in many forums,?data science blogs, and communities, there are some of its dark sides too which you should know and understand before taking your next step.
Beyond all the success stories, in this article, we bring you some of its darker sides to paint a clear picture for all the data science aspirants and everyone seeking to become a master of this trending technology.
But before going directly to its darker side, let’s first have brief insights into the data science itself.
What is Data Science?
Data Science is one of the most trending computational fields which has successfully revolutionized numerous other fields single-handedly. Basically, it provides a foundation that helps computers to solve a given problem.
From drug design to the?banking sector?to real estate to android applications, data science has its own advantages which give them the extra edge. It might be easier to get your fundamentals about data science cleared if you check out?its explanation in an illustrated form.
In very simple words, which probably you have assumed or heard from others, data science is generally the study of data. However, to your surprise, data science is not just about designing and training the most advanced Artificial Intelligence and Machine Learning algorithms by using the data but to finding the right data.
Data Science involves the extraction,?visualization,?analysis, management, and storing of the data to generate insights from it. These insights help individuals, companies, or organizations to make super-powerful, precise, and efficient data-driven decisions.
This field is a multidisciplinary field with roots in computer science, math, and statistics, and involves both structured and unstructured data. Instead of just deriving insights from the data, according to a study, any?typical data scientist spent 79% of their time collecting, organizing, and cleaning the data. In regard to this percentage, about 60% of the time is spent on?data cleaning?and organizing while the other 19% for collecting the data.
I hope, it is sufficient to give you the basics of data science. Now let’s go to the most awaited section of this article,?“the dark secrets of data science”.
What are the Dark Secrets of Data Science?
Like every coin having the two sides, data science also has certain eyebrow-raising questions or I would say not so good sides of data science. Below are some of the dark sides which are enough to neutralize its hype of usefulness for the better world.
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Obvious data science discoveries
Currently, most of the data science-related discoveries are too obvious. For instance, when any hospital used data to look for different causes of error by the doctors, they found the lack of sleep as the main culprit. Or in the case of banks, when employees tried to predict loan defaults, they found the names of all the people having nil or low savings are more likely to default on their installments. These and some other predictions like tall people are more likely to hit their heads are too obvious to predict or know.
The majority of the predictions that data scientists are currently making are too obvious. If we can already make certain predictions then why give our extra effort, time, and money to doing it by collecting and visualizing the data? Will it be worthy enough?
However, some scientists or big organizations are actually evaluating these predictions in order to study the more specific or subtle details like predicting the future onset of any particular disease but this often requires a lot of data, and its analysis ultimately demands more time, money, and effort.
Finding Nothing
Sometimes, despite doing all the hard work, data scientists find nothing i.e. no meaningful insights or patterns after the data visualization. Biologically, human minds are good at finding patterns even when there are no patterns present. In the case of data science, a lot of questions that pop up in the mind of data scientists are meant to validate connections noticed by the human brain. Sometimes they find something and sometimes they don’t find anything.
Although, no result or negative result is also a valuable result of any work it is often unsatisfying for the data people doing all the hard work. Often these people end up with the conclusion that they might have missed something and be skeptical about their victory for nothing.
Harder to find answers
Sometimes, statistics-based answers can be tricky and harder to find than we generally think. This happens mostly when data scientists or any individual uses sensitive statistical methods, small data, or sample sizes or are biased about some data ultimately making the finding or answers more likely to be wrong and unreliable.
The solution to this is very simple, use significantly larger data and reliable statistical tools or methods. Large sets of data help in making subtle and minute predictions that cannot be predicted in a normal scenario. These subtle predictions can be a game-changer in cases where the value of understanding is very less or nonexistent such as a certain diagnosis in healthcare, equity trading, or any other field.
But the cost to analyze or even gather large sets of data can be very high which makes it harder for smaller or even regular organizations to afford. Because of this, various organizations are reluctant to spend their money and resources unless they are very sure about the output.
Algorithms imitating the past
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