Flip Your Perspective With These 15 Clarifications About Data Science
Since Data Science is a new kind of field that’s making inroads in a really exciting manner, the average Joe definitely has questions about it. It’s important to know Data Science Basics since it’s a recent field and people have more misconceptions about it. Let’s clarify what exactly Data Science is and clear all the misunderstandings while understanding Data Science Basics at the same time.
More data doesn’t mean more accuracy - Generally, people think that more and more data means accurate decisions and better insights. But that’s not the case. It’s how whatever data is available is utilized in the best possible manner that makes all the difference.
Data Science and Business Intelligence are same – For those who are not much familiar with the industry thinks that data science and business intelligence are the same. No! Business Intelligence deals more with the operational part of the organization where the focus is why, where and when. While data science is more of predictive analysis, it figures out who has gone wrong, what is accurate and what may go wrong in the future.
Lots of data is not required – Many small to mid-sized business think that lots of data is required to arrive at a powerful insight. However, this is a big misconception. Data in bulk is the goal; however, you don’t need millions of customers to extract insights. The data science is all about four Vs – volume, variety, velocity, and veracity. A proper balance of all four Vs helps one arrive at an accurate conclusion.
Talent and experience matter more than qualification – Generally it’s considered that highly qualified data scientists should be hired, preferably those who are holding PhDs. However, on any given day, someone having years of experience and talent score over anyone highly qualified.
Data scientists are not necessarily coders – It’s not necessary that data scientists are coders, however, they have an intense knowledge of AI, machine learning, statistics, mathematics, actual science and analytical tools through Data Science Training. Having a good knowledge of coding is just an added bonus.
Data scientists are tech guys – No they are not tech guys and they can’t repair laptop, fix a projector or explain the slow speed of broadband. They are more like innovators, inventors or artists.
Data science is business – Data science is not just about analyzing data and providing insights. It’s about giving inputs that help the business grow like anything. Data scientists think like a businessman because money is what matters in the end.
Data science is an aloof activity – No, data scientists are involved in every step of the business since beginning to end including product conceptualization, product development, marketing initiatives, sales target setting and identifying the potential customers who will consume the product.
Data science equals big data – While big data is all about collection, managing, processing of data, data science is about mining, transforming, storing, modeling, exploring and analyzing data. Data science is one aspect of the big data and both have different work zones.
All data has value – This is a very common misconception that whatever data is available with data scientists is useful and is of value. That’s not the case. Data scientists usually filter the data and use the most reliable one.
Data science is not only science – It’s an art as well. The scientist is also required to be an innovator, a creative thinker with a rich imagination and a visionary. That’s more than just being a scientist.
Data science is only statistics – That’s is the biggest misconceptions. Statistics is just a part of data science while other fields of studies play an equally important role.
Date science is not about collecting data – No, data science means thinking and analyzing the data and arriving at some solid conclusion that eventually benefits the organization.
Data scientists are developers – Being developers is just a part of the whole data science picture. It’s in fact for those who love data, have interest in quantitative methods and are passionate about exploring data things.
Data science is teamwork – People have the misconception that a data scientist sits at the office and does all the data work. No, mostly it’s teamwork with one collecting the data, one storing it, one organizing it and one that’s solving problems, giving insights by making use of the data.