The entire world is abuzz with data science, machine learning and data analytics! With these new-age learning techniques growing at an astronomical pace, companies and businesses are looking for professionals in these fields to sift through their goldmine of data to generate actionable insights for making better business decisions.
But what is that which differentiates also these techniques? We will try to dig deep and learn the difference between data science and data analytics in this article.
All thanks to the patterns and result-oriented actionable insights that businesses can glean today, big data has become a major component of the tech world today. However, generating those results from large data sets requires the knowledge of specific tools and techniques that are all interrelated but different from one another. Data Science and Data Analytics are two such techniques that are interrelated but offer different results and pursue different approaches to generate those results.
To start with, it is pertinent to understand that just like science, data science is also a multidisciplinary field. It deals with finding actionable insights from large volumes of raw and unstructured data sets. It unearths hidden information from the data sets like hidden patterns that we don’t even realize that we don’t know, but are crucial for our businesses. The data science experts use different tools and techniques like predictive analytics, machine learning and statistics to unearth such informational insights from large data sets. They try to extract solutions from the data sets to the problems hitherto unknown.
Data Analytics focuses on the technique of processing and performing statistical analysis of the existing data sets. The data analysts focus on creating methods to capture, process and organize data to unearth patterns, trends and actionable insights for solving the existing problems. Simply put, data analytics is used to find immediate answers or solutions to the problems that we know and want to resolve in order to make efficient business decisions. More importantly, it focuses on finding results that could find a resolve and make improvements immediately.
Although many people use these terms interchangeably, they are two unique fields of study, interrelated yes, but cannot be synonymously used. The scope of each of the field is very different. While data science is an umbrella term that is used for all the different fields used for mining large data sets in order to produce actionable insights, data analytics, on the other hand, is very different. Data analytics is much more focussed on its approach. So, on one hand, data science is a macro approach, data analytics is micro. The goal of data science is to ask the right questions, while the goal of data analytics is to look for the actual actionable data. The major fields for data science are Machine Learning, Artificial Intelligence, Search Engine Engineering; on the other hand, the major fields in which data analytics is used are industries like healthcare, travel or gaming industries that have immediate data needs to make their operations more efficient and revenue size wider!
Another area of difference between the two is the question of exploration. While data science does not concern itself with finding answers to specific questions, data analytics is largely about that. While data science is relegated to exposing the insights hitherto unknown, data analytics is operated to data sets to uncover insights that would provide solutions to the problems that are known.
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Now that you know how relevant these new-age techniques are, you may want to consider upskilling yourself! Career Launcher offers a detailed course on data science and big analytics in collaboration with IFMR/KREA. Check out the course details here. Get in touch with our expert counselors to learn more about the course offerings!