What Is Data Science? Importance Of Data Science, Lifecycle
- To understand Data Science, it is really important to first understand the DATA that is being talked about here. Click here for a complete guide.
What Is Data Science?
Data Science is the process that combines statistics, scientific methods, and algorithms to derive only meaningful and important insights from a ginormous pool of data. It is an interdisciplinary field whose true foundation lies in Statistics, Mathematics, Computer Science, and Business. Hence, it becomes a little difficult to understand what exactly Data Science is and what makes Data scientists one of the coolest professions today. What to do with so much of this data? How to use it for the benefit of one and all? What are the real-world applications of the data? These are the questions that Data Science answers. The field of Data Science has been growing at a very fast pace and has become a sort of revolution for many industries. Hence, it becomes a little difficult to encapsulate it in a single formal definition. It is a very broad discipline that continues to expand as big data grows exponentially with time. However, in general, Data Science is the process of extracting and filtering clean and useful information from scattered and raw big data for the derivation of actionable insights. Now you might still be thinking, How is any of this a big deal!? Why is Data Science such a hype? This is because you just know the meaning of Data Science and not its application in so many diverse sectors. After all, you are unaware of the dire importance of Data Science in current times. So let us first know WHY DATA SCIENCE?
Why Data Science? Importance of Data Science
The importance of Data Science has increased rapidly lately. The reason is the transformation of the data. Earlier, the data used to be in a structured format, which was small in size and hence could be handled using simple BI tools. However, today most of the data is either unstructured or semi-structured, meaning the data is in the form of images, audio, videos, and other multimedia forms. So this data needs advanced analytical tools that are capable of handling huge volumes of such diverse data. This is only one of the many reasons for the popularity of Data Science today . The other major reasons are the profitability and efficiency that it offers to businesses in various sectors. Not just businesses, but even common people benefit from Data Science as it makes their day-to-day life easy. How? Let’s check it out.
Data Science – Applications & Uses Across Sectors
Data Science has made life easy by reducing time and effort in doing tasks when compared to a few years ago. Let us go through some other uses and the importance of Data Science across sectors. Netflix and YouTube are able to recommend the content you want to see next. This is based on learned preferences using preferential analytics. This is not just restricted to recommendations for movies and songs. You can also get recommendations on restaurants, hospitals, malls, etc, in real-time based on your location or any other preference. Your telecom operator knows when you are more likely to switch to a competitor. Banks use Data science methods for fraud detection . There are other sectors in which Data Science helps in anomaly detection (crime, hacking, diseases, etc). Data Science technology, along with genomic data, offers a deeper understanding of genetic issues and even reactions to particular drugs and diseases. Classification of data based on several filters. (for example, classifying emails into “important”, “spam”, “promotion”, “junk”, etc. Logistics companies like Blue Dart , DHL , and FedEx have discovered the best time and routes to ship and the best time to deliver with the best mode of transport, leading to cost efficiency as well as customer satisfaction. Forecasting to help elevate sales and revenue, and also methods for customer retention. Recognition for security purposes (voice, facial, text, and more). Pattern Detection includes financial market patterns and even weather patterns. Airline companies can now easily predict flight delays and notify passengers beforehand to enhance their travel experience.
What Type of Data do Data Scientists Use?
This is a very common query about what type of data the Data Scientists use in the analysis. The answer is both structured and unstructured data. Structured data comes in the form of Excel Spreadsheets and CSV files. To put it briefly, it is the kind of data that can be organised in rows and columns. Therefore, it is usually the written data that is either text or numbers. For example, Client data and spreadsheets with transaction information. Unstructured data, on the other hand, is everything else. It includes images, audio and video files, other multimedia files, and any other type of data that exists. Unstructured data represents more than 80% of all enterprise data.
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