Data Science?
What is Data Science?
Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.
Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data.
Because of the large amounts of data modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.
What is the use of Data Science?
With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. To make most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as trending job of 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist.
Components of Data Science
Data Science consists of 3 parts namely:
Machine Learning: Machine Learning involves algorithms and mathematical models, chiefly employed to make machines learn and prepare them to adapt to everyday advancements. For example, these days, time series forecasting is very much in use in trading and financial systems. In this, based on historical data patterns, the machine can predict the outcomes for the future months or years. This is an application of machine learning.
Big Data: Everyday, humans are producing so much of data in the form of clicks, orders, videos, images, comments, articles, RSS Feeds etc. These data are generally unstructured and is often called as Big Data. Big Data tools and techniques mainly help in converting this unstructured data into a structured form. For example, suppose someone wants to track the prices of different products on e-commerce sites. He/she can access the data of the same products from different websites using Web APIs and RSS Feeds. Then convert them into structured form.
Business Intelligence: Each business has and produces too much data every day. This data when analysed carefully and then presented in visual reports involving graphs, can bring good decision making to life. This can help the management in taking the best decision after carefully delving into patterns and details the reports bring to life.
Data Science Career Opportunities
A Data Scientist, according to Harvard Business Review, “is a high-ranking professional with the training and curiosity to make discoveries in the world of Big Data”. Therefore it comes as no surprise that Data Scientists are coveted professionals in the Big Data Analytics and IT industry.
With experts predicting that 40 zettabytes of data will be in existence by end of 2020, Data Science career opportunities will only shoot through the roof! Shortage of skilled professionals in a world which is increasingly turning to data for decision making has also led to the huge demand for Data Scientists in start-ups as well as well-established companies. A McKinsey Global Institute study states that by 2018, the US alone will face a shortage of about 190,000 professionals with deep analytical skills. With the Big Data wave showing no signs of slowing down, there’s a rush among global companies to hire Data Scientists to tame their business-critical Big Data.
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