Big Data, Data Science and Data Analytics – A Short Discussion

Data rules the world these days! Digital data is growing at an exponential rate. With all the latest news going around about data leaks gives us a wide idea about the significance of data and data-handling. Let’s start by differentiating among the concepts of big data, data science and data analytics. Big Data It’s not always feasible to process the huge amount of data that exists using the traditional applications. It begins with the raw data which is not segregated and is not possible to store in the computer’s limited memory. Data Science All that’s related to data cleansing, preparation and analysis comprises the field of data science. It combines statistics, mathematics, programming, problem – solving and the ability to look at things differently. Data Analytics It involves the application of algorithms or other mechanical processes to achieve insights from the vast amounts of data. It’s used in companies in order to make informed decisions. The Applications: Big Data is used in different financial service provider companies like credit card companies, banking sector, insurance agencies, investment banks etc. It has various applications like in customer analytics, fraud analytics, operational analytics etc. Telecommunication sector requires new customers; they also need to retain existing customers. All these involve analysing the huge chunk of customer-generated and machine-generated data being created daily. The retail sector also uses big data in similar manner. Data Science is utilised for conducting internet searches wherein the search engines use data science algorithms to deliver results in a better manner within seconds. Digital advertising also uses data science to create display banners and billboards. Data Analytics is used by the health sectors, travel and tourism industry, gaming industry etc. The data needs to be studied thoroughly to chalk out a plan to provide best services to the customers. The desires and preferences can be analysed better and customized experiences can be created for the users of the services. Requirements to become professionals: Big Data: Analytical skills, creative mind, number-crunching ability, Knowledge in the field of computer science are essentials to become a big data expert. Data Science: In-depth knowledge of SAS or R, coding ability using Python, knowledge in Hadoop platform, SQL database or coding. Data Analysis: Programming skills using R and Python are essentials. Statistical skills, machine learning knowledge, data wrangling ability and data visualization skills are mandates. In the current times of high-alert on data-handling, these professionals are highly valued across the industry because data is the most valued item and its value is going to further rise in the future.

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