BIG DATA VS DATA SCIENCE: KNOWING THE DIFFERENCE BY UNDOING THE KNOTS
Source – https://www.analyticsinsight.net/
Big data and data science technologies have data as their core content and perform various actions
Ever wondered whether to choose big data or data science? If you are into data and a tech geek, you might have come under such a dilemma at least once. In the digital world we live in, data is increasingly becoming the most valuable asset for organizations. It won’t be a surprise if it crosses the price of gold one day. But to explore every bit of data, we need more than just the basics. Big data and data science technologies have data as their core content and perform various actions.
Even though big data and data science are two different technologies, they are interlinked with each other on the grounds of data. Both technologies play a big role in digital evolution. More and more companies across various domains are adopting big data and data science to enhance the routine. Since data is rapidly transforming the way we live and communicate, big data and data science application help collect, sort and study data to improve organizations’ performance. Data science is an extension of statistics that deals with large datasets with the help of computer science technologies. On the other hand, big data engages with the vast collection of heterogeneous data from different sources. In this article, we’ll undo every knot and reveal the difference between data science and big data.
Big data represents a large set of data, both structured and unstructured, that inundates business on a day-to-day basis. The data is very large in size that none of the traditional data management tools can store it or process it efficiently. But the massive amount of data can be used to address business problems that humans find difficult to tackle with simple calculations.
Data science is a domain that deals with vast volumes of data to derive meaningful information and make business decisions. Data science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from raw data. The term ‘data science’ was coined in 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data.
Big data holds diverse data types generated from multiple data sources. Henceforth, big data approach can’t be easily achieved using the traditional data analysis method. Instead, unstructured data requires specialized data modelling techniques, tools and systems to extract insights and information as needed by organizations.
Data science is a specialized field filled with intelligent data capture techniques, data cleansing, mining and programming to prepare and align big data for intelligent analysis to extract insights and information. Data science is comparatively a challenging area due to the complexities involved in combining and applying the different methods, algorithms and complex programming techniques to perform intelligent analysis in large volumes of data.
Big data in financial services: Financial services like credit card companies, retail banks, private wealth management advisories, insurance forms, venture funds and institutional investment banks gather a lot of data every day. In order to make the data valuable, they use big data to address common problems. Unfortunately, the data are multi-structured data living in multiple disparate systems, which only big data can manage. Entities perform customer analytics, compliance analytics, fraud analytics and operational analytics to mitigate financial issues.
Big data in gaming: Online sources are the big generator of data. Especially, the gaming industry is a massive creator of big data. A single frame of an online game can require 100mb of data to render. Think about how much data is generated every day just in the gaming industry. Yes, it goes beyond uncountable.
Big data in healthcare: With the healthcare sector gaining more attention, organizations and executives working in the industry find technology as a solution to accelerate the medical processes. Hospitals and medical service providers store big data to analyze and perform tasks like track and optimize patient influx, track the use of equipment and medicines in the facilities, organize patient information, etc.
Data science in recommendations: Recommendation systems are increasingly becoming common in the modern world. We come across recommendations systems every day and find them amazing. Even before we look for more content, the online recommendation systems suggest what we might like. This is used as a marketing method of promoting products to consumers. Scores of companies are already using recommendation systems to enhance their sales.
Data science in advertising: Digital ads have click-through rates that differentiate them from traditional advertisements. Henceforth, flashing the right ad at right time and right place is very important in online advertisement campaigns. Digital marketers use data science algorithms to display banners and digital billboards where it gets maximum viewership.
Data science in internet search: Since internet is the prophet of the digital society, we search for everything online. Fortunately, we get relevant content most of the time. Data science is being applied to online search engines to make us get the outcomes we expect for. It goes through our previous browsing history and filters the results according to our routine search.
Big data engineers’ core functions are similar to that of data engineers’. Data engineers should design the architecture of a big data platform, maintain data pipeline, customize and manage integration tools, databases, warehouses, and analytical systems, manage and structure data, and set up data-access tools for data scientists. Some of the common big data careers are,
• Big data engineer
• Big data analyst
• Data visualisation developer
• Business analytics specialist
• Machine learning scientist
Data scientists work closely with business executives to understand their goals and determine how data can be used to achieve those goals. They design modelling processes, create algorithms and predictive models to extract the data the business needs, and help analyze the data and share insights with peers. In general, data scientists are entitled to ask the right question to begin the discovery process, acquire data, clean and store it, explore data analysis, apply data science techniques, etc. to improve business functionality. The most common careers in data science are,
• Data scientist
• Data analyst
• Data architect
• Data engineer
• Business Intelligence specialist