{"id":869,"date":"2018-02-20T13:22:57","date_gmt":"2018-02-20T13:22:57","guid":{"rendered":"https:\/\/www.digitalnest.in\/blog\/?p=869"},"modified":"2025-01-30T11:29:46","modified_gmt":"2025-01-30T11:29:46","slug":"difference-big-data-data-science","status":"publish","type":"post","link":"https:\/\/www.digitalnest.in\/blog\/difference-big-data-data-science\/","title":{"rendered":"Difference between Big Data and Data Science"},"content":{"rendered":"
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[et_pb_section fb_built=”1″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”]Data changes the way people live. According to the survey, the data is evolving each year every year and each day 2.5 GB data is being generated which is more than the human birth rate. The digital economy has unveiled the doors of the huge landscape of Big Data<\/strong><\/a>.<\/strong>\u00a0Several experts in fields such as data engineering, data mining, data analytics<\/strong>, data science<\/strong><\/a> are using it.<\/p>\n

There is still the confusion between the concepts of Big Data and Data Science though they seem to be the same in terms of exchanging data, their role is different and the jobs roles are also totally different.<\/p>\n

What\u2019s the huge difference between Big Data and Data Science?<\/h2>\n

\"What\u2019s<\/p>\n

Data Science<\/a><\/h3>\n

In the simpler terms, it is the combination of several techniques used to big data and driving insights through that. Digging deeper details into this, this field comprises of doing everything to the data right from fetching data (Data Mining<\/strong>), preparation (Data Analysis),<\/strong>\u00a0finding the best data\u00a0(Data Cleansing)\u00a0<\/strong>and dealing with structured, unstructured and semi-structured data.<\/p>\n

Data Science combines mathematics, statistics, problem-solving skills, algorithms, capturing data in brilliant ways, and knowing how to look the data in whole different aspects of preparing and aligning.<\/p>\n

\u00a0Big Data<\/em><\/strong><\/a><\/h4>\n

Big Data is nothing but processing the extensive amount of data which is not possible through traditional techniques. It starts with the raw data which can be stored on a single computer. It is something that is used for analyzing insights which assist better conclusion of making business moves that are strategically planned.<\/p>\n

It all depends on three V\u2019s, volume, Velocity and Variety. The three V\u2019s refers to the types and features the information and processing them for the future use. The process used here should be cost-effective and innovative and can allow enhanced decision making, insight and process automation.<\/p>\n

Applications of Big Data and Data Science<\/h5>\n

\"\"<\/p>\n

Applications of Data Science<\/strong><\/p>\n

This is widely applied in various fields and aspects<\/p>\n

\u00a0<\/strong>Recommended\u00a0systems<\/b><\/p>\n

Data science makes the system easier as it helps in finding the better prospects, displaying the relevant products and increasing the user experience. Most of the organizations use this recommender system to promote their products through suggesting them according to the user search relevance of information and demands, this recommendation depends on the previous search results of users.<\/p>\n

Internet Search<\/strong><\/p>\n

Data science algorithms are used by many search engines so as to deliver the best results in just a split second.<\/p>\n

Digital Adverts<\/strong><\/p>\n

The reason for the digital ads gets higher CTR than the conventional forms of advertisements is that the whole digital marketing ecosystem makes use of digital science algorithms spanning from display banners down to digital billboards.<\/p>\n

Applications of Big Data<\/strong><\/p>\n

Retail<\/h4>\n

Understanding customer pace is the main thing in the business, rather than being competitive, for which the proper analysis is always required to segregate the data. This segregated data is widely used by the companies which include customer transaction data, weblogs, loyalty program data, social media and store-branded credit data.<\/p>\n

Communications<\/h4>\n

Sales and Communications service is all about finding the customers, gaining new ones and expanding the current customer bases. That can be achieved only with the combining and analyzing lots of customer and machine-generated data.<\/p>\n

Financial services<\/h4>\n

Everything regarding the financial sector such as retails banks, insurance firms, private wealth management advisories, institutional banks and venture funds makes use of this big data. The major challenge experienced by all of them is the large amounts of multi-structured data set in multiple systems which can be take care only by Big Data. This is used in various ways such as fraud analytics, consumer analysis, operational and compliance analytics.<\/p>\n

\u00a0<\/strong>Tools used in Big Data and Data Science<\/strong><\/h6>\n

\"Tools<\/p>\n

Data Science is entirely dealt with R programming<\/a>, Python coding<\/a>, statistics and Machine Learning.<\/a>\u00a0Due to its extensive package repository around statistical and analytics applications, R is tremendously growing in popularity around the world and many firms are on the lookout for R programmers.<\/p>\n

Big Data is something which deals with\u00a0Hadoop,<\/a> Hive,\u00a0HBase ,\u00a0Spark and Scala<\/a>. A basic knowledge of Java is always required for the MapReduce Programming, clustering and Tableau-Hadoop connectors.<\/p>\n

Skills required for Big Data \u00a0Analysts and Data Scientists<\/strong><\/pre>\n
Big Data<\/strong><\/p>\n