The Difference between Data Science, Big Data and Data Analysis | Web Scraping Tool | ScrapeStorm
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What is data science?
Data science is an interdisciplinary field that includes all content related to structured and unstructured data, starting from preparation, cleaning, analysis, and derived from a useful perspective. It combines mathematics, statistics, intelligent data capture, programming, problem solving, data cleaning, different viewing angles, preparation and data alignment.
In short, it is a combination of several technologies and processes for processing data to obtain a valuable business perspective. Through the use of scientific methods, algorithms, processes and systems to effectively extract information, this information can be used by the business to make key business decisions.
What is big data?
If we talk about big data, we are not talking about data stored on one computer, but a large amount of non-aggregated raw data stored in different places, the size of which varies by pb. As more and more data is generated from various sources every millisecond, the data is not in standard form, but in various forms. In fact, 80% of the currently generated data is unstructured, and it is difficult to effectively process them using traditional techniques alone.
In the early days, the amount of data generated was not high, we have been filing them and only doing historical analysis. However, one important thing to remember is, “Big data is very important and needs to be analyzed so that we can draw useful insights and make better, strategic business moves.”
As the world’s leading research and consulting company, Gartner defines big data as “high-capacity, high-speed, and/or highly diversified information assets that require cost-effective and innovative forms of information processing that can enhance decision-making , Insight and process automation.”
What is data analysis?
Data analysis is a process that involves applying algorithms or mechanical procedures to derive useful business insights. The techniques and techniques of data analysis are widely used in industry to make informed decisions to verify or deny current models and theories.
Now let’s dive into each category of applications.
Data science applications:
1) Recommendation system: The recommendation system can predict whether a certain user is willing to buy a certain product and help users find related products quickly. Many industry giants, such as Amazon, are using recommendation systems to promote their recommendations and products based on user preferences by using their previous search results.
2) Digital advertising: Digital advertising is the latest trend, and it is also an advertising that has a higher CTR than traditional advertising. Whether it is gorgeous display banners or interactive digital billboards, digital scientific algorithms are the support behind the success of these digital advertisements.
3) Internet search: search engines can get any result in a short time.
Big data applications:
1) Retail: As market competition intensifies, retail business becomes increasingly difficult. Business owners are often looking for channels to better understand and serve customers. There is a lot of data every second, and proper analysis of all the data from social media, loyalty programs, customer transactions and other channels can help them gain a competitive advantage.
2) Communications: Telecom service providers analyze massive amounts of data to make important business decisions, such as expanding their customer base.
3) Financial services: Almost all top financial institutions, from insurance to retail banks, Cook provides big data services for their financial services. The major challenge facing these financial companies is that their huge multi-structured data is distributed in different systems. Analyzing big data can help them in many ways, such as customer analysis, fraud analysis, compliance analysis, and operational analysis.
Application of data analysis:
1) Energy management: Most companies use data analysis for energy management, including smart grid energy, utility company building automation, energy optimization, and energy distribution.
2) Medical: Cost pressure is the main challenge faced by hospitals, which also limits the effective treatment of many patients.
3) Games: Collecting data in games to optimize and spend is the main advantage of data analysis. Companies that make games have better insights into user preferences, dislikes, and relationships.
4) Tourism: Optimize the buying experience through online blog analysis, mobile data analysis, and social media data analysis. Customer preferences and desires can be extracted. Customized quotations and packages can be promoted based on the customer’s follow-up browsing situation, helping the company achieve better conversion rates.
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