Terminology analysis:
Big Data
According to Gartner, “Big data are high-volume, high-velocity, and/or high-variety information assets that require cost-effective, innovative forms of information processing to enhance insight, decision-making, and process automation”.
Big data means large amounts of raw data that cannot be processed efficiently by conventional applications, such as traditional database management systems. Due to the sheer volume, the application cannot store the data in a single computer's memory. Such large amounts of structured and unstructured data (big data) often overwhelm businesses. This data needs to be leveraged to analyze business insights for strategic business initiatives and better decisions.
data science
Data science involves the processing of big data (structured and unstructured), including its preparation, analysis, and cleaning. It also involves programming, mathematics, statistics, problem solving, the ability to see things in different ways, capturing data visually, etc. You can say that data science is a broader term for the techniques involved in deriving insights and information from data.
data analysis
Raw data science used to derive meaningful information and conclusions from existing data is called data analysis. It uses tools combined with algorithms to extract results from existing raw data.
Many industries utilize this process to enable them to make effective decisions as well as validate and refute old models or theories. Data analysis tools help you extrapolate results based on facts known to researchers.
After learning about data science, data analysis and big data, it is obvious that they are dealing with the same thing "data". Since processing large amounts of data is critical, data analysis covers the process covered in this article extensively. So, what is the simplest form of analysis? It is simply the process of using mathematics, statistics, machine learning techniques and predictive modeling to understand and design effective patterns for recorded data.
Application areas of big data:
Big data in communications
Telecom companies need big data to acquire new users, retain old users and expand their base to existing customers. Big Data enables you to solve relevant problems within this sector by combining and analyzing data continuously generated by users and systems (machine-generated).
Retail big data
Understanding your customers' needs is the backbone of any business, whether it's an online e-tailer or the store across the street. Big data represents the ability to analyze various data sources that businesses deal with on a daily basis. Whether it’s customer transaction data, blogs, data from store brand credit cards, loyalty program data or social media, big data is enough to master it.
Financial services big data
Big data is used by organizations such as retail banks, credit card companies, insurance companies, private wealth management consulting firms, venture capitalists, and investment banks. Big data helps them solve the problem of large amounts of multi-structured data present in their systems and manage them effectively. The main functions of big data are –
Fraud Analysis
Customer Analysis
operational analysis
Compliance Analysis
Education big data
With the widespread adoption of big data technology by industries and professionals, the education sector has not been affected by the application of big data. Since big data professionals are in high demand these days, so are big data expert trainers. In the application field of big data, individuals can create a bright career by cultivating big data professionals for enterprises, companies and industries.
Application areas of data science
digital advertising
Data science algorithms have greatly benefited the field of digital marketing, ranging from displaying slogans, but not limited to digital billboards. Data science drives higher click-through rates for digital ads compared to good old traditional advertising.
Internet search
Data science is the foundation of determining the underlying algorithms behind search engine results. Whenever you press the search key on any search engine, it prompts search engine bots to crawl various content available on the Internet.
Recommended system
Recommendation systems from data science help enhance user experience and simplify the process of finding relevant products over the Internet. As you browse the Internet or through in-app advertising, the Company promotes various products and provides you with recommendations based on your needs and relevance based on your search history.
Knowledge system of data science:
From the perspective of knowledge system, data science is mainly based on statistics, machine learning, data visualization and (certain) field knowledge. Its main research contents include basic data science theory, data processing, data calculation, data management, data analysis and Data product development.
Basic theory: mainly includes new concepts, theories, methods, technologies and tools in data science, as well as the research purpose, theoretical basis, research content, basic processes, main principles, typical applications, talent training, project management, etc. of data science. What needs special reminder is that "basic theory" and "theoretical basis" are two different concepts. The "basic theory" of data science is within the research boundaries of data science, while its "theoretical basis" is outside the research boundaries of data science and is the theoretical basis and source of data science.
Data Wrangling or Data Munging: One of the new concerns in data science. In order to improve data quality, reduce the complexity of data calculations, reduce the amount of data calculations, and improve the accuracy of data processing, data science projects need to perform certain processing on the original data - data auditing, data cleaning, data transformation, and data integration. , data desensitization, data reduction and data annotation, etc. It is worth mentioning that, unlike traditional data processing, data processing in data science places more emphasis on the value-added process in data processing, that is, how to integrate the creative design, critical thinking and curious questioning of data scientists into the data. during processing activities.
Data computing: In data science, the computing model has undergone fundamental changes - from traditional computing such as centralized computing, distributed computing, grid computing, to cloud computing. The more representative ones are Google's three major cloud computing technologies (GFS, BigTable and MapReduce), Hadoop MapReduce, Spark and YARN. Changes in computing models mean fundamental changes in the main bottlenecks, main contradictions and thinking patterns of data computing that are concerned in data science.
Data management: After completing "data processing" and "data calculation", the data still needs to be managed and maintained in order to carry out (repeat) "data analysis" and reuse and long-term storage of data. In data science, data management methods and technologies have also undergone important changes - including not only traditional relational databases, but also the emergence of some emerging data management technologies, such as NoSQL, NewSQL technology and relational cloud.
Data analysis: The data analysis methods used in data science are obviously professional, usually based on open source tools, and are significantly different from traditional data analysis. At present, R language and Python language have become more commonly used data analysis tools by data scientists.
Data product development: “Data product” has a special meaning in data science—a collective term for products developed based on data. Data product development is one of the main research missions of data science, and it is also an important difference between data science and other sciences. Different from traditional product development, data product development has the characteristics of being data-centric, diverse, hierarchical, and value-added. Data product development capabilities are also the main source of competitiveness for data scientists. Therefore, one of the purposes of learning data science is to improve your data product development capabilities.