2. Data Mining Algorithms (Data Mining Algorithms) visualization is for people to see, data mining is for the machine to see. Clustering, segmentation, isolated point analysis and other algorithms allow us to go deep inside the data and mine the value. These algorithms not only deal with the volume of big data, but also the speed at which it can be processed.
3. Predictive Analytic CapabilitiesData mining allows analysts to better understand the data, while predictive analytics allows analysts to make some predictive judgments based on the results of visual analytics and data mining.
4. Semantic Engines(Semantic Engines) We know that the diversity of unstructured data brings new challenges in data analysis, and we need a series of tools to parse, extract, and analyze the data. Semantic Engines need to be designed to be able to analyze data from ? documents? to intelligently extract information from them.
5. Data Quality and Master Data Management are management best practices. Working with data through standardized processes and tools ensures a pre-defined, high-quality analysis.
About which specific aspects of big data analytics are included, Aoto has shared with you here. If you have a strong interest in big data engineering, I hope this article can help you. If you still want to learn more about data analysts, big data engineers tips and materials, you can click on other articles on this site to learn.