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Discussion 1:
Data Analytics and Data Mining
Data Analytics and Data Mining are in some instances used interchangeably despite there being difference. However, a similarity in data analytics and data mining is that both are the technological applications that are aimed at streamlining the decision making processes in the organization as they simplify the complex data which could have otherwise not have been easily understood.
Data Analytics is the science of the analysis of raw data so as to make prerequisite conclusions regarding the information. Majority of the processes as well as techniques in data analytics are automates into algorithms and mechanical processes which transforms the raw data for human use. Various techniques in data analytics helps reveal the trends as well as the specific metrics that can be used in the analysis and development of patterns in the data and information. The information could then be used in the optimization processes as to increase the efficiency of systems in a business. Data analytics is significant as it helps companies optimize their performance through strategically using the data at their disposal (Mahmood, 2016). The implementation of data analytics into different business models means that the companies will reduce costs through the identification of efficient ways they can do business as well as store significant data amounts (Mishra et al., 2018). Companies could use data analytics in making efficient business decisions as well as analysis of the customer trends as well as satisfaction which leads to the creation of better and efficient products. Data analytics is used in bringing sense and meaning to a set of data and data patterns which need to be analyzed systematically in order to make sense out of the large streams of datasets.
Data Mining on the other hand involves the exploration as well as analysis of significant knowledge so as establish patterns as well as rules in the subsets of the data. It could also be the systematic and a successive technique of the identification as well as discovery of hidden patterns in a big dataset. Additionally, data mining is used in building machine learning models which are used in artificial intelligence (Mahmood, 2016). Data Mining is also known as Knowledge Discovery in Databases (KDD) and involves the process of the discovery of patterns in a large dataset as well as in data warehouses. Techniques such as regression analysis, clustering as well as outlier analysis are applied to the data in the identification of useful and critical outcomes (Mishra et al., 2018). The techniques make use of software and the backend algorithms in the analysis of data patterns. Some of the common data mining methods include the decision tree analysis, the Bayes theorem analysis and the item-set mining. Some of the examples of data mining tools include the RapidMiner software.
Data Analytics and Data Mining are significant in the career application as they are used in establishing the patterns in the data as well as the use of strategic approaches that could be used to bring sense and meaning out of the huge datasets that organizations process almost on a daily basis.
References
Mahmood, Z. (2016). Data Science and Big Data Computing: Frameworks and Methodologies.
Mishra, D. K., Yang, X.-S., & Unal, A. (2018). Data science and big data analytics: ACM-WIR 2018.
Discussion 2:
In this data driven generation every bit of information is important. General processing stages are Collection stage, Processing stage, data mining and the last, usage stage. In collection stage, gathering of raw data by using different techniques at one temporary storage. Processing involves in organizing the raw collected data in a data base where several other source’s data is also gathered. Some related and identified data patterns were extracted in data mining stage. Finally, in usage stage valuable information is formed by analyzing those extracted patterns. This might actually differ and depends on actual requirement of the organization practices (Singh et al., 2016). In brief, data mining refers to set operations performed on available data in a database to discover some valuable trends and relations. Data analytics refers to process of applying various algorithms to derive the insights from data sets.
Main differences between data mining and data analytics includes mining applicable for large datasets i.e., data mining provides great understanding with databases whereas analysis gives exposure to predictive analysis where mining can also be a part of it.
Considering an example of data-driven diagnostic system, when using sensing devices in manufacturing these devices massive amounts of data is generated. A new methodology is developed and showcasing with control charts as an output to these diagnostic systems. Here, authors describe the implementation flow of analyzing the data is well explained. After extraction of each feature control charts were developed with sigma limits. Further, using all this information the situation is analyzed for monitoring and performance results. Generally, data mining is used termed in early business of data study. In my opinion both are interconnected operations in general data processing procedure. But it may be executed with advanced algorithms and associated statistical insights. During this study, I found many data related terms like data science, Machine learning, big data along with data analytics. If someone wants to pursue his career in one those mentioned areas which would you suggest?
References:
Singh, Manoj Kumar, and Dileep Kumar G. Effective Big Data Management and Opportunities for Implementation. Information Science Reference, an Imprint of IGI Global, 2016.
Guo Weihong, Guo Shenghan, Wang Hui, Yu Xiao, Januszczak Annette, & Suriano Saumuy. (2017). A Data-Driven Diagnostic System Utilizing Manufacturing Data Mining and Analytics. SAE International Journal of Materials and Manufacturing, 10(3), 282.
