Course Data Science Big Data Analyze Submission Won't Be

Course Data Science Big Data Analylate Submission Will Not Be Accep

There is much discussion regarding Data Analytics and Data Mining. Sometimes these terms are used synonymously but there is a difference. What is the difference between Data Analytics vs Data Mining? Please provide an example of how each is used. Also explain how you may use data analytics and data mining in a future career.

Provide extensive additional information on the topic. Explain, define, or analyze the topic in detail. Share an applicable personal experience. Provide an outside source (for example, an article from the University Library) that applies to the topic, along with additional information about the topic or the source (please cite properly in APA). At least one scholarly source should be used in your initial discussion thread. Be sure to use information from your readings and other sources from the UC Library. Use proper citations and references in your post.

Paper For Above instruction

Data science encompasses various methodologies and techniques aimed at extracting insights from vast and complex datasets. Among these methodologies, data analytics and data mining are particularly prominent, yet they serve different purposes and employ different approaches. Understanding the distinction between these two concepts is vital for effectively leveraging data in business, technology, and research domains.

Difference Between Data Analytics and Data Mining

Data analytics refers to the process of examining datasets to draw conclusions about the information they contain. It involves the application of statistical analysis, descriptive and inferential statistics, and visualization techniques to interpret data, identify patterns, and support decision-making (Delen & Demirkan, 2013). Data analytics is often used in business intelligence to understand trends, measure performance, and forecast outcomes. For example, a retail company might analyze sales data to determine which products are most popular during specific seasons, enabling better inventory management and targeted marketing campaigns.

Data mining, on the other hand, is a subset of data analytics that focuses on discovering hidden patterns, relationships, or structures within large datasets through machine learning, pattern recognition, and database segmentation. It involves algorithms that automatically detect interesting patterns or clusters without prior hypotheses, often with the goal of predictive modeling or anomaly detection (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). For example, in credit card fraud detection, data mining techniques might uncover unusual purchasing patterns that suggest fraudulent activity, enabling preemptive action.

Examples of Usage

In practical applications, data analytics might be used by a healthcare provider to evaluate patient outcomes after treatments, utilizing statistical methods to correlate treatment types with recovery rates. This aids in policy formulation and personalized medicine. Conversely, data mining could be employed to scan enormous databases of electronic health records to uncover previously unknown risk factors for certain diseases, informing future research and preventive strategies.

Application in Future Careers

Looking ahead, both data analytics and data mining will have vital roles in various industries and careers. As a future data analyst, I foresee using data analytics extensively to interpret operational data, optimize processes, and support strategic decisions. For instance, in marketing, analyzing consumer behavior data helps tailor campaigns that resonate with target audiences, ultimately increasing engagement and sales.

In roles related to AI and machine learning, data mining will be crucial for developing models that predict customer churn, enhance recommendation systems, or detect security threats. For example, in cybersecurity careers, data mining algorithms analyze network traffic data to identify anomalies indicating potential cyber-attacks. Moreover, as organizations increasingly adopt big data solutions, skills in both analytics and data mining will be indispensable for extracting actionable insights and maintaining competitive advantage (Zhao et al., 2017).

Conclusion

Understanding the distinction between data analytics and data mining enables professionals to select appropriate methods based on their objectives. Data analytics emphasizes interpreting data to inform decisions, while data mining focuses on discovering hidden insights within large datasets. Both are indispensable in today’s data-driven world and will continue to shape future careers in technology, business, and research.

References

• Delen, D., & Demirkan, H. (2013). Data, information, and analytics: a research agenda. Decision Support Systems, 55(1), 359–363.
• Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37–54.
• Zhao, L., Huang, Y., Wang, Y., & Liu, J. (2017). Analysis on the Demand of Top Talent Introduction in Big Data and Cloud Computing Field in China Based on 3-F Method. 2017 Portland International Conference on Management of Engineering and Technology (PICMET), 1–3.
• Saiki, S., Fukuyasu, N., Ichikawa, K., Kanda, T., Nakamura, M., Matsumoto, S., Yoshida, S., & Kusumoto, S. (2018). A Study of Practical Education Program on AI, Big Data, and Cloud Computing through Development of Automatic Ordering System. 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD), 31–36.
• Liao, C.-H., & Chen, M.-Y. (2019). Building social computing system in big data: From the perspective of social network analysis. Computers in Human Behavior, 101, 457–465.
• Eyupoglu, C. (2019). Big Data in Cloud Computing and Internet of Things. 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 1–5.
• Psomakelis, E., Aisopos, F., Litke, A., Tserpes, K., Kardara, M., & Campo, P. M. (2016). Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications.