Course: Data Science & Big Data Analytics There is much discussion regarding Data Analytics and Data Mining. Sometimes these terms are used synonymously but there is a difference. What is the
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Post 1:
Poorna Chandra
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Difference between Data Analytics and Data Mining
Data analysis is the process of collecting, organizing, and transforming data to draw insightful conclusions from it. Data analysis is essential as it transforms figures, facts, and metrics into initiatives that contribute to improvement (Azzalini & Scarpa, 2012). It is essential to carry out data analysis from the insights organizations gain and make informed decisions. There are diverse categories of data analysis. These are text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Various tools have been developed to aid in data analysis. These tools are R Studio, Python, SQL Consoles, MySQL Workbench, Java, Mat lab, and SAS Forecasting. An example of how data analysis is used is through data analysis while planning. Organizations eliminate and reduce the chances of guesswork with data analysis. Through the insights, they gain concerning their customers, their needs, and preferences, they can make informed decisions about their customers' products and services. With the modern data analysis tools, they keep updating themselves with data based on the changing conditions.
Data mining is the process of revealing hidden, previously unknown relationships within substantial data sets. It is a process that reveals the hidden functional patterns in massive data sets (Azzalini & Scarpa, 2012). The difference between data analysis and data mining is that data mining utilizes scientific and mathematical systems to recognize data configurations. In contrast, data analysis is applied to tasks associated with business analytics problems. Data mining involves collecting data and identifying patterns while data analysis tests a hypothesis and translates what is found to useful and accessible information (Hand & Adams, 2014). An example of how data mining is used is in-market predictions. Data mining predicts users that are most likely to unsubscribe from a service or product, the interests of customers from their searches, or the requirements within a mailing list for an organization to attain a higher response rate.
References
Azzalini, A., & Scarpa, B. (2012). Data analysis and data mining: An introduction. OUP USA.
Hand, D. J., & Adams, N. M. (2014). Data mining. Wiley StatsRef: Statistics Reference Online, 1-7.
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Post 2:
1 day ago
Karthik
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Data analytics is the process of observing data sets to discover fashions and draw assumptions about the data they hold. In contrast, data mining is the process of finding anomalies, arrangements, and relationships within big data sets to foresee outcomes. Data mining provides the data collection and develops crude but necessary insights. Data analytics then practices the data, and it crude the assumption to create upon that and generate a classical based on the data (Shi‐Nash & Hardoon, 2017). The phase in the procedure of acquiring data analytics is known as data mining. Data analytics is dealing with every stage in the duty of any data-driven model.
There is no purpose of any favoritism or any notions formerly when tackling the data in data mining. At the same time, assumption testing is mainly used for data analysis. In data mining, the uses of imaginations, diagrams, GIPs, and bar charts are not necessarily required (Kudyba, 2014). While in data analytics, it is part of these visualizations. When data in a topic is well structured in data mining, it gleams very brightly. Meanwhile, data analysis allows the presentation to be performed in any accessible data. It will still develop meaningful understandings that could assist in pushing the corporation to even more giant summits.
Air France KLM caters to customer travel preferences is an example of data mining. It applies data mining methods to produce a 360-degree client view by incorporating data from trip searches, reservations, and flight operations with web, social media, and call center (Banu & Swamy, 2016). This deep client understanding is used to produce modified travel skills. Stopping hackers in their tracks is an example of data analytics. Data analytics helps in guarding businesses and individuals alongside hackers.
Reference
Banu, N. S., & Swamy, S. (2016, December). Prediction of heart disease at an early stage using data mining and big data analytics: A survey. In 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT) (pp. 256-261). IEEE.
Kudyba, S. (2014). Big data, mining, and analytics: components of strategic decision making. CRC Press.
Shi‐Nash, A., & Hardoon, D. R. (2017). Data analytics and predictive analytics in the era of big data. Internet of things and data analytics handbook, 329-345.
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