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. Please make your initial post and two response posts substantive. A substantive post will do at least TWO of the following: · Ask an interesting, thoughtful question pertaining to the topic · Answer a question (in detail) posted by another student or the instructor · 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 UC Library) that applies to the topic, along with additional information about the topic or the source (please cite properly in APA) · Make an argument concerning the topic. At least one scholarly source should be used in the 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.

Data Analytics and Data Mining are both essential techniques used in the field of data science to extract meaningful insights and draw conclusions from large sets of data. However, there are distinct differences between the two.

Data Analytics refers to the process of examining, cleaning, transforming, and modeling data to uncover useful information, patterns, and trends. It involves the application of statistical analysis and predictive modeling techniques to guide decision-making or gain insights into various phenomena. Data Analytics is broader in scope and focuses on understanding and explaining the underlying patterns in the data.

For example, a retail company may use data analytics to study customer behavior and preferences in order to improve marketing strategies. By analyzing customer purchase history, demographic information, and online browsing patterns, the company can identify trends and patterns to personalize promotions, target specific customer segments, and optimize pricing strategies. Data analytics provides actionable insights, enabling the company to make informed decisions and drive business growth.

On the other hand, Data Mining is a subset of Data Analytics and focuses on discovering patterns, relationships, and anomalies in large datasets. It involves the extraction of previously unknown, useful information from data by applying various machine learning algorithms, statistical techniques, and data visualization tools.

For instance, a telecommunications company might use data mining to identify patterns of customer churn. By analyzing historical data of customer behavior, service usage, and demographics, the company can build predictive models to identify customers most likely to churn. This helps the company proactively take measures to retain customers, such as offering targeted incentives or improving customer service. Data mining enables organizations to gain valuable insights from large volumes of data that may otherwise remain hidden or difficult to identify using conventional methods.

In summary, while both Data Analytics and Data Mining involve the analysis of data to extract insights, Data Analytics focuses on understanding and explaining patterns in data, whereas Data Mining specifically aims at discovering previously unknown patterns and useful information. It is important to note that Data Mining is just one step or technique within the broader scope of Data Analytics.

Reference:
Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O’Reilly Media.