assignment 4

Discussion 1

Data mining revolves around the sequential extraction, identification as well as the discovery of patterns in a given data sets. The constant increase in data has led to many discussions concerning the use of unstructured data (MOData, 2020). The increase in competition has led to increased demand for information, and that is where data mining tools come in. Unstructured data constitutes the majority of the data. Databases are constantly increasing, and businesses want to enhance their analysis of information (Tan, Steinbach, & Kumar, 2016). The discovery of data mining and data mining tools has improved the knowledge base of companies and how they make different decisions. Companies gather data from different sources such as sales, customer perceptions, and also market trends. Every day new products are released to the market, and data mining forms the basis of such evolution. The purchase history of consumers can be used to predict consumer trends. It can also be used to manage inventory records. Once the data has been collected, themes, as well as trends, will be developed. Models will be built based on the data collected (Stephanie, 2015). Ideas such as classification rules, mathematical formulas, and even decision trees will be used for analysis.

On the other hand, data analytics refers to the science behind the analysis of raw data. This has helped in making conclusions about various data types. The trends, as well as metrics, can be developed from data analysis. Information is normally subjected to data analytics. It has helped in optimizing performances and improving business operations (MOData, 2020).

Discussion 2

Data is an essential tool in businesses today. It helps one know the company's current position and how to get to where one intends to be. Data analytics and data mining are two close terms which are used in the assessment of data. Data mining is a process used by companies to turn raw data into useful information by using software to look for patterns in large batches of data (Alexandra Twin, 2020). This process helps identify unseen patterns by sorting out the relevant information from a disorganized data set. Data analytics on the other hand is a number of processes which transform raw data into information which makes sense and is meaningful which should be used to make decisions in a business.

The difference between data analytics and data mining is not distinct. It depends on the task at hand. They differ in a couple of ways in that, data mining focuses on revealing unseen patterns within a large volume of data and disposing information which is not important while data analytics focuses on transforming a set of data into useful information which can be used in making business decisions. Data mining is mostly build on scientific and mathematical procedures to uncover the unseen patterns in data while data analytics use past information and results to transform data into hypothesis information. Data mining is a process by itself while data analytics is a series of processes which together complement each other to achieve the end result. Data analytics has four processes which are; descriptive, diagnostic, predictive and prescriptive analysis methods which together constitute the data analytics process.

Data analytics is used in business to make the best decisions, marketing strategies and provide better services to their customers. This for example can be used in a hotel where data analytics takes information of a number of customer’s reviews and analyzes the data to identify any problem in their service deliver this informs the business owners of their decisions. Data mining can be used in detecting fake news (Shu, Sylvia, Wang, Tang & Liu, 2017). Data mining is applicable here since it sorts out large information on social media platforms that might be misleading and disposes it. Data mining has also found use in business where it can be used to identify any unusual pattern in sales and helps businesses avoid losses and make better decisions.

With the world advancing in technology, data is becoming an important value in making decisions and strategies in the business world. Data mining and data analytics are becoming marketable careers in companies, businesses and even hospitals. Data mining can be used in understanding which information is useful from large data sets and data analytics helps in reflecting the information from past data and patterns providing the best hypothesis.

Data analytics and data mining have often been said to be the same skill but from the discussion the distinct difference is clearly seen. Both activities deal with data and can be used together to get more insight on the information. As technological advancement continues, both areas will be seen to receive more demand.

Please make sure two response posts to given discussions substantive. A substantive post will do at least TWO of the following:

1.Ask an interesting, thoughtful question pertaining to the topic

2.Answer a question (in detail) posted by another student or the instructor

3.Provide extensive additional information on the topic

4.Explain, define, or analyze the topic in detail

5.Share an applicable personal experience

6.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)

7.Make an argument concerning the topic.

8.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.