Data Mining and Analytics Report: Techniques and Analysis

This report provides an overview of data mining and analytics, exploring various techniques used to manipulate and interpret data. It begins with an executive summary and introduction, followed by a discussion of key concepts such as data analytics, data mining, clinical decision support (CDS), data visualization, and dashboards. The report highlights the importance of data manipulation techniques in extracting meaningful insights from raw data, emphasizing the role of data mining in predicting future trends and supporting decision-making. It also touches upon the significance of data capture tools and their role in organizing data files. The report concludes that data mining is the most effective technique in the era of big data. It stresses the importance of collaboration between technology and business professionals to achieve efficiency, revenue growth, and profitability. The report also includes a list of references to support its findings.

Running Head: DATA MINING & ANALYTICS
Data Mining & Analytics
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2Data Mining & Analytics
Executive Summary
The huge production of data day to day has led to a revolution in the field of information and
technology. The main objective of research is to gather data in huge volume and make
meaningful information and insights out of it. Different BI tools are now available in the market
which uses the data warehouse where all meaningful data’s are stored for different data
manipulation techniques. In this report different data manipulation techniques will be described
briefly and at the end a conclusion will be concluded about some described techniques.

3Data Mining & Analytics
Table of Contents
Executive Summary.........................................................................................................................2
Introduction......................................................................................................................................4
Discussion........................................................................................................................................4
Data Analytics.............................................................................................................................4
Data Mining.................................................................................................................................4
Clinical Decision Support (CDS)................................................................................................4
Data Visualization.......................................................................................................................4
Dashboard....................................................................................................................................4
Conclusion.......................................................................................................................................6
References........................................................................................................................................7

4Data Mining & Analytics
Introduction
The ability to play with data is a crucial ability in data analysis. Different techniques are
there for different manipulation of data. Recognizing data and identifying the characteristics of
data has now become easier with different techniques available (Blikstein & Worsley, 2016).
Discussion
Data Analytics
Data analytics is termed to be as an essential technique to analyze raw data and come up
with some meaningful conclusion about that data information. Commercial industries uses data
analytics technology to make decision making in business for better growth and development
(Choi et al., 2016). Model creation and other regular task will be the main focus for data analyst
who work in Business intelligence.
Data Mining
Data mining is the process to find useful and important information or knowledge from
huge volumes of data. Making sense of raw data is the actual work of data mining. The main
benefit of data mining is that it is use for prediction like a mail is spam or not and more.
Clinical Decision Support (CDS)
CDS provides administrative staff, clinical, patients or other member of the care team
with a properly filtered and which targets a particular person or situation to enhance health and
health care (Ge et al., 2017). The main objective of CDS is to improve clinical care quality,
avoiding errors in the system and work more efficiently to all the staffs.
Data Visualization
Data visualization is the technique to visualize information and data graphically.
Visualization is an important part to understand outliers, patterns, trends and future possibilities.
With the recent popularity of big data, data visualization of data is essential to analyze huge data
to make better decisions. E.g. – Tableau.
Dashboard
A dashboard is termed to be as a collection of widgets which has an overview of a report
or metric. Dashboard is a user interface where information or data are organizes in a presentable
manner. A dashboard is an interactive graphical user interface (Tan, 2018).

5Data Mining & Analytics
Data capture tool
The purpose of data collection is to capture data’s or information which will be processed
to fulfill certain purposes. Data capture tool is beneficial to organize and stricter the data files. It
have the validation checking so that genuine and required data is captured. Has the ability to
provide high quality of data without any distortion to its appropriate destination.
Data mining is an essential piece of analytics because of its potential to predict what
likely to occur in the future or one can act according to take advantage of the upcoming trends.
Also due to the ability to process and predicting data mining is been used widely by data analyst.
Few data mining techniques are regression, clustering, tracking patterns, prediction and many
more.

6Data Mining & Analytics
Conclusion
From all the data manipulation technique it can be concluded that data mining is the most
effective from the rest. With the era of big data if both the technology and business profession
work together efficiently and deliver result on promising time then it will be a revolutionary time
to gain efficiency, revenue, productivity and profitability in work.

7Data Mining & Analytics
References
Blikstein, P., & Worsley, M. (2016). Multimodal Learning Analytics and Education Data
Mining: using computational technologies to measure complex learning tasks. Journal of
Learning Analytics, 3(2), 220-238.
Choi, T. M., Chan, H. K., & Yue, X. (2016). Recent development in big data analytics for
business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-
92.
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017). Data mining and analytics in the process
industry: The role of machine learning. IEEE Access, 5, 20590-20616.
Tan, P. N. (2018). Introduction to data mining. Pearson Education India.