University Report: Big Data Analytics on Drug Misuse Data

This report provides a critical evaluation of the big data approach to drug misuse data analysis. It utilizes the DRUG MISUSE dataset and employs descriptive analytics techniques within the Tableau environment. The report details the advantages and disadvantages of descriptive analytics, including data aggregation and data mining, while emphasizing the importance of data visualization through heatmaps and other methods. The student discusses the use of correlation analysis to identify relationships between data attributes and highlights the capabilities of Tableau in data mining, predictive analysis, and dashboard creation. The document concludes with recommendations for further analysis and emphasizes the value of big data tools for gaining insights and making informed decisions in the context of drug misuse data.

Running head: Security in Computing and IT
A Critical Evaluation of the Big Data Approach to Drug Misuse
Data Analytics
Name of the Student
Name of the University
Authors note

1SECURITY IN COMPUTING AND IT
Introduction
While analyzing the DRUG MISUSE data set there are certain techniques /approaches
are used in order to get the insights from the large amount of data stored in the provided excel
file using the tableau. This tool is very efficient in visualization of the trends inside the dataset
as well as predictive analytics. In addition to that, the Tableau also provides easy to use GUI
platform that suits the needs of the beginner as well as professional data analysts o get started
with the analysis on the selected dataset.
Used approaches in the DRUG MISUSE dataset
While analyzing the provided data the descriptive data analysis approach is selected. In
descriptive analytics process the it is intended to provide view or summary of the data depending
upon the facts and figures generated from the dataset and in some specific understandable
format. In addition to that the tool can be used in order to prepare data for further analysis. In the
analysis the primary techniques used were data aggregation as well as data mining in order to
report trends from the past recorded data. In this way the analyzed data can be easily presented in
a easily understandable format that can be beneficial to a wide business audience.
Advantages and disadvantages of the approaches
In case of descriptive analytics, it can be stated that it rarely intends to
investigate/analyze or try to establish the cause effect relationships among the different
attributes. In this approach of analytics mainly the surface analysis over the data set is done. In
this way validity of the results of the analysis can be easily implemented. Some common
methods employed in Descriptive Analytics are observations as recorded for the creation of this
dataset, surveys and so on. Therefore, collection as well as interpretation of enormous amount of
data involved in this type of analytics can help in exploring the hidden trends or insights.
The descriptive Analysis is important in providing knowledge base that can be used as
the foundation for further quantitative analysis on any selected data set. As the descriptive
analysis maps the landscape for the analysis of specific phenomenon or trends. It is also believed
that the when the results of the descriptive analysis if properly interpreted then it can help in the

2SECURITY IN COMPUTING AND IT
hypothesis formation with the powerful insights can provide useful insights to make informed
business decisions.
In descriptive statistics the metrics that are mostly used in the Descriptive analysis in
order to analyze as well as d summarize large dataset are Frequencies of the different attribute
values, mode, standard deviation, mean, median, correlation, plots such as scatter, bar plots,
tables, charts, histograms and others. Most of this plots and histograms are mainly used in order
to depict the relationship between variables.
In case of the plotting the visualization the heat map is mostly used. These heatmaps
are many viable optical plotting procedures for pattern articulation, creations information
through fractional or all out connections and geographic information sharing. They likewise
encourage the recognizable proof of zones of intrigue and disclosure the outrageous qualities
inside an informational collection. The warmth map is one visual, shading coded portrayal of
information esteems. Each worth takes a shading as indicated by the sort or region that falls
inside the region.
Tableau is a Business Intelligence and examination programming that helps information
researchers and scientist break down information rapidly, effectively, and conveniently. Tableau
let you associate with information in the cloud (for example large information, SQL, spreadsheet,
or Google Analytics and Salesforce), manufacture counts from existing information, make
reference lines and conjectures, make pattern investigations, relapses, and relationships, with his
particular measurable synopses. This product doesn't require composing code and the force
clients can rotate, split, and oversee metadata to streamline information sources. Information
security is of most extreme significance in each undertaking. Tableau permits clients to
manufacture upon their current information security executions. IT directors have the
adaptability to actualize security inside the database with database confirmation, inside Tableau
with consents, or a half and half methodology of both. Security will be implemented whether or
not clients are getting to the information from distributed perspectives on the web, on cell
phones, or through Tableau Desktop and Tableau Prep Builder.

3SECURITY IN COMPUTING AND IT
Recommendations
While working with the numerous attributes it is important to determine the correlation.
The correlation is used to decide whether two factors are connected. In the event that there is any
connection between them, the following stage is to decide the sort of relationship. In the event
that for instance its cost variable "x" increase each time the estimation of the variable "y"
increments. Furthermore, there might be an enthusiasm for how close the relationship is the
factors "x", "y" which prompts the examination of "x" increment comparative with "y"
increment.
The utilization of numerous data connection helps in understanding a lot of information
and discovering connections that help to represent a wonder. The affiliation is normally utilized
for information mining where the assurance of connections between factors in an informational
index prompts their disclosure examples and irregularities. This prompts the disclosure of the
idea of the reason for the marvel. It is expected that two factors are connected, and afterward
lined up with direct connection, which implies that when a variable is changed, at that point the
other is continually evolving.
Conclusion
Through the Tableau tool helps the analysts to initialize data mining processes which
includes setting up the data reading process, predictive analysis, and so on. The key
characteristics of the tool includes multiple data types of files, database sources as well as web
and cloud services. Furthermore, the Rapid Miner is also helpful in designing interactive,
shareable and easy to use dashboards, for the calculated analytics. There are models or
operation that can be analyzed using nodes of operation filter, merge or join data frames as well
validate the created predictive models.