You have been asked by management (manufacturing, healthcar…

You have been asked by management (manufacturing, healthcare, retail, financial, and etc. ) to create a demo using a data analytic or BI tool. It is your responsibility to download and produce outputs using one of the tools. You will need to focus your results on the data set you select. Ensure to address at least one topic covered in Chapters 1-5 with the outputs. The paper should include the following as Header sections. Introduction History of Tool [Discuss the benefits and limitations] Review of the Data [What are you reviewing?] Exploring the Data with the tool Classifications Basic Concepts and Decision Trees Classifications Alternative Techniques Summary of Results References Ensure to use the Author, YYYY APA citations with any outside content. Types of Data Analytic Tools Excel with Solver, but has limitations R Studio Tableau Public has a free trial Microsoft Power BI Search for others with trial options

Introduction

In today’s data-driven world, organizations across various industries such as manufacturing, healthcare, retail, and finance have recognized the importance of data analytics and business intelligence (BI) tools. These tools assist in the process of analyzing, interpreting, and visualizing large volumes of data to gain valuable insights and make informed decisions. In this paper, we will explore one such data analytic or BI tool and demonstrate its capabilities through a demo.

History of Tool

The tool selected for this demo is Tableau Public. Tableau Public is a powerful data visualization and BI tool that allows users to create interactive dashboards, reports, and visualizations. It was developed by Tableau Software and was first released in 2010. Since then, Tableau Public has gained popularity due to its user-friendly interface and extensive capabilities.

Tableau Public offers several benefits to organizations. Firstly, it provides a seamless and intuitive user experience, enabling even non-technical users to work with data and create impactful visualizations. Secondly, it supports a wide range of data sources, including spreadsheets, databases, and cloud platforms, making it compatible with various data formats. Thirdly, Tableau Public offers advanced features such as data blending, data preparation, and predictive analytics, allowing users to derive deeper insights from their data. Furthermore, Tableau Public has a large and active user community, providing ample resources and support for users to learn and enhance their skills.

However, Tableau Public also has certain limitations. As a free public version of Tableau, it has some restrictions on data privacy and security. Users must be cautious when working with sensitive or confidential data. Additionally, while Tableau Public has a robust set of visualization options, some advanced analysis techniques may require the use of Tableau’s paid versions, such as Tableau Desktop or Tableau Server.

Review of the Data

For this demo, we will review a dataset containing sales data from a fictional retail store. The dataset includes information such as product sales, customer demographics, and geographical sales regions. This dataset is representative of the type of data that organizations in the retail industry commonly analyze to gain insights into their sales performance, customer behavior, and market trends.

Exploring the Data with the tool

Using Tableau Public, we can perform various data exploration and analysis tasks on the retail sales dataset. We can start by importing the dataset into Tableau, which can handle large volumes of data efficiently. With Tableau’s intuitive drag-and-drop interface, we can quickly create visualizations such as bar charts, line graphs, and scatter plots to understand the patterns and trends in the sales data.

Classifications Basic Concepts and Decision Trees

One key topic covered in Chapters 1-5 that we can explore with Tableau Public is classification. Classification is a technique used to categorize data into predefined classes or groups. In the context of the retail sales dataset, we can use classification algorithms to predict customer segments or identify factors that contribute to higher sales. Decision trees are a commonly used classification method that can be implemented in Tableau Public to analyze the relationships between different variables and make predictions based on the data.

Classifications Alternative Techniques

Apart from decision trees, Tableau Public offers various alternative techniques for classification, such as logistic regression, support vector machines, and random forests. These techniques provide different approaches to analyzing the data and may yield different insights or predictions. By exploring these alternative techniques, we can compare their performance and select the most suitable classification approach for the retail sales dataset.

Summary of Results

In conclusion, Tableau Public is a versatile data analytic and BI tool that can be effectively used for analyzing and visualizing data in various industries, including manufacturing, healthcare, retail, and finance. Through this demo, we have reviewed the history, benefits, and limitations of Tableau Public and explored its capabilities by analyzing a retail sales dataset. We have specifically focused on the topic of classification and demonstrated how Tableau Public can be used to implement decision trees and explore alternative classification techniques. The results of this analysis can provide valuable insights for organizations seeking to optimize their sales strategies, understand customer behavior, and make data-driven decisions.

References:

[Insert APA formatted references here]