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|Title:||Business Analytics||Authors:||Cvetkoska, Violeta||Issue Date:||2022||Publisher:||Stobi Trejd DOOEL||Source:||Cvetkoska, V. (2022). Business Analytics, 1st. ed., Skopje: Stobi Trejd DOOEL.||Description:||Today's businesses have access to a tremendous amount of data, referred to as "big data", which grows at a remarkable pace each day. A variety of sources can be used to gather the data, including but not limited to: company databases; surveys; social media; the internet; transactions; sensors; etc., and the data can appear in a structured format as reports, or mainly (more than 80%) unstructured as images, videos, and audio. Companies face a major difficulty in the modern data-driven business environment when it comes to combining data from different databases, cleaning it, organizing it, summarizing it, modeling it, and analyzing it in order to make better business decisions. Business analytics methods, techniques, and tools have the key role of transforming data into valuable information that can be used by managers to make better and faster decisions. If organizations have business analysts with proficient analytics skills, they may be able to exploit data as a competitive advantage. Since today's business students will be the future drivers of the company's success, they must be equipped with the knowledge of how to deal with data. This book (the first in North Macedonia of this type) reflects the reality of big data and is written with the goal of preparing business students to be future-ready and highly marketable for their professional career as business analysts. The book is structured into five parts: Part I: Business analytics introduction (Chapters: 1-2) The first chapter explains the reality of big data and how companies can use business analytics to create value (increased profits, customer satisfaction, employees, efficiency, reduced costs, complaints, etc.). The second chapter focuses on manipulating data by applying spreadsheet analytics. Part II: Descriptive analytics (Chapters 3-5) Chapters 3-5 constitute the first pillar of business analytics, descriptive analytics, and refer to descriptive statistics, data exploration using pivot tables, and the central part of business analytics, represented in all its pillars (descriptive, diagnostic, predictive, and descriptive analytics), data visualization (DataViz). Part III: Diagnostic analytics (Chapters: 6-8) The third part refers to the second pillar of business analytics, diagnostic analytics, and contains three chapters (Chapters 6-8) to deal with the methods and tools used for correlation analysis, identify outliers, and what the role of drill-down analysis is. Part IV: Predictive analytics (Chapters 9-11) Chapters 9-11 refer to the third business analytics pillar, predictive analytics, and cover methods, techniques, and tools for forecasting, regression analysis, and data mining with a focus on logistic regression. Part V: Prescriptive analytics (Chapters 12-14) Chapters 12-14 comprise the fourth pillar of business analytics, prescriptive analytics, and focus on methods, techniques, and tools for decision analysis, optimization methods, and the multi-criteria decision method, AHP.||URI:||http://hdl.handle.net/20.500.12188/26070|
|Appears in Collections:||Faculty of Economics 01: Books / Книги|
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