The indispensable guide for all managers and business students who wish to use Microsoft Excel to its full potential. As the industry standard spreadsheet for the analysis and presentation of results, Microsoft Excel is indispensable in the business world. This text provides a practical and straightforward guide to using the functions of Microsoft Excel to their full potential, guiding the reader from basic principles through to the more complicated areas such as modelling, the analysis of charts, reporting, and automatic importing of data from the web directly into an Excel workbook. Bernard Liengme has written this book specifically to meet the requirements of business students and professionals working with Microsoft Excel. The text is illustrated throughout with screen-shots, as well as a wide variety of examples and case studies based in real-world business contexts, introduced with a minimum of maths, and readily adaptable to workplace situations. The new edition has been brought fully up to date with the new Microsoft Office XP release of Excel 2002 but can be used alongside any previous version of Excel, with new Excel 2002 features clearly indicated throughout.

A fast-paced, high-filtered tutorial on Excel for business professionals, managers, and executives, this guide describes how business people can use Excel for analyzing, reporting, and presenting business information. It describes features of Excel important to business users: working with data, creating worksheet formulas, performing business calculations, and more.

The Template-based management (TBM) approach has been used since 2003 across the world in diverse contexts. It has evolved hand-in-hand with the evolution of business: Agile, Blueprints, Canvas, Design Thinking, or Kanban are only few of the many current concepts based on the approach. This book expands and upgrades the author's 2003 book 'Template-driven Consulting' (Springer) by tracing this evolution and offering the current state-of-the-art to practitioners. TBM combines structure and method: pre-structuring diverse processes, it helps to present complex activities and procedures in a simple, clear, and transparent manner and then implement them. The use of TBM ranges from conception or creative work in agencies to designing organizations and strategies, planning and monitoring initiatives and projects, to innovation management and optimizing cost structures, processes, or entire departments and divisions. The book also demonstrates how successful organizations use TBM to methodically and structurally apply the internal know-how in a cost and time-optimal way for attaining sustainable business success. Readers will learn to apply and use TBM, identify its importance, and benefit from a variety of case studies that illustrate the application and use for the entire business and management practice.

This book gathers selected papers that were submitted to the 2021 International Conference on Comprehensible Science (ICCS 2021) that aims to make available the discussion and the publication of papers on all aspects of single and multi-disciplinary research on conference topics. ICCS 2021 held on June 18–19, 2021. An important characteristic feature of conference is the short publication time and worldwide distribution. Written by respected researchers, the book covers a range of innovative topics related to: artificial intelligence research; big data and data mining; blockchain and cryptocurrency; business, finance and accounting and statistics; cyber security systems; ecology systems; educational technologies; engineering and technology; innovative economics; media technologies; medicine, public health and rehabilitation; nutrition and diet researches; physical and material sciences; and smart cities and contracts. This book may be used for private and professional non-commercial research and classroom use (e.g., sharing the contribution by mail or in hard copy form with research colleagues for their professional non-commercial research and classroom use); for use in presentations or handouts for any level students, researchers, etc.; and for the further development of authors’ scientific career (e.g., by citing and attaching contributions to job or grant application).

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R