Perform time series forecasts, linear prediction, and data segmentation with no-code Excel machine learning Key Features • Segment data, regression predictions, and time series forecasts without writing any code • Group multiple variables with K-means using Excel plugin without programming • Build, validate, and predict with a multiple linear regression model and time series forecasts Book Description Data Forecasting and Segmentation Using Microsoft Excel guides you through basic statistics to test whether your data can be used to perform regression predictions and time series forecasts. The exercises covered in this book use real-life data from Kaggle, such as demand for seasonal air tickets and credit card fraud detection. You'll learn how to apply the grouping K-means algorithm, which helps you find segments of your data that are impossible to see with other analyses, such as business intelligence (BI) and pivot analysis. By analyzing groups returned by K-means, you'll be able to detect outliers that could indicate possible fraud or a bad function in network packets. By the end of this Microsoft Excel book, you'll be able to use the classification algorithm to group data with different variables. You'll also be able to train linear and time series models to perform predictions and forecasts based on past data. What you will learn • Understand why machine learning is important for classifying data segmentation • Focus on basic statistics tests for regression variable dependency • Test time series autocorrelation to build a useful forecast • Use Excel add-ins to run K-means without programming • Analyze segment outliers for possible data anomalies and fraud • Build, train, and validate multiple regression models and time series forecasts Who this book is for This book is for data and business analysts as well as data science professionals. MIS, finance, and auditing professionals working with MS Excel will also find this book beneficial.

Explore a variety of Excel features, functions, and productivity tips for various aspects of financial modeling Key Features Explore Excel's financial functions and pivot tables with this updated second edition Build an integrated financial model with Excel for Microsoft 365 from scratch Perform financial analysis with the help of real-world use cases Book DescriptionFinancial modeling is a core skill required by anyone who wants to build a career in finance. Hands-On Financial Modeling with Excel for Microsoft 365 explores financial modeling terminologies with the help of Excel. Starting with the key concepts of Excel, such as formulas and functions, this updated second edition will help you to learn all about referencing frameworks and other advanced components for building financial models. As you proceed, you'll explore the advantages of Power Query, learn how to prepare a 3-statement model, inspect your financial projects, build assumptions, and analyze historical data to develop data-driven models and functional growth drivers. Next, you'll learn how to deal with iterations and provide graphical representations of ratios, before covering best practices for effective model testing. Later, you'll discover how to build a model to extract a statement of comprehensive income and financial position, and understand capital budgeting with the help of end-to-end case studies. By the end of this financial modeling Excel book, you'll have examined data from various use cases and have developed the skills you need to build financial models to extract the information required to make informed business decisions.What you will learn Identify the growth drivers derived from processing historical data in Excel Use discounted cash flow (DCF) for efficient investment analysis Prepare detailed asset and debt schedule models in Excel Calculate profitability ratios using various profit parameters Obtain and transform data using Power Query Dive into capital budgeting techniques Apply a Monte Carlo simulation to derive key assumptions for your financial model Build a financial model by projecting balance sheets and profit and loss Who this book is for This book is for data professionals, analysts, traders, business owners, and students who want to develop and implement in-demand financial modeling skills in their finance, analysis, trading, and valuation work. Even if you don't have any experience in data and statistics, this book will help you get started with building financial models. Working knowledge of Excel is a prerequisite.

Helping tech-savvy marketers and data analysts solve real-world business problems with Excel Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data. This expert book offers the perfect solution. Written by data analysis expert Wayne L. Winston, this practical resource shows you how to tap a simple and cost-effective tool, Microsoft Excel, to solve specific business problems using powerful analytic techniques—and achieve optimum results. Practical exercises in each chapter help you apply and reinforce techniques as you learn. Shows you how to perform sophisticated business analyses using the cost-effective and widely available Microsoft Excel instead of expensive, proprietary analytical tools Reveals how to target and retain profitable customers and avoid high-risk customers Helps you forecast sales and improve response rates for marketing campaigns Explores how to optimize price points for products and services, optimize store layouts, and improve online advertising Covers social media, viral marketing, and how to exploit both effectively Improve your marketing results with Microsoft Excel and the invaluable techniques and ideas in Marketing Analytics: Data-Driven Techniques with Microsoft Excel.

The practices of revenue management and pricing analytics have transformed the transportation and hospitality industries, and are increasingly important in industries as diverse as retail, telecommunications, banking, health care and manufacturing. Segmentation, Revenue Management and Pricing Analytics guides students and professionals on how to identify and exploit revenue management and pricing opportunities in different business contexts. Bodea and Ferguson introduce concepts and quantitative methods for improving profit through capacity allocation and pricing. Whereas most marketing textbooks cover more traditional, qualitative methods for determining customer segments and prices, this book uses historical sales data with mathematical optimization to make those decisions. With hands-on practice and a fundamental understanding of some of the most common analytical models, readers will be able to make smarter business decisions and higher profits. This book will be a useful and enlightening read for MBA students in pricing and revenue management, marketing, and service operations.

Business Finance Planning with Microsoft® Excel® shows how to visualize, plan, and put into motion an idea for creating a start-up company. Microsoft Excel is a tool that makes it easier to build a business financial planning process for a new business venture. With an easy-to follow structure, the book flows as a six-step process: Presenting a case study of a business start-up Creating goals and objectives Determining expenses from those goals and objectives, Estimating potential sales revenue based on what competitors charge their customers Predicting marketing costs Finalizing the financial analysis with a of financial statements. Written around an IT startup case study, the book presents a host of Excel worksheets describing the case study along with accompanying blank forms. Readers can use these forms in their own businesses, so they can build parts of their own business plans as they go. This is intended to be a practical guide that teaches and demonstrates by example, in the end presenting a usable financial model to build and tweak a financial plan with a set of customizable Excel worksheets. The book uses practical techniques to help with the planning processing. These include applying a SWOT (strengths, weaknesses, opportunities, and threats) matrix to evaluate a business idea and SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound) objectives to link together goals. As the book concludes, readers will be able to develop their own income statement, balance sheet, and the cash-flow statement for a full analysis of their new business ideas. Worksheets are available to download from: https://oracletroubleshooter.com/business-finance-planning/app/

This book presents the proceedings of the 9th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2021), held at NIT Mizoram, Aizwal, Mizoram, India, during June 25 – 26, 2021. FICTA conference aims to bring together researchers, scientists, engineers, and practitioners to exchange their new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. This volume covers broad areas of Intelligent Data Engineering and Analytics. The conference papers included herein presents both theoretical as well as practical aspects of data intensive computing, data mining, big data, knowledge management, intelligent data acquisition and processing from sensors, data communication networks protocols and architectures, etc. The volume will also serve as a knowledge centre for students of post-graduate level in various engineering disciplines.

INTRODUCTION TO LOGISTICS SYSTEMS MANAGEMENT The updated new edition of the award-winning introductory textbook on logistics system management Introduction to Logistics Systems Management provides an in-depth introduction to the methodological aspects of planning, organization, and control of logistics for organizations in the private, public and non-profit sectors. Based on the authors’ extensive teaching, research, and industrial consulting experience, this classic textbook is used in universities worldwide to teach students the use of quantitative methods for solving complex logistics problems. Fully updated and revised, the third edition places increased emphasis on the complexity and flexibility required by modern logistics systems. In this context, the extensive use of data, descriptive analytics, predictive models, and optimization techniques will be invaluable to support the decisions and actions of logistics and supply chain managers. Throughout the book, brand-new case studies and numerical examples illustrate how various methods can be used in industrial and service logistics to reduce costs and improve service levels. The book: includes new models and techniques that have emerged over the past decade; describes methodologies for logistics decision making, forecasting, logistics system design, procurement, warehouse management, and freight transportation management; includes end-of-chapter exercises, Microsoft® Excel® files and Python computer codes for each algorithm covered; includes access to a companion website with additional exercises, links to video tutorials, and supplementary teaching material. To facilitate creation of course material, additional LaTeX source data containing the formulae, optimization models, tables and algorithms described in the book is available to instructors. Introduction to Logistics Systems Management, Third Edition remains an essential textbook for senior undergraduate and graduate students in engineering, computer science, and anagement science courses. It is also a highly useful reference for academic researchers and industry practitioners alike.

OBJECTIVES The book objectives provide a full delivery of information on the fields of artificial intelligence (AI) and machine learning (ML) to educators, students and practitioners of marketing. By explaining AI and ML terminology and its applications including marketing, the book is designed to inform and educate. Marketing use of AI and ML has exploded in recent decades as marketers have seen the considerable benefits of these two technologies. It is understood and explained that AI deals with 'Intelligent behaviour' by machines rather than natural intelligence found in humans and animals, it is the machine mimicking ' cognitive functions' that humans associate with the mind in learning, expression and problem solving and much more.

This book offers a comprehensive and readable introduction to modern business and data analytics. It is based on the use of Excel, a tool that virtually all students and professionals have access to. The explanations are focused on understanding the techniques and their proper application, and are supplemented by a wealth of in-chapter and end-of-chapter exercises. In addition to the general statistical methods, the book also includes Monte Carlo simulation and optimization. The second edition has been thoroughly revised: new topics, exercises and examples have been added, and the readability has been further improved. The book is primarily intended for students in business, economics and government, as well as professionals, who need a more rigorous introduction to business and data analytics – yet also need to learn the topic quickly and without overly academic explanations.

Praise for the First Edition " full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing." —Research magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature." —computingreviews.com Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The Second Edition now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods Summaries at the start of each chapter that supply an outline of key topics The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions. Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.