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| excel data analysis project: Mastering Excel Through Projects Hong Zhou, 2022 Master Excel in less than two weeks with this unique project-based book! Let's face it, we all master skills in our own way, but building a soup-to-nuts project is one of the best ways to make learning stick and get up to speed quickly. Whether you are just getting started with Excel or are an experienced user, this book will elevate your knowledge and skills. For a beginner, the micro examples in each chapter will warm you up before you dive into the projects. For experienced users, the projects, especially those with table setup considerations, will help you become more creative in your interactions with Excel. Readers will benefit from building eight unique projects, each covering a different topic, including a word game, a food nutrition ranking, a payroll (tax withholding) calculation, an encryption, a two-way table, a Kaplan-Meier analysis, a data analysis via a pivot table and the K-means Clustering data mining method. Through these projects, you will experience firsthand how Excel skills are organized together to accomplish tasks that sound complex and daunting when first described. Get started with a word game which asks users to find English words that amount to exactly 100 points, with each letter of the alphabet assigned a point 1, 2, 3, ... 26, respectively. You will disassemble a word into letters and then sum up their points, and then take it one step further, contemplating how to make the completed Excel worksheet more user friendly and completely automated. Increasingly challenging tasks like this example build on what you have learned and increase your confidence along the way, ensuring your mastery of Excel. What You Will Learn Gain confidence to tackle a challenging Excel-related mission, even those that seem impossible Become skilled in the creative uses of Excel formulas and functions and other built-in features Appreciate the art of refining worksheets to maximize automation Understand the value of treating each worksheet as a unique product This book is for people who are interested in learning Excel as quickly and efficiently as possible. While Excel beginners and intermediate users are the primary audience, experienced Excel users might also discover new skills and ways of working with Excel. Hong Zhou is a professor of computer science and mathematics at the University of Saint Joseph in Connecticut. Before returning to school for his doctoral degree, Dr. Zhou worked as a Java developer in Silicon Valley. Since 2004, Dr. Zhou has been teaching various courses in computer science, data science, mathematics, statistics, and informatics. His major research interests include data mining, bioinformatics, software agents, and blockchain. Dr. Zhou became interested in Excel through teaching computer skills and using them for research purposes; for example, applying Excel in teaching data mining, encryption, and health informatics. He also enjoys applying his Excel skills to help colleagues in their research projects. |
| excel data analysis project: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| excel data analysis project: Excel 2019 Project Book Gary Bronson, Jeffrey Hsu, 2021-09-29 Designed as a project and case-oriented approach to learning Excel, the emphasis of this book is on learning by doing. The book presents a series of progressively reinforcing problem sets, which allow the exploration of real-life business problems. First, the background, theory, formulas, and calculations are discussed, followed by the design of Excel spreadsheets, which support the creation of effective spreadsheets for these applications. Finally, the proper solution and other related aspects are discussed, forming a cohesive set of practical application problems. Some of the topics explored include amortization tables, weighted averages, cash flows, payroll calculations, break even analysis, and spreadsheet databases. Excel techniques include formulas and functions, cell addressing, conditional and lookup functions, graphs, sorting, and filtering. FEATURES: Provides 30 projects, several How-to Guides, and Application Types to learn Excel skills using problems, applications, and case studies featuring practical business problems Explores formulas and functions, financial functions, cell addressing, conditional functions, lookup functions, graphs, sorting, and filtering, amortization tables, future values of an investment, weighted averages, cash flows, payroll calculations, break even analysis, economic order quantity, spreadsheet databases, and more Instructor resources available with four Excel video tutorials and images from the text. |
| excel data analysis project: Data Analysis Using SQL and Excel Gordon S. Linoff, 2010-09-16 Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like. |
| excel data analysis project: Using Microsoft Excel for Social Research Charlotte Brookfield, 2021-01-20 Full of practical advice and real-world examples, this step-by-step guide offers you an accessible introduction to doing quantitative social research using Microsoft Excel. |
| excel data analysis project: Microsoft Excel Data Analysis and Business Modeling (Office 2021 and Microsoft 365) Wayne Winston, 2021-12-17 Master business modeling and analysis techniques with Microsoft Excel and transform data into bottom-line results. Award-winning educator Wayne Winston's hands-on, scenario-focused guide helps you use today's Excel to ask the right questions and get accurate, actionable answers. More extensively updated than any previous edition, new coverage ranges from one-click data analysis to STOCKHISTORY, dynamic arrays to Power Query, and includes six new chapters. Practice with over 900 problems, many based on real challenges faced by working analysts. Solve real problems with Microsoft Excel—and build your competitive advantage Quickly transition from Excel basics to sophisticated analytics Use recent Power Query enhancements to connect, combine, and transform data sources more effectively Use the LAMBDA and LAMBDA helper functions to create Custom Functions without VBA Use New Data Types to import data including stock prices, weather, information on geographic areas, universities, movies, and music Build more sophisticated and compelling charts Use the new XLOOKUP function to revolutionize your lookup formulas Master new Dynamic Array formulas that allow you to sort and filter data with formulas and find all UNIQUE entries Illuminate insights from geographic and temporal data with 3D Maps Improve decision-making with probability, Bayes' theorem, and Monte Carlo simulation and scenarios Use Excel trend curves, multiple regression, and exponential smoothing for predictive analytics Use Data Model and Power Pivot to effectively build and use relational data sources inside an Excel workbook |
| excel data analysis project: Advanced Excel for Scientific Data Analysis Robert De Levie, 2004 This guide to Excel focuses on three areas--least squares, Fourier transformation, and digital simulation. It illustrates the techniques with detailed examples, many drawn from the scientific literature. It also includes and describes a number of sample macros and functions to facilitate common data analysis tasks. De Levie is affiliated with Bowdoin College. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com). |
| excel data analysis project: Excel Data Analysis For Dummies Paul McFedries, 2018-11-13 Take Excel to the next level Excel is the world’s leading spreadsheet application. It’s a key module in Microsoft Office—the number-one productivity suite—and it is the number-one business intelligence tool. An Excel dashboard report is a visual presentation of critical data and uses gauges, maps, charts, sliders, and other graphical elements to present complex data in an easy-to-understand format. Excel Data Analysis For Dummies explains in depth how to use Excel as a tool for analyzing big data sets. In no time, you’ll discover how to mine and analyze critical data in order to make more informed business decisions. Work with external databases, PivotTables, and Pivot Charts Use Excel for statistical and financial functions and data sharing Get familiar with Solver Use the Small Business Finance Manager If you’re familiar with Excel but lack a background in the technical aspects of data analysis, this user-friendly book makes it easy to start putting it to use for you. |
| excel data analysis project: Statistical Analysis Conrad Carlberg, 2014-04-04 Use Excel 2013’s statistical tools to transform your data into knowledge Conrad Carlberg shows how to use Excel 2013 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features, including recently introduced consistency functions. Along the way, he clarifies confusing statistical terminology and helps you avoid common mistakes. You’ll learn how to use correlation and regression, analyze variance and covariance, and test statistical hypotheses using the normal, binomial, t, and F distributions. To help you make accurate inferences based on samples from a population, this edition adds two more chapters on inferential statistics, covering crucial topics ranging from experimental design to the statistical power of F tests. Becoming an expert with Excel statistics has never been easier! You’ll find crystal-clear instructions, insider insights, and complete step-by-step projects—all complemented by extensive web-based resources. Master Excel’s most useful descriptive and inferential statistical tools Tell the truth with statistics—and recognize when others don’t Accurately summarize sets of values Infer a population’s characteristics from a sample’s frequency distribution Explore correlation and regression to learn how variables move in tandem Use Excel consistency functions such as STDEV.S() and STDEV.P() Test differences between two means using z tests, t tests, and Excel’s Data Analysis Add-in Use ANOVA to test differences between more than two means Explore statistical power by manipulating mean differences, standard errors, directionality, and alpha Take advantage of Recommended PivotTables, Quick Analysis, and other Excel 2013 shortcuts |
| excel data analysis project: Advanced Analytics with Excel 2019 Manisha Nigam, 2020-06-19 Explore different ways and methods to consolidate data, complex analysis, and prediction or forecast based on trends Ê KEY FEATURESÊ _ Ê Ê Use the Analysis ToolPak to perform complex Data analysis _ Ê Ê Get well versed with the formulas, functions, and components in Excel _ Ê Ê Handy templates to give you a head start _ Ê Ê Usage of multiple examples to explain the application in a real-world scenario _ Ê Ê Implement macros for your everyday tasks that will help you save your time _ Ê Ê Explore different Charts types for Data visualization Ê Ê DESCRIPTION Book explains and simplify the usage of Excel features and functionalities, with the help of examples. It starts with ÔGetting Started with ExcelÕ and ÔPerforming functions with shortcut keysÕ which will help you in getting started with Excel. Then ÔFormulas and FunctionsÕ gives an initial understanding of what are operators, formulas, functions, their components. Further ÔData Visualization with new Charts typesÕ, ÔGantt and Milestone chartÕ, ÔSmartArt and Organization ChartÕ give details on the different chart types available in Excel. Ê In the intermediate section you will learn ÔGet creative with Icons, 3D models, Digital InkingÕ details multiple new and improved features that got introduced to enhance the visual presentation. In the end, Chapters ÔMail Merge using ExcelÕ, ÔCreate Custom Excel TemplateÕ and ÔMacros in ExcelÕ explain the Excel features that help in automating tasks. You will learn how to generate multiple documents automatically with customization, create and use your own templates and use of macros to do repeated task automatically. And at last Chapter ÔGet help for your problemÕ lists few problem statements and their probable solutions with references to the Excel feature or functionality that can be used to resolve the problem. Ê Ê Ê WHAT WILL YOU LEARN _ Ê Ê Get familiar with the most used advanced Excel formulas and functions for Data analysis _ Ê Ê Learn how to create a Gantt / Timeline / Milestone Chart in Excel _ Ê Ê Use charts for Better Data visualization _ Ê Ê Build organization charts with SmartArt tools in Excel _ Ê Ê Use the Analysis ToolPak & Power Pivots to perform complex Data analysis _ Ê Ê Learn how to link and share workbooks for automatic updates ÊÊ WHO THIS BOOK IS FOR This book is for professionals from any domain, who are searching for shortcuts & advanced methods to resolve their daily problems.Ê Ê Table of Contents 1.Ê Ê Ê Getting Started with Excel 2.Ê Ê Ê Perform Functions with Shortcut Keys 3.Ê Ê Ê Formulas and Functions 4.Ê Ê Ê Data Visualization with New Chart types 5.Ê Ê Ê Gantt and Milestone Chart 6.Ê Ê Ê SmartArt & Organization Chart 7.Ê Ê Get creative with Icons, 3D models, Digital Inking 8.Ê Ê Ê Putting Data in perspective with Pivots 9.Ê Ê Ê Complex Data Analysis using ToolPak 10.Ê Forecasting in Excel 11.Ê Mail Merge using Excel 12.Ê Create Custom Excel Template 13.Ê Macros in Excel 14.Ê Get help for your problem |
| excel data analysis project: Excel Data Analysis Hector Guerrero, 2018-12-14 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. |
| excel data analysis project: Excel 2016 Bible John Walkenbach, 2015-10-09 The complete guide to Excel 2016, from Mr. Spreadsheet himself Whether you are just starting out or an Excel novice, the Excel 2016 Bible is your comprehensive, go-to guide for all your Excel 2016 needs. Whether you use Excel at work or at home, you will be guided through the powerful new features and capabilities by expert author and Excel Guru John Walkenbach to take full advantage of what the updated version offers. Learn to incorporate templates, implement formulas, create pivot tables, analyze data, and much more. Navigate this powerful tool for business, home management, technical work, and much more with the only resource you need, Excel 2016 Bible. Create functional spreadsheets that work Master formulas, formatting, pivot tables, and more Get acquainted with Excel 2016's new features and tools Customize downloadable templates and worksheets Whether you need a walkthrough tutorial or an easy-to-navigate desk reference, the Excel 2016 Bible has you covered with complete coverage and clear expert guidance. |
| excel data analysis project: Microsoft Excel 2019 Data Analysis and Business Modeling Wayne Winston, 2019-03-28 Master business modeling and analysis techniques with Microsoft Excel 2019 and Office 365 and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands-on, scenario-focused guide helps you use Excel to ask the right questions and get accurate, actionable answers. New coverage ranges from Power Query/Get & Transform to Office 365 Geography and Stock data types. Practice with more than 800 problems, many based on actual challenges faced by working analysts. Solve real business problems with Excel—and build your competitive advantage: Quickly transition from Excel basics to sophisticated analytics Use PowerQuery or Get & Transform to connect, combine, and refine data sources Leverage Office 365’s new Geography and Stock data types and six new functions Illuminate insights from geographic and temporal data with 3D Maps Summarize data with pivot tables, descriptive statistics, histograms, and Pareto charts Use Excel trend curves, multiple regression, and exponential smoothing Delve into key financial, statistical, and time functions Master all of Excel’s great charts Quickly create forecasts from historical time-based data Use Solver to optimize product mix, logistics, work schedules, and investments—and even rate sports teams Run Monte Carlo simulations on stock prices and bidding models Learn about basic probability and Bayes’ Theorem Use the Data Model and Power Pivot to effectively build and use relational data sources inside an Excel workbook Automate repetitive analytics tasks by using macros |
| excel data analysis project: Data Analytics in Project Management Seweryn Spalek, J. Davidson Frame, Yanping Chen, Carl Pritchard, Alfonso Bucero, Werner Meyer, Ryan Legard, Michael Bragen, Klas Skogmar, Deanne Larson, Bert Brijs, 2019-01-01 Data Analytics in Project Management. Data analytics plays a crucial role in business analytics. Without a rigid approach to analyzing data, there is no way to glean insights from it. Business analytics ensures the expected value of change while that change is implemented by projects in the business environment. Due to the significant increase in the number of projects and the amount of data associated with them, it is crucial to understand the areas in which data analytics can be applied in project management. This book addresses data analytics in relation to key areas, approaches, and methods in project management. It examines: • Risk management • The role of the project management office (PMO) • Planning and resource management • Project portfolio management • Earned value method (EVM) • Big Data • Software support • Data mining • Decision-making • Agile project management Data analytics in project management is of increasing importance and extremely challenging. There is rapid multiplication of data volumes, and, at the same time, the structure of the data is more complex. Digging through exabytes and zettabytes of data is a technological challenge in and of itself. How project management creates value through data analytics is crucial. Data Analytics in Project Management addresses the most common issues of applying data analytics in project management. The book supports theory with numerous examples and case studies and is a resource for academics and practitioners alike. It is a thought-provoking examination of data analytics applications that is valuable for projects today and those in the future. |
| excel data analysis project: Dear Data Giorgia Lupi, Stefanie Posavec, 2016-09-13 Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates the infinitesimal, incomplete, imperfect, yet exquisitely human details of life, in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere. |
| excel data analysis project: Beginning Excel What-If Data Analysis Tools Paul Cornell, 2006-11-09 * The only book on the market dedicated to the What-If functions and tools built into Excel, which are key to many business analysis scenarios. * Focused and to the point – where other Excel books force the reader to wade through 100’s of pages of related information and commit hours of reading, this book gets straight to the topic of teaching the reader about What-If scenario tools in Excel. * Offers the deepest tutorial treatment of the Solver add-in, the Goal Seeker and the other What-If scenario analysis tools, in a practical, hands-on exercises approach to using Excel to get business results. |
| excel data analysis project: Analyzing Business Data with Excel Gerald Knight, 2006-01-03 As one of the most widely used desktop applications ever created, Excel is familiar to just about everyone with a computer and a keyboard. Yet most of us don't know the full extent of what Excel can do, mostly because of its recent growth in power, versatility, and complexity. The truth is that there are many ways Excel can help make your job easier-beyond calculating sums and averages in a standard spreadsheet. Analyzing Business Data with Excel shows you how to solve real-world business problems by taking Excel's data analysis features to the max. Rather than focusing on individual Excel functions and features, the book keys directly on the needs of business users. Most of the chapters start with a business problem or question, and then show you how to create pointed spreadsheets that address common data analysis issues. Aimed primarily at experienced Excel users, the book doesn't spend much time on the basics. After introducing some necessary general tools, it quickly moves into more specific problem areas, such as the following: Statistics Pivot tables Workload forecasting Modeling Measuring quality Monitoring complex systems Queuing Optimizing Importing data If you feel as though you're getting shortchanged by your overall application of Excel, Analyzing Business Data with Excel is just the antidote. It addresses the growing Excel data analysis market head on. Accountants, managers, analysts, engineers, and supervisors-one and all-will learn how to turn Excel functionality into actual solutions for the business problems that confront them. |
| excel data analysis project: Microsoft Excel 2013 Data Analysis and Business Modeling Wayne Winston, 2014-01-15 Master business modeling and analysis techniques with Microsoft Excel 2013, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands-on, scenario-focused guide shows you how to use the latest Excel tools to integrate data from multiple tables—and how to effectively build a relational data source inside an Excel workbook. Solve real business problems with Excel—and sharpen your edge Summarize data with PivotTables and Descriptive Statistics Explore new trends in predictive and prescriptive analytics Use Excel Trend Curves, multiple regression, and exponential smoothing Master advanced Excel functions such as OFFSET and INDIRECT Delve into key financial, statistical, and time functions Make your charts more effective with the Power View tool Tame complex optimization problems with Excel Solver Run Monte Carlo simulations on stock prices and bidding models Apply important modeling tools such as the Inquire add-in |
| excel data analysis project: Advanced Excel Essentials Jordan Goldmeier, 2014-11-10 Advanced Excel Essentials is the only book for experienced Excel developers who want to channel their skills into building spreadsheet applications and dashboards. This book starts from the assumption that you are well-versed in Excel and builds on your skills to take them to an advanced level. It provides the building blocks of advanced development and then takes you through the development of your own advanced spreadsheet application. For the seasoned analyst, accountant, financial professional, management consultant, or engineer—this is the book you’ve been waiting for! Author Jordan Goldmeier builds on a foundation of industry best practices, bringing his own forward-thinking approach to Excel and rich real-world experience, to distill a unique blend of advanced essentials. Among other topics, he covers advanced formula concepts like array formulas and Boolean logic and provides insight into better code and formulas development. He supports that insight by showing you how to build correctly with hands-on examples. |
| excel data analysis project: Health Services Research and Analytics Using Excel Nalin Johri, PhD, MPH, 2020-02-01 Your all-in-one resource for quantitative, qualitative, and spatial analyses in Excel® using current real-world healthcare datasets. Health Services Research and Analytics Using Excel® is a practical resource for graduate and advanced undergraduate students in programs studying healthcare administration, public health, and social work as well as public health workers and healthcare managers entering or working in the field. This book provides one integrated, application-oriented resource for common quantitative, qualitative, and spatial analyses using only Excel. With an easy-to-follow presentation of qualitative and quantitative data, students can foster a balanced decision-making approach to financial data, patient statistical data and utilization information, population health data, and quality metrics while cultivating analytical skills that are necessary in a data-driven healthcare world. Whereas Excel is typically considered limited to quantitative application, this book expands into other Excel applications based on spatial analysis and data visualization represented through 3D Maps as well as text analysis using the free add-in in Excel. Chapters cover the important methods and statistical analysis tools that a practitioner will face when navigating and analyzing data in the public domain or from internal data collection at their health services organization. Topics covered include importing and working with data in Excel; identifying, categorizing, and presenting data; setting bounds and hypothesis testing; testing the mean; checking for patterns; data visualization and spatial analysis; interpreting variance; text analysis; and much more. A concise overview of research design also provides helpful background on how to gather and measure useful data prior to analyzing in Excel. Because Excel is the most common data analysis software used in the workplace setting, all case examples, exercises, and tutorials are provided with the latest updates to the Excel software from Office365 ProPlus® and newer versions, including all important “Add-ins” such as 3D Maps, MeaningCloud, and Power Pivots, among others. With numerous practice problems and over 100 step-by-step videos, Health Services Research and Analytics Using Excel® is an extremely practical tool for students and health service professionals who must know how to work with data, how to analyze it, and how to use it to improve outcomes unique to healthcare settings. Key Features: Provides a competency-based analytical approach to health services research using Excel Includes applications of spatial analysis and data visualization tools based on 3D Maps in Excel Lists select sources of useful national healthcare data with descriptions and website information Chapters contain case examples and practice problems unique to health services All figures and videos are applicable to Office365 ProPlus Excel and newer versions Contains over 100 step-by-step videos of Excel applications covered in the chapters and provides concise video tutorials demonstrating solutions to all end-of-chapter practice problems Robust Instructor ancillary package that includes Instructor’s Manual, PowerPoints, and Test Bank |
| excel data analysis project: Data Analysis with Excel® Les Kirkup, 2002-03-07 An essential introduction to data analysis techniques using spreadsheets, for undergraduate and graduate students. |
| excel data analysis project: DATA ANALYSIS AND BUSINESS MODELLING USING MICROSOFT EXCEL Hansa Lysander Manohar, 2017-03-30 |
| excel data analysis project: Data Visualization with Excel Dashboards and Reports Dick Kusleika, 2021-02-05 Large corporations like IBM and Oracle are using Excel dashboards and reports as a Business Intelligence tool, and many other smaller businesses are looking to these tools in order to cut costs for budgetary reasons. An effective analyst not only has to have the technical skills to use Excel in a productive manner but must be able to synthesize data into a story, and then present that story in the most impactful way. Microsoft shows its recognition of this with Excel. In Excel, there is a major focus on business intelligence and visualization. Data Visualization with Excel Dashboards and Reports fills the gap between handling data and synthesizing data into meaningful reports. This title will show readers how to think about their data in ways other than columns and rows. Most Excel books do a nice job discussing the individual functions and tools that can be used to create an Excel Report. Titles on Excel charts, Excel pivot tables, and other books that focus on Tips and Tricks are useful in their own right; however they don't hit the mark for most data analysts. The primary reason these titles miss the mark is they are too focused on the mechanical aspects of building a chart, creating a pivot table, or other functionality. They don't offer these topics in the broader picture by showing how to present and report data in the most effective way. What are the most meaningful ways to show trending? How do you show relationships in data? When is showing variances more valuable than showing actual data values? How do you deal with outliers? How do you bucket data in the most meaningful way? How do you show impossible amounts of data without inundating your audience? In Data Visualization with Excel Reports and Dashboards, readers will get answers to all of these questions. Part technical manual, part analytical guidebook; this title will help Excel users go from reporting data with simple tables full of dull numbers, to creating hi-impact reports and dashboards that will wow management both visually and substantively. This book offers a comprehensive review of a wide array of technical and analytical concepts that will help users create meaningful reports and dashboards. After reading this book, the reader will be able to: Analyze large amounts of data and report their data in a meaningful way Get better visibility into data from different perspectives Quickly slice data into various views on the fly Automate redundant reporting and analyses Create impressive dashboards and What-If analyses Understand the fundamentals of effective visualization Visualize performance comparisons Visualize changes and trends over time |
| excel data analysis project: Development Research in Practice Kristoffer Bjärkefur, Luíza Cardoso de Andrade, Benjamin Daniels, Maria Ruth Jones, 2021-07-16 Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University |
| excel data analysis project: Hands-On Machine Learning with Microsoft Excel 2019 Julio Cesar Rodriguez Martino, 2019-04-30 A practical guide to getting the most out of Excel, using it for data preparation, applying machine learning models (including cloud services) and understanding the outcome of the data analysis. Key FeaturesUse Microsoft's product Excel to build advanced forecasting models using varied examples Cover range of machine learning tasks such as data mining, data analytics, smart visualization, and more Derive data-driven techniques using Excel plugins and APIs without much code required Book Description We have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel. The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed. At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning. What you will learnUse Excel to preview and cleanse datasetsUnderstand correlations between variables and optimize the input to machine learning modelsUse and evaluate different machine learning models from ExcelUnderstand the use of different visualizationsLearn the basic concepts and calculations to understand how artificial neural networks workLearn how to connect Excel to the Microsoft Azure cloudGet beyond proof of concepts and build fully functional data analysis flowsWho this book is for This book is for data analysis, machine learning enthusiasts, project managers, and someone who doesn't want to code much for performing core tasks of machine learning. Each example will help you perform end-to-end smart analytics. Working knowledge of Excel is required. |
| excel data analysis project: Microsoft Excel 2010 Wayne L. Winston, 2011 An award-winning business professor and corporate consultant shares the best of his real-world experience in this practical, scenario-focused guide--fully updated for Excel 2010. |
| excel data analysis project: R Through Excel Richard M. Heiberger, Erich Neuwirth, 2010-01-23 In this book, the authors build on RExcel, a free add-in for Excel that can be downloaded from the R distribution network. RExcel seamlessly integrates the entire set of R's statistical and graphical methods into Excel, allowing students to focus on statistical methods and concepts and minimizing the distraction of learning a new programming language. |
| excel data analysis project: Managing Data Using Excel Mark Gardener, 2015-04-20 Microsoft Excel is a powerful tool that can transform the way you use data. This book explains in comprehensive and user-friendly detail how to manage, make sense of, explore and share data, giving scientists at all levels the skills they need to maximize the usefulness of their data. Readers will learn how to use Excel to: * Build a dataset – how to handle variables and notes, rearrangements and edits to data. * Check datasets – dealing with typographic errors, data validation and numerical errors. * Make sense of data – including datasets for regression and correlation; summarizing data with averages and variability; and visualizing data with graphs, pivot charts and sparklines. * Explore regression data – finding, highlighting and visualizing correlations. * Explore time-related data – using pivot tables, sparklines and line plots. * Explore association data – creating and visualizing contingency tables. * Explore differences – pivot tables and data visualizations including box-whisker plots. * Share data – methods for exporting and sharing your datasets, summaries and graphs. Alongside the text, Have a Go exercises, Tips and Notes give readers practical experience and highlight important points, and helpful self-assessment exercises and summary tables can be found at the end of each chapter. Supplementary material can also be downloaded on the companion website. Managing Data Using Excel is an essential book for all scientists and students who use data and are seeking to manage data more effectively. It is aimed at scientists at all levels but it is especially useful for university-level research, from undergraduates to postdoctoral researchers. |
| excel data analysis project: Data-Driven Security Jay Jacobs, Bob Rudis, 2014-02-24 Uncover hidden patterns of data and respond with countermeasures Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches and attacks. This careful guide explores two of the most powerful data analysis and visualization. You'll soon understand how to harness and wield data, from collection and storage to management and analysis as well as visualization and presentation. Using a hands-on approach with real-world examples, this book shows you how to gather feedback, measure the effectiveness of your security methods, and make better decisions. Everything in this book will have practical application for information security professionals. Helps IT and security professionals understand and use data, so they can thwart attacks and understand and visualize vulnerabilities in their networks Includes more than a dozen real-world examples and hands-on exercises that demonstrate how to analyze security data and intelligence and translate that information into visualizations that make plain how to prevent attacks Covers topics such as how to acquire and prepare security data, use simple statistical methods to detect malware, predict rogue behavior, correlate security events, and more Written by a team of well-known experts in the field of security and data analysis Lock down your networks, prevent hacks, and thwart malware by improving visibility into the environment, all through the power of data and Security Using Data Analysis, Visualization, and Dashboards. |
| excel data analysis project: Guerrilla Data Analysis Using Microsoft Excel MrExcel's Holy Macro! Books, Oz du Soleil, Bill Jelen, 2024-09-26 Master Excel data analysis with this hands-on guide. Learn efficient techniques, advanced functions, and best practices for real-world scenarios. Key Features Hands-on techniques for efficient Excel data analysis Advanced functions and best practices for real-world scenarios Step-by-step guidance on complex tasks like data validation and dynamic arrays Book DescriptionUnlock Microsoft Excel's hidden potential with this dynamic guide designed for data professionals and enthusiasts. You'll start by reviewing Excel basics before advancing to powerful tools like Excel Tables, Pivot Tables, and Power Query. Each chapter enhances your ability to analyze and visualize data efficiently, from complex lookups and dynamic arrays to essential data validation techniques that ensure accuracy and integrity in your spreadsheets. As you progress, you'll learn how to protect your work with advanced sheet protection methods and collaboration tools for seamless teamwork. The book also covers sophisticated functions like INDIRECT, OFFSET, and LET, preparing you to tackle complex data challenges. Additionally, you'll receive critical advice on avoiding the pitfalls of machine learning-driven features and maintaining clean, organized data. By the end of the guide, you'll have mastered Excel's advanced capabilities, empowering you to streamline workflows, optimize data processes, and make confident, data-driven decisions. This guide is your comprehensive resource for transforming your approach to data analysis with Excel.What you will learn Master Excel tables and dynamic spreadsheets Use VLOOKUP and XLOOKUP effectively Create and manipulate PivotTables Clean and validate data with Excel tools Apply conditional formatting and de-duping techniques Implement data models and relationships in Excel Who this book is for This book is ideal for data analysts, business professionals, and Excel users who need to enhance their data analysis skills. Readers should have a basic understanding of Excel and be familiar with its interface. No advanced Excel knowledge is required, but a willingness to learn and apply new techniques is essential. |
| excel data analysis project: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
| excel data analysis project: Python for Excel Felix Zumstein, 2021-03-04 While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently. Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started. Use Python without extensive programming knowledge Get started with modern tools, including Jupyter notebooks and Visual Studio code Use pandas to acquire, clean, and analyze data and replace typical Excel calculations Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports Use xlwings to build interactive Excel tools that use Python as a calculation engine Connect Excel to databases and CSV files and fetch data from the internet using Python code Use Python as a single tool to replace VBA, Power Query, and Power Pivot |
| excel data analysis project: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| excel data analysis project: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| excel data analysis project: Analytics Phil Simon, 2017-07-03 For years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all. This has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends—and act upon them until it's too late. But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating? What if there were a better way to do analytics? Fortunately, you're in luck... Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon. Analytics: The Agile Way demonstrates how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you. Through a series of case studies and examples, Analytics: The Agile Way demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors. |
| excel data analysis project: Marketing Analytics Wayne L. Winston, 2014-01-08 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. |
| excel data analysis project: #MakeoverMonday Andy Kriebel, Eva Murray, 2018-10-02 Explore different perspectives and approaches to create more effective visualizations #MakeoverMonday offers inspiration and a giant dose of perspective for those who communicate data. Originally a small project in the data visualization community, #MakeoverMonday features a weekly chart or graph and a dataset that community members reimagine in order to make it more effective. The results have been astounding; hundreds of people have contributed thousands of makeovers, perfectly illustrating the highly variable nature of data visualization. Different takes on the same data showed a wide variation of theme, focus, content, and design, with side-by-side comparisons throwing more- and less-effective techniques into sharp relief. This book is an extension of that project, featuring a variety of makeovers that showcase various approaches to data communication and a focus on the analytical, design and storytelling skills that have been developed through #MakeoverMonday. Paging through the makeovers ignites immediate inspiration for your own work, provides insight into different perspectives, and highlights the techniques that truly make an impact. Explore the many approaches to visual data communication Think beyond the data and consider audience, stakeholders, and message Design your graphs to be intuitive and more communicative Assess the impact of layout, color, font, chart type, and other design choices Creating visual representation of complex datasets is tricky. There’s the mandate to include all relevant data in a clean, readable format that best illustrates what the data is saying—but there is also the designer’s impetus to showcase a command of the complexity and create multidimensional visualizations that “look cool.” #MakeoverMonday shows you the many ways to walk the line between simple reporting and design artistry to create exactly the visualization the situation requires. |
| excel data analysis project: Statistics Using Excel Succinctly Charles Zaiontz, 2017-02-01 Learn the ins and outs of Microsoft Excel's statistical capabilities. Author Charles Zaiontz will help you familiarize yourself with an often overlooked but very powerful set of tools. With Statistics Using Excel Succinctly, you will be able to maximize your Excel skills. |
| excel data analysis project: Mastering Shiny Hadley Wickham, 2021-04-29 Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production |
| excel data analysis project: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
What does the "@" symbol mean in Excel formula (outside a table)
Oct 24, 2021 · Excel has recently introduced a huge feature called Dynamic arrays. And along with that, Excel also started to make a " substantial upgrade " to their formula language. One …
excel - How to show current user name in a cell? - Stack Overflow
if you don't want to create a UDF in VBA or you can't, this could be an alternative. =Cell("Filename",A1) this will give you the full file name, and from this you could get the user …
How to represent a DateTime in Excel - Stack Overflow
The underlying data type of a datetime in Excel is a 64-bit floating point number where the length of a day equals 1 and 1st Jan 1900 00:00 equals 1. So 11th June 2009 17:30 is about …
excel - Check whether a cell contains a substring - Stack Overflow
Sep 4, 2013 · Is there an in-built function to check if a cell contains a given character/substring? It would mean you can apply textual functions like Left/Right/Mid on a conditional basis without …
How to keep one variable constant with other one changing with …
The $ tells excel not to adjust that address while pasting the formula into new cells. Since you are dragging across rows, you really only need to freeze the row part: =(B0+4)/A$0
Excel: Searching for multiple terms in a cell - Stack Overflow
Feb 11, 2013 · In addition to the answer of @teylyn, I would like to add that you can put the string of multiple search terms inside a SINGLE cell (as opposed to using a different cell for each …
How to freeze the =today() function once data has been entered
Aug 2, 2015 · Excel's default format handling doesn't know to format this as date - so you would need to do this separately. More work than Ctrl + ; , but there might be some other use-cases …
excel - Return values from the row above to the current row
Jun 15, 2012 · To solve this problem in Excel, usually I would just type in the literal row number of the cell above, e.g., if I'm typing in Cell A7, I would use the formula =A6. Then if I copied that …
Assign a value to a cell depending on content of another cell
Jan 16, 2020 · I am trying to use the IF function to assign a value to a cell depending on another cells value So, if the value in column 'E' is 1, then the value in column G should be the same …
excel - Remove leading or trailing spaces in an entire column of …
Mar 6, 2012 · I've found that the best (and easiest) way to delete leading, trailing (and excessive) spaces in Excel is to use a third-party plugin. I've been using ASAP Utilities for Excel and it …
What does the "@" symbol mean in Excel formula (outside a table)
Oct 24, 2021 · Excel has recently introduced a huge feature called Dynamic arrays. And along with that, Excel also started to make a " substantial upgrade " to their formula language. One …
excel - How to show current user name in a cell? - Stack Overflow
if you don't want to create a UDF in VBA or you can't, this could be an alternative. =Cell("Filename",A1) this will give you the full file name, and from this you could get the user …
How to represent a DateTime in Excel - Stack Overflow
The underlying data type of a datetime in Excel is a 64-bit floating point number where the length of a day equals 1 and 1st Jan 1900 00:00 equals 1. So 11th June 2009 17:30 is about …
excel - Check whether a cell contains a substring - Stack Overflow
Sep 4, 2013 · Is there an in-built function to check if a cell contains a given character/substring? It would mean you can apply textual functions like Left/Right/Mid on a conditional basis without …
How to keep one variable constant with other one changing with …
The $ tells excel not to adjust that address while pasting the formula into new cells. Since you are dragging across rows, you really only need to freeze the row part: =(B0+4)/A$0
Excel: Searching for multiple terms in a cell - Stack Overflow
Feb 11, 2013 · In addition to the answer of @teylyn, I would like to add that you can put the string of multiple search terms inside a SINGLE cell (as opposed to using a different cell for each …
How to freeze the =today() function once data has been entered
Aug 2, 2015 · Excel's default format handling doesn't know to format this as date - so you would need to do this separately. More work than Ctrl + ; , but there might be some other use-cases …
excel - Return values from the row above to the current row
Jun 15, 2012 · To solve this problem in Excel, usually I would just type in the literal row number of the cell above, e.g., if I'm typing in Cell A7, I would use the formula =A6. Then if I copied that …
Assign a value to a cell depending on content of another cell
Jan 16, 2020 · I am trying to use the IF function to assign a value to a cell depending on another cells value So, if the value in column 'E' is 1, then the value in column G should be the same …
excel - Remove leading or trailing spaces in an entire column of …
Mar 6, 2012 · I've found that the best (and easiest) way to delete leading, trailing (and excessive) spaces in Excel is to use a third-party plugin. I've been using ASAP Utilities for Excel and it …
