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| excel case studies for data analysis: 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 case studies for data analysis: 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 case studies for data analysis: Text Analytics for Business Decisions Andres Fortino, 2021-05-13 With the rise in data science development, we now have many remarkable techniques and tools to extend data analysis from numeric and categorical data to textual data. Sifting through the open-ended responses from a survey, for example, was an arduous process when performed by hand. Using a case study approach, this book was written for business analysts who wish to increase their skills in extracting answers for text data in order to support business decision making. Most of the exercises use Excel, today’s most common analysis tool, and R, a popular analytic computer environment. The techniques covered range from the most basic text analytics, such as key word analysis, to more sophisticated techniques, such as topic extraction and text similarity scoring. Companion files with numerous datasets are included for use with case studies and exercises. FEATURES: Organized by tool or technique, with the basic techniques presented first and the more sophisticated techniques presented later Uses Excel and R for datasets in case studies and exercises Features the CRISP-DM data mining standard with early chapters for conducting the preparatory steps in data mining Companion files with numerous datasets and figures from the text. The companion files are available online by emailing the publisher with proof of purchase at info@merclearning.com. |
| excel case studies for data analysis: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-11-04 A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website. |
| excel case studies for data analysis: 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 case studies for data analysis: 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 case studies for data analysis: DATA ANALYSIS AND BUSINESS MODELLING USING MICROSOFT EXCEL Hansa Lysander Manohar, 2017-03-30 |
| excel case studies for data analysis: 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 case studies for data analysis: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| excel case studies for data analysis: Data Analysis with Excel® Les Kirkup, 2002-03-07 An essential introduction to data analysis techniques using spreadsheets, for undergraduate and graduate students. |
| excel case studies for data analysis: Using Excel for Business Analysis Danielle Stein Fairhurst, 2015-05-18 This is a guide to building financial models for business proposals, to evaluate opportunities, or to craft financial reports. It covers the principles and best practices of financial modelling, including the Excel tools, formulas, and functions to master, and the techniques and strategies necessary to eliminate errors. |
| excel case studies for data analysis: Excel Data Analysis For Dummies Stephen L. Nelson, E. C. Nelson, 2014-04-14 Harness the power of Excel to discover what your numbers are hiding Excel Data Analysis For Dummies, 2nd Edition is the ultimate guide to getting the most out of your data. Veteran Dummies author Stephen L. Nelson guides you through the basic and not-so-basic features of Excel to help you discover the gems hidden in your rough data. From input, to analysis, to visualization, the book walks you through the steps that lead to superior data analysis. Excel is the number-one spreadsheet application, with ever-expanding capabilities. If you're only using it to balance the books, you're missing out on a host of functions that can benefit your business or personal finances by uncovering trends and other important information hidden within the numbers. Excel Data Analysis For Dummies, 2nd Edition eliminates the need for advanced statistics or analysis courses by allowing you to harness the full power of Excel to do the heavy lifting for you. This 2nd Edition is fully updated to include information about Excel's latest features, making it a your go-to Excel guide for data analysis. Topics include: Working with external databases PivotTables and PivotCharts Using Excel for statistical and financial functions Solver, Small Business Finance Manager, and more The book also includes a guide to chart types and formatting, and advice on effective visual data presentation. You already have the data, so you might as well get something great out of it. Excel Data Analysis For Dummies, 2nd Edition is the key to discovering what your numbers are hiding. |
| excel case studies for data analysis: 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 case studies for data analysis: 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 case studies for data analysis: R and Data Mining Yanchang Zhao, 2012-12-31 R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work |
| excel case studies for data analysis: Introductory Business Statistics 2e Alexander Holmes, Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| excel case studies for data analysis: Guerilla Data Analysis Using Microsoft Excel Bill Jelen, 2002-09-30 This book includes step-by-step examples and case studies that teach users the many power tricks for analyzing data in Excel. These are tips honed by Bill Jelen, &“MrExcel,&” during his 10-year run as a financial analyst charged with taking mainframe data and turning it into useful information quickly. Topics include perfectly sorting with one click every time, matching lists of data, data consolidation, data subtotals, pivot tables, and much more. |
| excel case studies for data analysis: Beginning Excel, First Edition Barbara Lave, Diane Shingledecker, Julie Romey, Noreen Brown, Mary Schatz, 2020 This is the first edition of a textbook written for a community college introductory course in spreadsheets utilizing Microsoft Excel; second edition available: https://openoregon.pressbooks.pub/beginningexcel19/. While the figures shown utilize Excel 2016, the textbook was written to be applicable to other versions of Excel as well. The book introduces new users to the basics of spreadsheets and is appropriate for students in any major who have not used Excel before. |
| excel case studies for data analysis: Bayesian Analysis Made Simple Phil Woodward, 2011-08-26 Although the popularity of the Bayesian approach to statistics has been growing for years, many still think of it as somewhat esoteric, not focused on practical issues, or generally too difficult to understand. Bayesian Analysis Made Simple is aimed at those who wish to apply Bayesian methods but either are not experts or do not have the time to create WinBUGS code and ancillary files for every analysis they undertake. Accessible to even those who would not routinely use Excel, this book provides a custom-made Excel GUI, immediately useful to those users who want to be able to quickly apply Bayesian methods without being distracted by computing or mathematical issues. From simple NLMs to complex GLMMs and beyond, Bayesian Analysis Made Simple describes how to use Excel for a vast range of Bayesian models in an intuitive manner accessible to the statistically savvy user. Packed with relevant case studies, this book is for any data analyst wishing to apply Bayesian methods to analyze their data, from professional statisticians to statistically aware scientists. |
| excel case studies for data analysis: Data Analytics for Organisational Development Uwe H. Kaufmann, Amy B. C. Tan, 2021-07-26 A practical guide for anyone who aspires to become data analytics–savvy Data analytics has become central to the operation of most businesses, making it an increasingly necessary skill for every manager and for all functions across an organisation. Data Analytics for Organisational Development: Unleashing the Potential of Your Data introduces a methodical process for gathering, screening, transforming, and analysing the correct datasets to ensure that they are reliable tools for business decision-making. Written by a Six Sigma Master Black Belt and a Lean Six Sigma Black Belt, this accessible guide explains and illustrates the application of data analytics for organizational development and design, with particular focus on Customer and Strategy Analytics, Operations Analytics and Workforce Analytics. Designed as both a handbook and workbook, Data Analytics for Organisational Development presents the application of data analytics for organizational design and development using case studies and practical examples. It aims to help build a bridge between data scientists, who have less exposure to actual business issues, and the non-data scientists. With this guide, anyone can learn to perform data analytics tasks from translating a business question into a data science hypothesis to understanding the data science results and making the appropriate decisions. From data acquisition, cleaning, and transformation to analysis and decision making, this book covers it all. It also helps you avoid the pitfalls of unsound decision making, no matter where in the value chain you work. Follow the “Five Steps of a Data Analytics Case” to arrive at the correct business decision based on sound data analysis Become more proficient in effectively communicating and working with the data experts, even if you have no background in data science Learn from cases and practical examples that demonstrate a systematic method for gathering and processing data accurately Work through end-of-chapter exercises to review key concepts and apply methods using sample data sets Data Analytics for Organisational Development includes downloadable tools for learning enrichment, including spreadsheets, Power BI slides, datasets, R analysis steps and more. Regardless of your level in your organisation, this book will help you become savvy with data analytics, one of today’s top business tools. |
| excel case studies for data analysis: Case Study Research for Business Jillian Dawes Farquhar, 2012-02-21 The only case study research textbook written exclusively for students of Business and related disciplines. Using a step-by-step approach, Case Study Research for Business takes you right through the case study research process from research design and data collection using qualitative and quantitative methods, to research analysis, writing up and presenting your work. Key features: - Takes a multidisciplinary approach to case study research design by drawing on research philosophies to improve student understanding of these critical research traditions and hence provide firmer theoretical foundations for their research - Coverage of contemporary topics such as research ethics and access - Packed with practical examples from all areas of business - Pedagogical features include vignettes, exercises and ′cases′ which directly relate to business research Case Study Research for Business will prove a valuable resource for undergraduate, postgraduate and research students of business and related disciplines. |
| excel case studies for data analysis: Data Analytics for Organisational Development Uwe H. Kaufmann, Amy B. C. Tan, 2021-07-27 A practical guide for anyone who aspires to become data analytics–savvy Data analytics has become central to the operation of most businesses, making it an increasingly necessary skill for every manager and for all functions across an organisation. Data Analytics for Organisational Development: Unleashing the Potential of Your Data introduces a methodical process for gathering, screening, transforming, and analysing the correct datasets to ensure that they are reliable tools for business decision-making. Written by a Six Sigma Master Black Belt and a Lean Six Sigma Black Belt, this accessible guide explains and illustrates the application of data analytics for organizational development and design, with particular focus on Customer and Strategy Analytics, Operations Analytics and Workforce Analytics. Designed as both a handbook and workbook, Data Analytics for Organisational Development presents the application of data analytics for organizational design and development using case studies and practical examples. It aims to help build a bridge between data scientists, who have less exposure to actual business issues, and the non-data scientists. With this guide, anyone can learn to perform data analytics tasks from translating a business question into a data science hypothesis to understanding the data science results and making the appropriate decisions. From data acquisition, cleaning, and transformation to analysis and decision making, this book covers it all. It also helps you avoid the pitfalls of unsound decision making, no matter where in the value chain you work. Follow the “Five Steps of a Data Analytics Case” to arrive at the correct business decision based on sound data analysis Become more proficient in effectively communicating and working with the data experts, even if you have no background in data science Learn from cases and practical examples that demonstrate a systematic method for gathering and processing data accurately Work through end-of-chapter exercises to review key concepts and apply methods using sample data sets Data Analytics for Organisational Development includes downloadable tools for learning enrichment, including spreadsheets, Power BI slides, datasets, R analysis steps and more. Regardless of your level in your organisation, this book will help you become savvy with data analytics, one of today’s top business tools. |
| excel case studies for data analysis: Excel Data Analysis For Dummies Stephen L. Nelson, E. C. Nelson, 2015-11-30 Want to take the guesswork out of analyzing data? Let Excel do all the work for you! Data collection, management and analysis is the key to making effective business decisions, and if you are like most people, you probably don't take full advantage of Excel's data analysis tools. With Excel Data Analysis For Dummies, 3rd Edition, you'll learn how to leverage Microsoft Excel to take your data analysis to new heights by uncovering what is behind all of those mind-numbing numbers. The beauty of Excel lies in its functionality as a powerful data analysis tool. This easy-to-read guide will show you how to use Excel in conjunction with external databases, how to fully leverage PivotTables and PivotCharts, tips and tricks for using Excel's statistical and financial functions, how to visually present your data so it makes sense, and information about the fancier, more advanced tools for those who have mastered the basics! Once you're up to speed, you can stop worrying about how to make use of all that data you have on your hands and get down to the business of discovering meaningful, actionable insights for your business or organization. Excel is the most popular business intelligence tool in the world, and the newest update – Microsoft Excel 2016 – features even more powerful features for data analysis and visualization. Users can slice and dice their data and create visual presentations that turn otherwise indecipherable reports into easy-to-digest presentations that can quickly and effectively illustrate the key insights you are seeking. Fully updated to cover the latest updates and features of Excel 2016 Learn useful details about statistics, analysis, and visual presentations for your data Features coverage of database and statistics functions, descriptive statistics, inferential statistics, and optimization modeling with Solver Helps anyone who needs insight into how to get things done with data that is unwieldy and difficult to understand With Excel Data Analysis For Dummies, 3rd Edition, you'll soon be quickly and easily performing key analyses that can drive organizational decisions and create competitive advantages. |
| excel case studies for data analysis: Data Mining for Business Analytics Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel, 2019-10-14 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 |
| excel case studies for data analysis: 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 case studies for data analysis: Analysis of Neural Data Robert E. Kass, Uri T. Eden, Emery N. Brown, 2014-07-08 Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work. |
| excel case studies for data analysis: 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 case studies for data analysis: Data Analytics for Pandemics Gitanjali Rahul Shinde, Asmita Balasaheb Kalamkar, Parikshit N. Mahalle, Nilanjan Dey, 2020-08-30 Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes. |
| excel case studies for data analysis: 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 case studies for data analysis: 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 case studies for data analysis: The Power of People Nigel Guenole, Jonathan Ferrar, Sheri Feinzig, 2017-05-19 Learn from Today’s Most Successful Workforce Analytics Leaders Transforming the immense potential of workforce analytics into reality isn’t easy. Pioneering practitioners have learned crucial lessons that can help you succeed. The Power of People shares their journeys—and their indispensable insights. Drawing on incisive case studies and vignettes, three experts help you bring purpose and clarity to any workforce analytics project, with robust research design and analysis to get reliable insights. They reveal where to start, where to find stakeholder support, and how to earn “quick wins” to build upon. You’ll learn how to sustain success through best-practice data management, technology usage, partnering, and skill building. Finally, you’ll discover how to earn even more value by establishing an analytical mindset throughout HR, and building two key skills: storytelling and visualization. The Power of People will be invaluable to HR executives establishing or leading analytics functions; HR professionals planning analytics projects; and any business executive who wants more value from HR. |
| excel case studies for data analysis: Creating and Verifying Data Sets with Excel Robert E. McGrath, 2014-01-21 Accurate data entry and analysis can be deceptively labor-intensive and time-consuming. Creating and Verifying Data Sets with Excel is a focused, easy-to-read guide that gives readers the wherewithal to make use of a remarkable set of data tools tucked within Excel—tools most researchers are entirely unaware of. Robert E. McGrath’s book is the first to focus exclusively on Excel as a data entry system. It incorporates a number of learning tools such as screenshots, text boxes that summarize key points, examples from across the social sciences, tips for creating professional-looking tables, and questions at the end of each chapter. Providing practical strategies to improve and ease the processes of data entry, creation and analysis, this step-by-step guide is a brief, but invaluable resource for both students and researchers. |
| excel case studies for data analysis: Mastering Data Visualization with Tableau Dr. Arpana Chaturvedi, Prof. Praveen Malik, 2024-07-11 DESCRIPTION Mastering Data Visualization with Tableau” is an invaluable book that will help you become more knowledgeable and elevate your understanding and skills in data visualization using Tableau which is one of the leading tools in the industry. This comprehensive resource covers the basics of visual analytics, explaining both the theory and practical ways to turn raw data into useful insights. You will start with Tableau Desktop by learning to download it, navigate the interface, and connect to data sources. The book teaches you to create and format basic charts, adding interactivity with parameters, sets, sorting, and filtering. You will explore calculations and advanced visualizations like bar-in-bar charts and maps. It covers designing interactive dashboards, using text and images for storytelling, and sharing work via PDFs and Tableau Public. The book ends with AI features in Tableau and hands-on exercises to practice. Through this book, readers can gain the confidence to handle complex datasets, apply advanced visualization techniques, and harness Tableau's full potential to make informed decisions faster and with greater accuracy. This guide is your pathway to becoming proficient in the art and science of data visualization with Tableau. KEY FEATURES ● Detailed exploration of Tableau, Tableau interface, dimensions, measures, and other visualization tools. ● Techniques for interactive data visualization using actions, filters, sets, parameters, groups, and hierarchy. ● Advanced graphing techniques and dynamic visualization strategies, calculated fields, table calculations, and LOD. ● Comprehensive integration of AI to improve data analysis. WHAT YOU WILL LEARN ● Tableau for complex data visualizations and apply predictive analytics. Clean and prepare data efficiently and create interactive dashboards that drive strategic business decisions. ● Advanced charts like bar-in-bar, profit calendar, and map visualizations. ● Gain practical hands-on experience with a question bank based on various industry use cases, enhancing your ability to tackle real-world data challenges. WHO THIS BOOK IS FOR This book is an excellent resource for students from any discipline, data scientists, business analysts, and professionals eager to master Tableau for comprehensive insights, effective dashboards, and advanced data analysis. TABLE OF CONTENTS 1. Introduction to Data Visualization and Visual Analytics 2. Getting Started with Tableau Desktop 3. Connecting to Data Sources and Data Interpretation 4. Basic Data Visualization and Graphs in Tableau 5. Dynamic Interaction: Parameters, Set, Hierarchies, and Sorting 6. Dynamic Interaction Using Filter and Action on Worksheet 7. Advanced Data Visualization and Graphs in Tableau 8. Calculations in Tableau 9. Dashboard Design and Story Creation 10. Enhancing Dashboards: Sharing and Collaboration 11. Integrating AI in Tableau: An Overview 12. Data Cleaning and Preparation Using Tableau Prep Builder |
| excel case studies for data analysis: Improving the User Experience through Practical Data Analytics Mike Fritz, Paul D. Berger, 2015-03-03 Improving the User Experience through Practical Data Analytics shows you how to make UX design decisions based on data—not hunches. Authors Fritz and Berger help the UX professional recognize the enormous potential of user data that is collected as a natural by-product of routine UX research methods, including moderated usability tests, unmoderated usability tests, surveys, and contextual inquiries. Then, step-by-step, they explain how to utilize both descriptive and predictive statistical techniques to gain meaningful insight with that data. By mastering the use of these techniques, you'll delight your users, increase your bottom line and gain a powerful competitive advantage for your company—and yourself. Key features include: - Practical advise on choosing the right data analysis technique for each project. - A step-by-step methodology for applying each technique, including examples and scenarios drawn from the UX field. - Detailed screen shots and instructions for performing the techniques using Excel (both for PC and Mac) and SPSS. - Clear and concise guidance on interpreting the data output. - Exercises to practice the techniques - Practical guidance on choosing the right data analysis technique for each project. - Real-world examples to build a theoretical and practical understanding of key concepts from consumer and financial verticals. - A step-by-step methodology for applying each predictive technique, including detailed examples. - A detailed guide to interpreting the data output and examples of how to effectively present the findings in a report. - Exercises to learn the techniques |
| excel case studies for data analysis: Forensic Analytics Mark J. Nigrini, 2020-04-20 Become the forensic analytics expert in your organization using effective and efficient data analysis tests to find anomalies, biases, and potential fraud—the updated new edition Forensic Analytics reviews the methods and techniques that forensic accountants can use to detect intentional and unintentional errors, fraud, and biases. This updated second edition shows accountants and auditors how analyzing their corporate or public sector data can highlight transactions, balances, or subsets of transactions or balances in need of attention. These tests are made up of a set of initial high-level overview tests followed by a series of more focused tests. These focused tests use a variety of quantitative methods including Benford’s Law, outlier detection, the detection of duplicates, a comparison to benchmarks, time-series methods, risk-scoring, and sometimes simply statistical logic. The tests in the new edition include the newly developed vector variation score that quantifies the change in an array of data from one period to the next. The goals of the tests are to either produce a small sample of suspicious transactions, a small set of transaction groups, or a risk score related to individual transactions or a group of items. The new edition includes over two hundred figures. Each chapter, where applicable, includes one or more cases showing how the tests under discussion could have detected the fraud or anomalies. The new edition also includes two chapters each describing multi-million-dollar fraud schemes and the insights that can be learned from those examples. These interesting real-world examples help to make the text accessible and understandable for accounting professionals and accounting students without rigorous backgrounds in mathematics and statistics. Emphasizing practical applications, the new edition shows how to use either Excel or Access to run these analytics tests. The book also has some coverage on using Minitab, IDEA, R, and Tableau to run forensic-focused tests. The use of SAS and Power BI rounds out the software coverage. The software screenshots use the latest versions of the software available at the time of writing. This authoritative book: Describes the use of statistically-based techniques including Benford’s Law, descriptive statistics, and the vector variation score to detect errors and anomalies Shows how to run most of the tests in Access and Excel, and other data analysis software packages for a small sample of the tests Applies the tests under review in each chapter to the same purchasing card data from a government entity Includes interesting cases studies throughout that are linked to the tests being reviewed. Includes two comprehensive case studies where data analytics could have detected the frauds before they reached multi-million-dollar levels Includes a continually-updated companion website with the data sets used in the chapters, the queries used in the chapters, extra coverage of some topics or cases, end of chapter questions, and end of chapter cases. Written by a prominent educator and researcher in forensic accounting and auditing, the new edition of Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations is an essential resource for forensic accountants, auditors, comptrollers, fraud investigators, and graduate students. |
| excel case studies for data analysis: HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) Harvard Business Review, 2018-03-13 Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes |
| excel case studies for data analysis: Analytics Stories Wayne L. Winston, 2020-09-02 Inform your own analyses by seeing how one of the best data analysts in the world approaches analytics problems Analytics Stories: How to Make Good Things Happen is a thoughtful, incisive, and entertaining exploration of the application of analytics to real-world problems and situations. Covering fields as diverse as sports, finance, politics, healthcare, and business, Analytics Stories bridges the gap between the oft inscrutable world of data analytics and the concrete problems it solves. Distinguished professor and author Wayne L. Winston answers questions like: Was Liverpool over Barcelona the greatest upset in sports history? Was Derek Jeter a great infielder What's wrong with the NFL QB rating? How did Madoff keep his fund going? Does a mutual fund’s past performance predict future performance? What caused the Crash of 2008? Can we predict where crimes are likely to occur? Is the lot of the American worker improving? How can analytics save the US Republic? The birth of evidence-based medicine: How did James Lind know citrus fruits cured scurvy? How can I objectively compare hospitals? How can we predict heart attacks in real time? How does a retail store know if you're pregnant? How can I use A/B testing to improve sales from my website? How can analytics help me write a hit song? Perfect for anyone with the word “analyst” in their job title, Analytics Stories illuminates the process of applying analytic principles to practical problems and highlights the potential pitfalls that await careless analysts. |
| excel case studies for data analysis: Media Analytics C. Ann Hollifield, Amy Jo Coffey, 2023-06-27 This textbook takes a case study approach to media and audience analytics. Realizing the best way to understand analytics in the digital age is to practice it, the authors have created a collection of cases using datasets that present real and hypothetical scenarios for students to work through. Media Analytics introduces the key principles of media economics and management. It outlines how to interpret and present results, the principles of data visualization and storytelling, and the basics of research design and sampling. Although shifting technology makes measurement and analytics a dynamic space, this book takes an evergreen, conceptual approach, reminding students to focus on the principles and foundations that will remain constant. Aimed at upper-level students in the fast-growing area of media analytics in a cross-platform world, students using this text will learn how to find the stories in the data and how to present those stories in an engaging way to others. Instructor and Student Resources include an Instructor’s Manual, discussion questions, short exercises, and links to additional resources. They are available online at www.routledge.com/cw/hollifield. |
| excel case studies for data analysis: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| excel case studies for data analysis: 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 Case Studies For Data Analysis - mathiasdahlgren.com
5 Excel Case Studies For Data Analysis Published at mathiasdahlgren.com and scalability, Excel's accessibility, ease of use, and built-in functionalities make it an invaluable asset, …
Excel 2019 Data Analysis - tcworkshop.com
Excel 2019: Data Analysis, Rel. 1.0, 5/6/2020 Page 3 Lesson 1: Tables & Data Management Understanding Structured Data While data in Excel can be laid out in many different ways …
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MIGRATING AND CLEANING DATA USING EXCEL: A CASE STUDY. John Wilton, Master’s Candidate and Anne Matheus, PhD Marist College. Abstract: This is a case study from a …
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Excel is generally considered a number cruncher. However, its structure and data manipulation and display features can be utilized for qualitative analysis. In this article, the authors discuss …
Social Analysis Part I with Microsoft Excel - UK Data Service
Excel for social research. It provides a series of exercises to develop your skills in using Excel for social analysis. Part I introduces ways of manipulating, presenting and reporting on a dataset …
Analytics mindset case study solutions - EY
The students are able to practice developing their ETL skills with rich, real-world data. As a reminder, an analytics mindset is the ability to: Below, each case and the associated files are …
Data Analysis Using Spreadsheets - Stanford University
A surprisingly large fraction of the world’s structured data is managed and manipulated in spreadsheets. Spreadsheets are used by 750 million people — 10% of the world’s population.
Chapter 6: Data Analysis with Excel and SPSS
how to import data from a Web experiment into Excel, how to filter the data, and how to perform simple analyses. You will learn how to import data to SPSS, and how to carry out cross-tabs in …
STUDY MATERIAL DATA ANALYTICS FOR FINANCE PROFESSIONALS
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guide is to assist in the completion of the L4 S&C excel case study. It includes a breakdown of what is expected to be completed, with examples given for each task. The case study to be …
Using Microsoft Excel to code and thematically analyse qualitative data …
(2014) implemented a simple, cost-effective technology-based analysis of captured focus group data with a widely used software suite. This report describes in detail the qualitative data …
Collection and management of research data in Excel - QIMR …
Slide 1. Collection and management of research data in Excel. Overview. Where to store data. Database considerations. What is data cleaning and why do we do it? Practical Microsoft …
Data Analysis Case Studies - Data Action Lab
In this report, we provide examples of data analysis and quantitative methods applied to “real-life” problems. We emphasize qualitative aspects of the projects as well as significant results and …
Excel Data Analysis - Texas Tech University Departments
• Pivot Tables are an easy way to build reports on your data in Excel • Allow you to group your data by category (date, location, temperature, college, major, residence hall, etc) • Allows you …
Data Analytics and Continuous Control Monitoring Book-22-8-12
Data analysis can help auditors meet their auditing objectives and would thereby helps to comply with auditing standards, support enterprise risk management system, uncover fraud and …
A Case Study on Financial Modeling & Valuation - Imarticus
A Case Study on Financial Modeling & Valuation. CRISIL GR&A provides customized financial modeling and valuation support specific to client requirements. The team has an experience of …
THE APPLICATION OF EXCEL IN FINANCIAL STATEMENT ANALYSIS ...
Therefore, excel can play an important role in the analysis of corporate financial statements. In-depth study of the application of excel in corporate financial statements can effectively help …
Excel Case Studies For Data Analysis - mdghs.com
5 Excel Case Studies For Data Analysis Published at www.mdghs.com and scalability, Excel's accessibility, ease of use, and built-in functionalities make it an invaluable asset, especially for …
Analyzing Case Study Evidence - SAGE Publications Inc
Analyzing Case Study Evidence. How to Start Your Analysis, Your Analytic Choices, and How They Work. AN ANALYTIC STRATEGY: MORE THAN FAMILIARITY WITH ANALYTIC …
How to Analyze a Case Study - Simmons University
A business case simulates a real situation and has three characteristics: 1. a significant issue, 2. enough information to reach a reasonable conclusion, 3. no stated conclusion. A case may …
Excel Case Studies For Data Analysis - mathiasdahlgren.com
5 Excel Case Studies For Data Analysis Published at mathiasdahlgren.com and scalability, Excel's accessibility, ease of use, and built-in functionalities make it an invaluable asset, …
Excel 2019 Data Analysis - tcworkshop.com
Excel 2019: Data Analysis, Rel. 1.0, 5/6/2020 Page 3 Lesson 1: Tables & Data Management Understanding Structured Data While data in Excel can be laid out in many different ways …
Migrating and Cleaning Data Using Excel - Massachusetts …
MIGRATING AND CLEANING DATA USING EXCEL: A CASE STUDY. John Wilton, Master’s Candidate and Anne Matheus, PhD Marist College. Abstract: This is a case study from a …
Excel as a Qualitative Data Analysis Tool - SAGE Publications Inc
Excel is generally considered a number cruncher. However, its structure and data manipulation and display features can be utilized for qualitative analysis. In this article, the authors discuss …
Social Analysis Part I with Microsoft Excel - UK Data Service
Excel for social research. It provides a series of exercises to develop your skills in using Excel for social analysis. Part I introduces ways of manipulating, presenting and reporting on a dataset …
Analytics mindset case study solutions - EY
The students are able to practice developing their ETL skills with rich, real-world data. As a reminder, an analytics mindset is the ability to: Below, each case and the associated files are …
Data Analysis Using Spreadsheets - Stanford University
A surprisingly large fraction of the world’s structured data is managed and manipulated in spreadsheets. Spreadsheets are used by 750 million people — 10% of the world’s population.
Chapter 6: Data Analysis with Excel and SPSS
how to import data from a Web experiment into Excel, how to filter the data, and how to perform simple analyses. You will learn how to import data to SPSS, and how to carry out cross-tabs in …
STUDY MATERIAL DATA ANALYTICS FOR FINANCE PROFESSIONALS
FINANCE PROFESSIONALS. MODULE MODULE III III DATA ANALYTICS WITH EXCEL & ADVANCED EXCEL: CASE STUDIES. Board of Advanced Studies. THE INSTITUTE OF …
WELCOME TO THE CASE STUDY GUIDE - STRENGTH
guide is to assist in the completion of the L4 S&C excel case study. It includes a breakdown of what is expected to be completed, with examples given for each task. The case study to be …
Using Microsoft Excel to code and thematically analyse qualitative data …
(2014) implemented a simple, cost-effective technology-based analysis of captured focus group data with a widely used software suite. This report describes in detail the qualitative data …
Collection and management of research data in Excel - QIMR …
Slide 1. Collection and management of research data in Excel. Overview. Where to store data. Database considerations. What is data cleaning and why do we do it? Practical Microsoft …
Data Analysis Case Studies - Data Action Lab
In this report, we provide examples of data analysis and quantitative methods applied to “real-life” problems. We emphasize qualitative aspects of the projects as well as significant results and …
Excel Data Analysis - Texas Tech University Departments
• Pivot Tables are an easy way to build reports on your data in Excel • Allow you to group your data by category (date, location, temperature, college, major, residence hall, etc) • Allows you …
Data Analytics and Continuous Control Monitoring Book-22-8-12
Data analysis can help auditors meet their auditing objectives and would thereby helps to comply with auditing standards, support enterprise risk management system, uncover fraud and …
A Case Study on Financial Modeling & Valuation - Imarticus
A Case Study on Financial Modeling & Valuation. CRISIL GR&A provides customized financial modeling and valuation support specific to client requirements. The team has an experience of …
THE APPLICATION OF EXCEL IN FINANCIAL STATEMENT ANALYSIS ...
Therefore, excel can play an important role in the analysis of corporate financial statements. In-depth study of the application of excel in corporate financial statements can effectively help …
Excel Case Studies For Data Analysis - mdghs.com
5 Excel Case Studies For Data Analysis Published at www.mdghs.com and scalability, Excel's accessibility, ease of use, and built-in functionalities make it an invaluable asset, especially for …
Analyzing Case Study Evidence - SAGE Publications Inc
Analyzing Case Study Evidence. How to Start Your Analysis, Your Analytic Choices, and How They Work. AN ANALYTIC STRATEGY: MORE THAN FAMILIARITY WITH ANALYTIC …
How to Analyze a Case Study - Simmons University
A business case simulates a real situation and has three characteristics: 1. a significant issue, 2. enough information to reach a reasonable conclusion, 3. no stated conclusion. A case may …
