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Alternatives to Excel for Data Analysis: A Comprehensive Guide
Author: Dr. Anya Sharma, PhD in Data Science, Certified Data Analyst (CDA), 10+ years experience in data analysis and consulting.
Publisher: DataScienceCentral.com - A leading online community and resource for data science professionals.
Editor: Michael Davis, Data Science Editor at DataScienceCentral.com, with 15+ years experience in technical editing and publishing.
Keywords: alternatives to excel for data analysis, data analysis software, spreadsheet alternatives, data visualization tools, big data analysis, statistical software, R programming, Python programming, SQL, data analysis tools, excel replacement, cloud-based data analysis
Introduction:
Microsoft Excel has long been the go-to tool for data analysis, particularly for smaller datasets and simpler tasks. However, as datasets grow larger and the complexity of analyses increases, the limitations of Excel become increasingly apparent. This article explores compelling alternatives to Excel for data analysis, examining their strengths, weaknesses, and ideal use cases. We will delve into various options, from powerful statistical packages to versatile programming languages and cloud-based solutions. Understanding these alternatives to Excel for data analysis empowers users to choose the most effective tool for their specific needs.
H1: Limitations of Excel for Data Analysis
While Excel offers familiarity and ease of use for basic data manipulation, it falls short in several key areas:
Scalability: Excel struggles with large datasets. Performance degrades significantly as the number of rows and columns increases, leading to slow processing times and potential crashes. This is a major drawback for anyone dealing with big data. Considering alternatives to Excel for data analysis becomes crucial when datasets exceed a few hundred thousand rows.
Data Integrity: Manually entering and editing data in Excel increases the risk of errors. Lack of robust data validation features makes it difficult to ensure data accuracy and consistency. This is a critical issue when dealing with sensitive data. Better alternatives to Excel for data analysis prioritize data integrity.
Advanced Analytics: Excel's analytical capabilities are limited compared to dedicated statistical packages or programming languages. Performing complex statistical analyses, machine learning, or data mining in Excel is cumbersome and often impractical. Seeking alternatives to Excel for data analysis is essential for sophisticated analytical tasks.
Collaboration: Collaborating on Excel spreadsheets can be challenging. Simultaneous editing can lead to conflicts and version control issues. Many alternatives to Excel for data analysis offer robust collaboration features.
Reproducibility: Reproducing analyses in Excel can be difficult, especially when complex formulas and macros are involved. This makes it hard to ensure the accuracy and reliability of results. Exploring alternatives to Excel for data analysis that support reproducible research is key.
H2: Exploring the Best Alternatives to Excel for Data Analysis
Several powerful alternatives to Excel for data analysis exist, catering to various needs and skill levels. Here are some prominent options:
H3: Statistical Software Packages
R: A powerful open-source programming language specifically designed for statistical computing and graphics. R offers extensive libraries for data analysis, visualization, and modeling. It is highly flexible and customizable, making it a strong choice for advanced statistical analysis.
SPSS: A commercial statistical software package widely used in academic and business settings. SPSS provides a user-friendly interface and a comprehensive suite of statistical tools. It is particularly well-suited for survey data analysis and hypothesis testing.
SAS: A comprehensive commercial software suite for advanced analytics, including statistical modeling, business intelligence, and data management. SAS is known for its reliability and robustness, making it a preferred choice in regulated industries.
H3: Programming Languages for Data Analysis
Python: A versatile programming language with extensive libraries for data analysis (pandas, NumPy, Scikit-learn) and visualization (Matplotlib, Seaborn). Python offers a good balance of ease of use and power, making it a popular choice for data scientists. Its versatility extends beyond data analysis, allowing for automation and integration with other systems.
SQL: A powerful language for querying and managing relational databases. SQL is essential for extracting and manipulating data from large databases. While not a direct replacement for Excel, it is an indispensable tool for anyone working with relational data.
H3: Cloud-Based Data Analysis Platforms
Google Sheets: A cloud-based spreadsheet application that offers many features similar to Excel, but with enhanced collaboration and scalability features. While still having limitations for complex analyses, Google Sheets is a suitable choice for collaborative projects and moderately sized datasets.
Tableau: A powerful data visualization and business intelligence tool. While not strictly a replacement for Excel’s spreadsheet functionality, Tableau excels at creating interactive dashboards and visualizations from various data sources. It’s a great alternative for presenting data insights effectively.
Power BI: Microsoft's cloud-based business analytics service. Power BI offers robust data visualization, reporting, and dashboarding capabilities, integrating well with other Microsoft products.
H3: Specialized Data Analysis Tools
Jupyter Notebooks: Interactive computing environments that allow for combining code, visualizations, and narrative text. Jupyter Notebooks are particularly useful for exploratory data analysis and sharing findings.
H4: Choosing the Right Alternative to Excel for Data Analysis
The best alternatives to Excel for data analysis depends on several factors, including:
Dataset size and complexity: For small datasets and simple analyses, Excel might suffice. However, for larger datasets or complex analyses, statistical software, programming languages, or cloud-based platforms are preferable.
Analytical needs: If you need to perform advanced statistical analyses, machine learning, or data mining, then R, Python, SPSS, or SAS are more appropriate than Excel.
Budget: Open-source options like R and Python are free, while commercial software like SPSS and SAS can be expensive.
Technical skills: Some alternatives to Excel for data analysis, like R and Python, require programming skills, while others, like SPSS, have user-friendly interfaces.
Conclusion:
While Microsoft Excel serves as a useful tool for basic data analysis, its limitations become evident when dealing with larger datasets or more complex analytical tasks. Exploring the various alternatives to Excel for data analysis presented in this article—statistical software, programming languages, cloud-based platforms, and specialized tools—enables users to select the most appropriate and powerful tool to meet their specific needs and unlock the full potential of their data. The choice hinges on factors like dataset size, analytical goals, budget, and technical expertise. By carefully considering these factors, users can make an informed decision and significantly enhance the efficiency and effectiveness of their data analysis workflows.
FAQs:
1. What is the best free alternative to Excel for data analysis? R and Python, along with Google Sheets, are excellent free alternatives, each with strengths and weaknesses. The optimal choice depends on your skillset and analytical needs.
2. Which software is best for large datasets? R, Python, and SQL, coupled with cloud-based platforms or distributed computing frameworks, are better suited for large datasets than Excel.
3. How do I choose between R and Python for data analysis? Both are powerful; R excels in statistical computing, while Python offers broader versatility for data science tasks beyond statistics. Choose based on your project's focus.
4. Can I use SQL as a complete replacement for Excel? No, SQL focuses on database interaction; it is not a spreadsheet program. It's a complementary tool for extracting data to be analyzed elsewhere.
5. What's the easiest alternative to Excel for beginners? Google Sheets offers a familiar interface and relatively low learning curve, making it ideal for beginners.
6. Is Tableau better than Power BI? Both are excellent data visualization tools. The best choice depends on your existing tech stack (e.g., Microsoft ecosystem) and specific visualization needs.
7. How much does SAS software cost? SAS pricing is highly variable depending on the modules required and licensing options. Contact SAS directly for pricing details.
8. What are the limitations of cloud-based data analysis tools? Dependence on internet connectivity, potential security concerns, and vendor lock-in are potential drawbacks.
9. Where can I learn more about R and Python for data analysis? Numerous online courses, tutorials, and books are available on platforms like Coursera, edX, DataCamp, and Codecademy.
Related Articles:
1. "Mastering Data Analysis with R: A Beginner's Guide": A comprehensive tutorial covering fundamental R programming for data manipulation and analysis.
2. "Python for Data Science: A Hands-On Approach": A practical guide to using Python libraries (pandas, NumPy, Scikit-learn) for data science projects.
3. "A Comparison of Statistical Software Packages: SPSS vs. SAS vs. R": An in-depth comparison of these three popular statistical software packages based on their features, strengths, and weaknesses.
4. "Unlocking the Power of SQL for Data Analysis": An introduction to SQL commands and techniques for extracting and manipulating data from relational databases.
5. "Data Visualization with Tableau: Creating Effective Dashboards": A practical guide to creating insightful and interactive dashboards using Tableau.
6. "Building Data Pipelines with Python and Apache Airflow": Learn how to automate data processing and analysis workflows using Python and Airflow.
7. "Big Data Analysis with Hadoop and Spark": Explore the tools and techniques used for analyzing massive datasets using distributed computing frameworks.
8. "Advanced Data Wrangling Techniques in Python": A tutorial on handling and cleaning messy data using Python's data manipulation libraries.
9. "The Ethical Implications of Data Analysis: Ensuring Responsible Data Practices": A discussion about responsible data handling and potential biases in data analysis.
| alternatives to excel 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. |
| alternatives to excel 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. |
| alternatives to excel for data analysis: 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. |
| alternatives to excel for data analysis: 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 |
| alternatives to excel 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. |
| alternatives to excel 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. |
| alternatives to excel for data analysis: Financial Modeling in Excel For Dummies Danielle Stein Fairhurst, 2017-04-24 Make informed business decisions with the beginner's guide to financial modeling using Microsoft Excel Financial Modeling in Excel For Dummies is your comprehensive guide to learning how to create informative, enlightening financial models today. Not a math whiz or an Excel power-user? No problem! All you need is a basic understanding of Excel to start building simple models with practical hands-on exercises and before you know it, you'll be modeling your way to optimized profits for your business in no time. Excel is powerful, user-friendly, and is most likely already installed on your computer—which is why it has so readily become the most popular financial modeling software. This book shows you how to harness Excel's capabilities to determine profitability, develop budgetary projections, model depreciation, project costs, value assets and more. You'll learn the fundamental best practices and know-how of financial modeling, and how to put them to work for your business and your clients. You'll learn the tools and techniques that bring insight out of the numbers, and make better business decisions based on quantitative evidence. You'll discover that financial modeling is an invaluable resource for your business, and you'll wonder why you've waited this long to learn how! Companies around the world use financial modeling for decision making, to steer strategy, and to develop solutions. This book walks you through the process with clear, expert guidance that assumes little prior knowledge. Learn the six crucial rules to follow when building a successful financial model Discover how to review and edit an inherited financial model and align it with your business and financial strategy Solve client problems, identify market projections, and develop business strategies based on scenario analysis Create valuable customized templates models that can become a source of competitive advantage From multinational corporations to the mom-and-pop corner store, there isn't a business around that wouldn't benefit from financial modeling. No need to buy expensive specialized software—the tools you need are right there in Excel. Financial Modeling in Excel For Dummies gets you up to speed quickly so you can start reaping the benefits today! |
| alternatives to excel for data analysis: R for Excel Users John L. Taveras, 2016-08-08 R has a steep learning curve and, if taken in all at once, it can be overwhelming. But we can tame this curve by putting aside visualizations and analysis, and focusing on working with data. This book is all about data manipulation: importing, creating, modifying, filtering, summarizing and reshaping data sets. You will also go deep on the building blocks of R: vectors and functions. The language is simplified and technical lingo is kept to a minimum. You will see analogies to Excel where applicable, to ease your understanding of concepts. Supplemental articles and videos can be found at rforexcelusers.com |
| alternatives to excel for data analysis: Probabilistic Machine Learning Kevin P. Murphy, 2022-03-01 A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach. |
| alternatives to excel for data analysis: Advancing Into Analytics George Mount, 2021-01-22 Data analytics may seem daunting, but if you're familiar with Excel, you have a head start that can help you make the leap into analytics. Advancing into Analytics will lower your learning curve. Author George Mount, founder and CEO of Stringfest Analytics, clearly and gently guides intermediate Excel users to a solid understanding of analytics and the data stack. This book demonstrates key statistical concepts from spreadsheets and pivots your existing knowledge about data manipulation into R and Python programming. With this practical book at your side, you'll learn how to: Explore a dataset for potential research questions to check assumptions and to build hypotheses Make compelling business recommendations using inferential statistics Load, view, and write datasets using R and Python Perform common data wrangling tasks such as sorting, filtering, and aggregating using R and Python Navigate and execute code in Jupyter notebooks Identify, install, and implement the most useful open source packages for your needs And more. |
| alternatives to excel 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). |
| alternatives to excel for data analysis: Introduction to List of Microsoft 365 Applications Gilad James, PhD, Microsoft 365 is a subscription-based service that provides a suite of productivity applications and services to users. It includes widely used applications such as Word, Excel, PowerPoint, and Outlook, as well as other tools such as SharePoint, OneDrive, and Skype for Business. Microsoft 365 was designed to integrate and complement each application and service, offering users a comprehensive and seamless experience in their daily tasks. With the increasing demand for remote and flexible work arrangements, Microsoft 365 has become an essential resource for individuals and organizations alike. The applications and services within Microsoft 365 are accessible from any device and any location, making collaboration easy and efficient. Additionally, Microsoft 365's security features and regular updates guarantee that the applications and services remain safe and up-to-date, giving users peace of mind as they work. This article will provide an overview of the range of applications and services available within Microsoft 365, highlighting the benefits and features of each. |
| alternatives to excel for data analysis: Fundamentals of Data Analytics Prof. Dipanjan Kumar Dey, : Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions. The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Various approaches to data analytics include looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics). Data analytics relies on a variety of software tools ranging from spreadsheets, data visualization, and reporting tools, data mining programs, or open-source languages for the greatest data manipulation. |
| alternatives to excel for data analysis: 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 |
| alternatives to excel for data analysis: Better Data Visualizations Jonathan Schwabish, 2021-02-09 Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do’s and don’ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart’s design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message. |
| alternatives to excel for data analysis: DATA ANALYSIS BISHNU, PARTHA SARATHI, BHATTACHERJEE, VANDANA, Data Analysis Using Statistics and Probability with R Language is a complete introduction to data analysis. It provides a sound understanding of the foundations of the data analysis, in addition to covering many important advanced topics. Moreover, all the techniques have been implemented using R language as well as Excel. This book is intended for the undergraduate and postgraduate students of Management and Engineering disciplines. It is also useful for research scholars. KEY FEATURES 1. Covers data analysis topics such as: • Descriptive statistics like mean, median, mode, standard deviation, skewness, kurtosis, correlation and regression • Probability and probability distribution • Inferential statistics like estimation of parameters, hypothesis testing, ANOVA test, chi-square and t-test • Statistical quality control, time series analysis, statistical decision theory • Explorative data analysis like clustering and classification • Advanced techniques like conjoint analysis, panel data analysis, and logistic regression analysis 2. Comprises 12 chapters which include examples, solved problems, review questions and unsolved problems. 3. Requires no programming background and can be used to understand theoretical concepts also by skipping programming. 4. R and Excel implementations, and additional advanced topics are available at https://phindia.com/partha_sarathi_ bishnu_ and_vandana_bhattacherjee 5. Whenever in any branch, data analysis technique is required, this book is the best. TARGET AUDIENCE • Students of MBA, ME/M.Tech, and BE/B.Tech. • M.Sc. (Computer Science), MCA, BCA, and research scholars |
| alternatives to excel for data analysis: Hands-On Data Analysis with Pandas Stefanie Molin, 2019-07-26 Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial. |
| alternatives to excel for data analysis: Applied Choice Analysis David A. Hensher, John M. Rose, William H. Greene, 2005-06-02 Almost without exception, everything human beings undertake involves a choice. In recent years there has been a growing interest in the development and application of quantitative statistical methods to study choices made by individuals with the purpose of gaining a better understanding both of how choices are made and of forecasting future choice responses. In this primer the authors provide an unintimidating introduction to the main techniques of choice analysis and include detail on themes such as data collection and preparation, model estimation and interpretation and the design of choice experiments. A companion website to the book provides practice data sets and software to estimate the main discrete choice models such as multinomial logit, nested logit and mixed logit. This primer will be an invaluable resource to students as well as of immense value to consultants and professionals, researchers and anyone else interested in choice analysis and modelling. |
| alternatives to excel for data analysis: Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications, Third Edition Johan Gabrielsson, Daniel Weiner, 2001-11-30 This is a revised and very expanded version of the previous second edition of the book. Pharmacokinetic and Pharmacodynamic Data Analysis provides an introduction into pharmacokinetic and pharmacodynamic concepts using simple illustrations and reasoning. It describes ways in which pharmacodynamic and pharmacodynamic theory may be used to give insight into modeling questions and how these questions can in turn lead to new knowledge. This book differentiates itself from other texts in this area in that it bridges the gap between relevant theory and the actual application of the theory to real life situations. The book is divided into two parts; the first introduces fundamental principles of PK and PD concepts, and principles of mathematical modeling, while the second provides case studies obtained from drug industry and academia. Topics included in the first part include a discussion of the statistical principles of model fitting, including how to assess the adequacy of the fit of a model, as well as strategies for selection of time points to be included in the design of a study. The first part also introduces basic pharmacokinetic and pharmacodynamic concepts, including an excellent discussion of effect compartment (link) models as well as indirect response models. The second part of the text includes over 70 modeling case studies. These include a discussion of the selection of the model, derivation of initial parameter estimates and interpretation of the corresponding output. Finally, the authors discuss a number of pharmacodynamic modeling situations including receptor binding models, synergy, and tolerance models (feedback and precursor models). This book will be of interest to researchers, to graduate students and advanced undergraduate students in the PK/PD area who wish to learn how to analyze biological data and build models and to become familiar with new areas of application. In addition, the text will be of interest to toxicologists interested in learning about determinants of exposure and performing toxicokinetic modeling. The inclusion of the numerous exercises and models makes it an excellent primary or adjutant text for traditional PK courses taught in pharmacy and medical schools. A diskette is included with the text that includes all of the exercises and solutions using WinNonlin. |
| alternatives to excel for data analysis: Think Like a Data Scientist Brian Godsey, 2017-03-09 Summary Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Table of Contents PART 1 - PREPARING AND GATHERING DATA AND KNOWLEDGE Philosophies of data science Setting goals by asking good questions Data all around us: the virtual wilderness Data wrangling: from capture to domestication Data assessment: poking and prodding PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS Developing a plan Statistics and modeling: concepts and foundations Software: statistics in action Supplementary software: bigger, faster, more efficient Plan execution: putting it all together PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP Delivering a product After product delivery: problems and revisions Wrapping up: putting the project away |
| alternatives to excel for data analysis: Data Analysis for Managers with Microsoft Excel S. Christian Albright, Wayne L. Winston, Christopher James Zappe, 2004 This text presents statistical concepts and methods in a unified, modern, spreadsheet-oriented approach. Featuring a wealth of business applications, this examples-based text illustrates a variety of statistical methods to help students analyze data sets and uncover important information to aid decision-making. DATA ANALYSIS FOR MANAGERS contains professional StatPro add-ins for Microsoft Excel from Palisade, valued at one hundred fifty dollars packaged at no additional cost with every new text. |
| alternatives to excel for data analysis: Auditing Raymond N. Johnson, Laura Davis Wiley, Robyn Moroney, Fiona Campbell, Jane Hamilton, 2019-05-20 The explosion of data analytics in the auditing profession demands a different kind of auditor. Auditing: A Practical Approach with Data Analytics prepares students for the rapidly changing demands of the auditing profession by meeting the data-driven requirements of today's workforce. Because no two audits are alike, this course uses a practical, case-based approach to help students develop professional judgement, think critically about the auditing process, and develop the decision-making skills necessary to perform a real-world audit. To further prepare students for the profession, this course integrates seamless exam review for successful completion of the CPA Exam. |
| alternatives to excel for data analysis: SPSS Statistics For Dummies Jesus Salcedo, Keith McCormick, 2020-09-09 The fun and friendly guide to mastering IBM’s Statistical Package for the Social Sciences Written by an author team with a combined 55 years of experience using SPSS, this updated guide takes the guesswork out of the subject and helps you get the most out of using the leader in predictive analysis. Covering the latest release and updates to SPSS 27.0, and including more than 150 pages of basic statistical theory, it helps you understand the mechanics behind the calculations, perform predictive analysis, produce informative graphs, and more. You’ll even dabble in programming as you expand SPSS functionality to suit your specific needs. Master the fundamental mechanics of SPSS Learn how to get data into and out of the program Graph and analyze your data more accurately and efficiently Program SPSS with Command Syntax Get ready to start handling data like a pro—with step-by-step instruction and expert advice! |
| alternatives to excel for data analysis: Data Visualization Kieran Healy, 2018-12-18 An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions |
| alternatives to excel for data analysis: Big Data Analytics Beyond Hadoop Vijay Srinivas Agneeswaran, 2014-05-15 Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students. |
| alternatives to excel for data analysis: The Art of R Programming Norman Matloff, 2011-10-11 R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: –Create artful graphs to visualize complex data sets and functions –Write more efficient code using parallel R and vectorization –Interface R with C/C++ and Python for increased speed or functionality –Find new R packages for text analysis, image manipulation, and more –Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing. |
| alternatives to excel for data analysis: Statistical Analysis Conrad Carlberg, 2011-04-22 Statistical Analysis: Microsoft Excel 2010 “Excel has become the standard platform for quantitative analysis. Carlberg has become a world-class guide for Excel users wanting to do quantitative analysis. The combination makes Statistical Analysis: Microsoft Excel 2010 a must-have addition to the library of those who want to get the job done and done right.” —Gene V Glass, Regents’ Professor Emeritus, Arizona State University Use Excel 2010’s statistical tools to transform your data into knowledge Use Excel 2010’s powerful statistical tools to gain a deeper understanding of your data, make more accurate and reliable inferences, and solve problems in fields ranging from business to health sciences. Top Excel guru Conrad Carlberg shows how to use Excel 2010 to perform the 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 its new consistency functions. Along the way, you discover the most effective ways to use correlation and regression and analysis of variance and covariance. You see how to use Excel to test statistical hypotheses using the normal, binomial, t and F distributions. 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 an extensive set of 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 • View how values cluster and disperse • Infer a population’s characteristics from a sample’s frequency distribution • Explore correlation and regression to learn how variables move in tandem • Understand Excel’s new consistency functions • Test differences between two means using z tests, t tests, and Excel’s Data Analysis Add-in • Use ANOVA and ANCOVA to test differences between more than two means • Explore statistical power by manipulating mean differences, standard errors, directionality, and alpha There is an Excel workbook for each chapter, and each worksheet is keyed to one of the book's figures. You'll also find additional material, such as a chart that demonstrates how statistical power shifts as you manipulate sample size, mean differences, alpha and directionality. To access these free files, please visit http://www.quepublishing.com/title/0789747200 and click the Downloads Tab. |
| alternatives to excel for data analysis: Qualitative Data Analysis with NVivo Patricia Bazeley, 2007-04-12 `In plain language but with very thorough detail, this book guides the researcher who really wants to use the NVivo software (and use it now) into their project. The way is lit with real-project examples, adorned with tricks and tips, but it’s a clear path to a project' - Lyn Richards, Founder and Non-Executive Director, QSR International Doing Qualitative Data Analysis with NVivo is essential reading for anyone thinking of using their computer to help analyze qualitative data. With 15 years experience in computer-assisted analysis of qualitative and mixed-mode data, Patricia Bazeley is one of the leaders in the use and teaching of NVivo software. Through this very practical book, readers are guided on how best to make use of the powerful and flexible tools offered by the latest version of NVivo as they work through each stage of their research projects. Explanations draw on examples from her own and others' projects, and are supported by the methodological literature. Researchers have different requirements and come to their data from different perspectives. This book shows how NVivo software can accommodate and assist analysis across those different perspectives and methodological approaches. It is required reading for both students and experienced researchers alike. |
| alternatives to excel for data analysis: 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 |
| alternatives to excel for data analysis: 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 |
| alternatives to excel for data analysis: R Data Analysis without Programming David W. Gerbing, 2023-01-30 The new edition of this innovative book, R Data Analysis without Programming, prepares the readers to quickly analyze data and interpret statistical results using R. Professor Gerbing has developed lessR, a ground-breaking method in alleviating the challenges of R programming. The lessR extends R, removing the need for programming. This edition expands upon the first edition’s introduction to R through lessR, which enables the readers to learn how to organize data for analysis, read the data into R, and generate output without performing numerous functions and programming exercises first. With lessR, readers can select the necessary procedure and change the relevant variables with simple function calls. The text reviews and explains basic statistical procedures with the lessR enhancements added to the standard R environment. Using lessR, data analysis with R becomes immediately accessible to the novice user and easier to use for the experienced user. Highlights along with content new to this edition include: Explanation and Interpretation of all data analysis techniques; much more than a computer manual, this book shows the reader how to explain and interpret the results. Introduces the concepts and commands reviewed in each chapter. Clear, relaxed writing style more effectively communicates the underlying concepts than more stilted academic writing. Extensive margin notes highlight, define, illustrate, and cross-reference the key concepts. When readers encounter a term previously discussed, the margin notes identify the page number for the initial introduction. Scenarios that highlight the use of a specific analysis followed by the corresponding R/lessR input, output, and an interpretation of the results. Numerous examples of output from psychology, business, education, and other social sciences, that demonstrate the analysis and how to interpret results. Two data sets are analyzed multiple times in the book, provide continuity throughout. Comprehensive: A wide range of data analysis techniques are presented throughout the book. Integration with machine learning as regression analysis is presented from both the traditional perspective and from the modern machine learning perspective. End of chapter problems help readers test their understanding of the concepts. A website at www.lessRstats.com that features the data sets referenced in both standard text and SPSS formats so readers can practice using R/lessR by working through the text examples and worked problems, R/lessR videos to help readers better understand the program, and more. This book is ideal for graduate and undergraduate courses in statistics beyond the introductory course, research methods, and/or any data analysis course, taught in departments of psychology, business, education, and other social and health sciences; this book is also appreciated by researchers doing data analysis. Prerequisites include basic statistical knowledge, though the concepts are explained from the beginning in the book. Previous knowledge of R is not assumed. |
| alternatives to excel for data analysis: Statistical Tools For Managers (using Ms Excel) Apte, 2009 |
| alternatives to excel for data analysis: Thinking Spreadsheet Joel Grus, 2011 Whether you're a complete beginner or a grizzled veteran, Thinking Spreadsheet will make you an Excel expert. Its clear instruction and carefully-chosen examples will help you * Understand how spreadsheets work, what they do well, and what they don't do well. * Use the spreadsheet's structure to intelligently organize your data. * Solve problems using techniques that take advantage of the spreadsheet's strengths. * Build spreadsheets that are easy to understand and difficult to break. Along the way you'll learn core spreadsheet principles, basic tools like SUM() and IF(), advanced functions like MATCH() and VLOOKUP(), and power-user features like array formulas and pivot tables. You'll also learn a little bit of mathematics, a little bit of probability, a little bit of statistics, and a whole lot about how to intelligently solve problems. You might even laugh a few times! |
| alternatives to excel for data analysis: Learning Statistics with R Daniel Navarro, 2013-01-13 Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com |
| alternatives to excel for data analysis: The Economy of Nature: Data Analysis Update Robert E. Ricklefs, Matt R. Whiles, 2007 An introductory text that offers a survey of ecology, this work presents examples from natural history, coverage of evolution, and quantitative approach. It includes 20 data analysis modules that introduce students to ecological data and quantitative methods used by ecologists. |
| alternatives to excel for data analysis: Excel Data Analysis Hector Guerrero, 2010-03-10 Why does the World Need—Excel Data Analysis, Modeling, and Simulation? When spreadsheets ?rst became widely available in the early 1980s, it spawned a revolution in teaching. What previously could only be done with arcane software and large scale computing was now available to the common-man, on a desktop. Also, before spreadsheets, most substantial analytical work was done outside the classroom where the tools were; spreadsheets and personal computers moved the work into the classroom. Not only did it change how the analysis curriculum was taught, but it also empowered students to venture out on their own to explore new ways to use the tools. I can’t tell you how many phone calls, of?ce visits, and/or emails I have received in my teaching career from ecstatic students crowing about what they have just done with a spreadsheet model. I have been teaching courses related to spreadsheet based analysis and modeling for about 25 years and I have watched and participated in the spreadsheet revolution. |
| alternatives to excel for data analysis: Basics in Nursing Research and Biostatistics Sreevani Rentala, 2018-10-30 PART A--NURSING RESEARCH Unit 1. Introduction to Nursing Research Unit 2. Research Process Unit 3. Research Problem and Hypothesis Unit 4. Review of Literature Unit 5. Theory and Conceptual Framework in Nursing Research Unit 6. Research Approaches and Designs Unit 7. Sample and Sampling Techniques Unit 8. Tools and Methods of Data Collection Unit 9. Plan for Data Analysis and Interpretation Unit 10. Dissemination (Communication) and Utilization of Research Findings PART B--BIOSTATISTICS Unit 11. Introduction to Biostatistics Unit 12. Measures of Central Tendency Unit 13. Measures of Variability Unit 14. Normal Probability Distribution Unit 15. Measures of Relationship Unit 16. Inferential Statistics and Hypothesis Testing Unit 17. Application of Statistics in Health and Use of Computers for Data Analysis Glossary Appendices Index |
| alternatives to excel for data analysis: Power Query Cookbook Andrea Janicijevic, 2021-10-15 Leverage your source data from hundreds of different connections, perform millions of different transformations, and easily manage highly complex data lifecycles with Power Query Key FeaturesCollect, combine, and transform data using Power Query's data connectivity and data preparation featuresOvercome the problems faced while accessing data from multiple sources and reshape it to meet your business requirementsExplore how the M language can be used to write your own customized solutionsBook Description Power Query is a data preparation tool that enables data engineers and business users to connect, reshape, enrich, and transform their data to facilitate relevant business insights and analysis. With Power Query's wide range of features, you can perform no-code transformations and complex M code functions at the same time to get the most out of your data. This Power Query book will help you to connect to data sources, achieve intuitive transformations, and get to grips with preparation practices. Starting with a general overview of Power Query and what it can do, the book advances to cover more complex topics such as M code and performance optimization. You'll learn how to extend these capabilities by gradually stepping away from the Power Query GUI and into the M programming language. Additionally, the book also shows you how to use Power Query Online within Power BI Dataflows. By the end of the book, you'll be able to leverage your source data, understand your data better, and enrich it with a full stack of no-code and custom features that you'll learn to design by yourself for your business requirements. What you will learnUnderstand how to use Power Query to connect and explore dataExplore ways to reshape and enrich dataDiscover the potential of Power Query across the Microsoft platformBuild complex and custom transformationsUse M code to write new queries against data sourcesUse the Power Query Online tool within Power BI DataflowsImplement best practices such as reusing dataflows, optimizing expanding table operations, and field mappingWho this book is for This book is for data analysts, BI developers, data engineers, and anyone looking for a desk reference guide to learn how Power Query can be used with different Microsoft products to handle data of varying complexity. Beginner-level knowledge of Power BI and the M Language will help you to get the best out of this book. |
| alternatives to excel for data analysis: The Coding Manual for Qualitative Researchers Johnny Saldana, 2009-02-19 The Coding Manual for Qualitative Researchers is unique in providing, in one volume, an in-depth guide to each of the multiple approaches available for coding qualitative data. In total, 29 different approaches to coding are covered, ranging in complexity from beginner to advanced level and covering the full range of types of qualitative data from interview transcripts to field notes. For each approach profiled, Johnny Saldaña discusses the method’s origins in the professional literature, a description of the method, recommendations for practical applications, and a clearly illustrated example. |
| alternatives to excel for data analysis: The Cambridge Handbook of Group Interaction Analysis Elisabeth Brauner, Margarete Boos, Michaela Kolbe, 2018-08-02 This Handbook provides a compendium of research methods that are essential for studying interaction and communication across the behavioral sciences. Focusing on coding of verbal and nonverbal behavior and interaction, the Handbook is organized into five parts. Part I provides an introduction and historic overview of the field. Part II presents areas in which interaction analysis is used, such as relationship research, group research, and nonverbal research. Part III focuses on development, validation, and concrete application of interaction coding schemes. Part IV presents relevant data analysis methods and statistics. Part V contains systematic descriptions of established and novel coding schemes, which allows quick comparison across instruments. Researchers can apply this methodology to their own interaction data and learn how to evaluate and select coding schemes and conduct interaction analysis. This is an essential reference for all who study communication in teams and groups. |
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ALTERNATIVE | English meaning - Cambridge Dictionary
Alternative (ly) means ‘something that is different and unusual and offers a possible choice’: … There must be an alternative to …
Alternative Definition & Meaning | Britannica Dictionary
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ALTERNATIVE Definition & Meaning - Merriam-Webster
alternative implies a need to choose one and reject another possibility. preference suggests a choice guided by one's judgment or predilections. selection implies a range of choice. election …
ALTERNATIVE | English meaning - Cambridge Dictionary
Alternative (ly) means ‘something that is different and unusual and offers a possible choice’: … There must be an alternative to people sleeping on the streets. no alternative You've given me no …
Alternative Definition & Meaning | Britannica Dictionary
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a choice among only two possibilities such that if one is chosen, the other cannot be chosen: Here are the alternatives: surrender or die. one of these choices: The alternative to riding is walking. …
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Definition of alternative noun from the Oxford Advanced Learner's Dictionary. a thing that you can choose to do or have out of two or more possibilities. The car is too expensive so we're trying to …
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