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| chatgpt 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. |
| chatgpt for data analysis: Avoiding Data Pitfalls Ben Jones, 2019-11-19 Avoid data blunders and create truly useful visualizations Avoiding Data Pitfalls is a reputation-saving handbook for those who work with data, designed to help you avoid the all-too-common blunders that occur in data analysis, visualization, and presentation. Plenty of data tools exist, along with plenty of books that tell you how to use them—but unless you truly understand how to work with data, each of these tools can ultimately mislead and cause costly mistakes. This book walks you step by step through the full data visualization process, from calculation and analysis through accurate, useful presentation. Common blunders are explored in depth to show you how they arise, how they have become so common, and how you can avoid them from the outset. Then and only then can you take advantage of the wealth of tools that are out there—in the hands of someone who knows what they're doing, the right tools can cut down on the time, labor, and myriad decisions that go into each and every data presentation. Workers in almost every industry are now commonly expected to effectively analyze and present data, even with little or no formal training. There are many pitfalls—some might say chasms—in the process, and no one wants to be the source of a data error that costs money or even lives. This book provides a full walk-through of the process to help you ensure a truly useful result. Delve into the data-reality gap that grows with our dependence on data Learn how the right tools can streamline the visualization process Avoid common mistakes in data analysis, visualization, and presentation Create and present clear, accurate, effective data visualizations To err is human, but in today's data-driven world, the stakes can be high and the mistakes costly. Don't rely on catching mistakes, avoid them from the outset with the expert instruction in Avoiding Data Pitfalls. |
| chatgpt for data analysis: Automated Data Analytics Soraya Sedkaoui, 2024-10-11 The human mind is endowed with a remarkable capacity for creative synthesis between intuition and reason; this mental alchemy is the source of genius. A new synergy is emerging between human ingenuity and the computational capacity of generative AI models. Automated Data Analytics focuses on this fruitful collaboration between the two to unlock the full potential of data analysis. Together, human ethics and algorithmic productivity have created an alloy stronger than the sum of its parts. The future belongs to this symbiosis between heart and mind, human and machine. If we succeed in harmoniously combining our strengths, it will only be a matter of time before we discover new analytical horizons. This book sets out the foundations of this promising partnership, in which everyone makes their contribution to a common work of considerable scope. History is being forged before our very eyes. It is our responsibility to write it wisely, and to collectively pursue the ideal of augmented intelligence progress. |
| chatgpt for data analysis: Automated Data Analytics Soraya Sedkaoui, 2024-11-13 The human mind is endowed with a remarkable capacity for creative synthesis between intuition and reason; this mental alchemy is the source of genius. A new synergy is emerging between human ingenuity and the computational capacity of generative AI models. Automated Data Analytics focuses on this fruitful collaboration between the two to unlock the full potential of data analysis. Together, human ethics and algorithmic productivity have created an alloy stronger than the sum of its parts. The future belongs to this symbiosis between heart and mind, human and machine. If we succeed in harmoniously combining our strengths, it will only be a matter of time before we discover new analytical horizons. This book sets out the foundations of this promising partnership, in which everyone makes their contribution to a common work of considerable scope. History is being forged before our very eyes. It is our responsibility to write it wisely, and to collectively pursue the ideal of augmented intelligence progress. |
| chatgpt for data analysis: Pandas Cookbook Theodore Petrou, 2017-10-23 Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results. Style and approach The author relies on his vast experience teaching pandas in a professional setting to deliver very detailed explanations for each line of code in all of the recipes. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data. |
| chatgpt for data analysis: From Zero to Data Hero with ChatGPT Andrew Wu, 2023-12-24 This is a comprehensive guide for prospective data scientists. It combines practical skills and advanced techniques with ChatGPT's groundbreaking capabilities. This easy-to-follow book shortens the learning curve for data analysis and machine learning beginners. It includes five chapters: 1. Kickstart Your Data Science Journey with ChatGPT's Power Tools: Introduces ChatGPT and the Noteable Plugin for quick data analysis. 2. The Great Data Hunt: Data collection and manipulation, including APIs, web scraping, data formats. 3. Making Data Meaningful: The basics of data analysis, simplified statistics and practical exercises. 4. Seeing the Unseen: Data Visualization: Techniques for revealing patterns in data using visual tools. 5. Venturing into the Machine's Mind: Machine Learning: Demystifies machine learning from regression analysis to recommendation engines, utilizing ChatGPT. Perfect for students, professionals and enthusiasts alike, this book offers a groundbreaking approach that makes data science accessible and manageable with the help of ChatGPT. |
| chatgpt for data analysis: Data Analysis with IBM SPSS Statistics Kenneth Stehlik-Barry, Anthony J. Babinec, 2017-09-22 Master data management & analysis techniques with IBM SPSS Statistics 24 About This Book Leverage the power of IBM SPSS Statistics to perform efficient statistical analysis of your data Choose the right statistical technique to analyze different types of data and build efficient models from your data with ease Overcome any hurdle that you might come across while learning the different SPSS Statistics concepts with clear instructions, tips and tricks Who This Book Is For This book is designed for analysts and researchers who need to work with data to discover meaningful patterns but do not have the time (or inclination) to become programmers. We assume a foundational understanding of statistics such as one would learn in a basic course or two on statistical techniques and methods. What You Will Learn Install and set up SPSS to create a working environment for analytics Techniques for exploring data visually and statistically, assessing data quality and addressing issues related to missing data How to import different kinds of data and work with it Organize data for analytical purposes (create new data elements, sampling, weighting, subsetting, and restructure your data) Discover basic relationships among data elements (bivariate data patterns, differences in means, correlations) Explore multivariate relationships Leverage the offerings to draw accurate insights from your research, and benefit your decision-making In Detail SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data. The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed. By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease. Style and approach Provides a practical orientation to understanding a set of data and examining the key relationships among the data elements. Shows useful visualizations to enhance understanding and interpretation. Outlines a roadmap that focuses the process so decision regarding how to proceed can be made easily. |
| chatgpt for data analysis: Healthcare Data Analytics Chandan K. Reddy, Charu C. Aggarwal, 2015-06-23 At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available |
| chatgpt for data analysis: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-28 A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook |
| chatgpt for data analysis: Ultimate Data Science Programming in Python Saurabh Chandrakar, 2024-09-25 DESCRIPTION In today's data-driven world, the ability to extract meaningful insights from vast datasets is crucial for success in various fields. This ultimate book for mastering open-source libraries of data science in Python equips you with the essential tools and techniques to navigate the ever-evolving field of data analysis and visualization. Discover how to use Python libraries like NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. This book also covers scientific computing with SciPy and integrates ChatGPT to boost your data science workflow. Designed for data scientists, analysts, and beginners, it offers a practical, hands-on approach to mastering data science fundamentals. With real-world applications and exercises, you will turn raw data into actionable insights, gaining a competitive edge. This book covers everything you need, including open-source libraries, Visual Explorer tools, and ChatGPT, making it a one-stop resource for Python-based data science. Readers will gain confidence after going through this book and we assure you that all the minute details have been taken into consideration while delivering the content. After reading, learning, and practicing from this book, we are sure that all IT professionals, novices, or job seekers will be able to work on data science projects thus proving their mettle. KEY FEATURES ● Master key Python libraries like NumPy, Pandas, and Seaborn for effective data analysis and visualization. ● Understand complex data science concepts through simple explanations and practical examples. ● Get hands-on experience with 300+ solved examples to solidify your Python data science skills. WHAT YOU WILL LEARN ● Learn to work with popular IDEs like VS Code and Jupyter Notebook for efficient Python development. ● Master open-source libraries such as NumPy, SciPy, Matplotlib, and Pandas through advanced, real-world examples. ● Utilize automated EDA tools like PyGWalker and AutoViz to simplify complex data analysis. ● Create sophisticated visualizations like heatmaps, FacetGrid, and box plots using Matplotlib and Seaborn. ● Efficiently handle missing data, outliers, and perform filtering, sorting, grouping, and aggregation using Pandas and Polars. WHO THIS BOOK IS FOR This book is ideal for diploma, undergraduate, and postgraduate students from engineering and science fields to programming and software professionals. It is also perfect for data science, ML, and AI engineers looking to expand their expertise in cutting-edge technologies. TABLE OF CONTENTS 1. Environmental Setup for Using Data Science Libraries in Python 2. Exploring Numpy Library for Data Science in Python 3. Exploring Array Manipulations in Numpy 4. Exploring Scipy Library for Data Science in Python 5. Line Plot exploration with Matplotlib Library 6. Charting Data With Various Visuals Using Matplotlib 7. Exploring Pandas Series for Data Science in Python 8. Exploring Pandas Dataframe for Data Science in Python 9. Advanced Dataframe Filtering Techniques 10. Exploring Polars Library for Data Science in Python 11. Exploring Expressions in Polars 12. Exploring Seaborn Library for Data Science in Python 13. Crafting Seaborn Plots: KDE, Line, Violin and Facets 14. Integrating Data Science Libraries with ChatGPT Prompts 15. Exploring Automated EDA Libraries for Machine Learning 16. Case Study Using Python Data Science Libraries |
| chatgpt for data analysis: Practical Data Analysis Hector Cuesta, Dr. Sampath Kumar, 2016-09-30 A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data. |
| chatgpt for data analysis: Dear Data Giorgia Lupi, Stefanie Posavec, 2016-09-13 Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates the infinitesimal, incomplete, imperfect, yet exquisitely human details of life, in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere. |
| chatgpt for data analysis: Data Analytics and Machine Learning Pushpa Singh, |
| chatgpt for data analysis: Real-Time Data Decisions With AI and ChatGPT Techniques Sharma, Priyanka, Jyotiyana, Monika, Kumar, A.V. Senthil, 2024-09-19 Modern businesses face the challenge of how to most effectively harness the power of Artificial Intelligence (AI) to enhance customer engagement and streamline operations. The proliferation of AI tools like ChatGPT offers immense potential. Yet, businesses often need help to navigate the complexities of implementation and maximize the benefits. This gap between AI's promise and its practical application highlights the need for a comprehensive resource that offers practical insights and innovative strategies. Real-Time Data Decisions With AI and ChatGPT Techniques is a groundbreaking book that addresses this critical challenge. By providing a detailed analysis of ChatGPT and other AI tools, this book equips businesses with the knowledge and strategies needed to leverage AI effectively. From algorithmic enhancements to real-world applications, each chapter offers valuable insights and actionable recommendations, making this book an indispensable guide for businesses seeking to capitalize on AI's transformative potential. |
| chatgpt for data analysis: Data Science Bookcamp Leonard Apeltsin, 2021-12-07 Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution |
| chatgpt for data analysis: The Data Analysis Workshop Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev, 2020-07-29 Learn how to analyze data using Python models with the help of real-world use cases and guidance from industry experts Key FeaturesGet to grips with data analysis by studying use cases from different fieldsDevelop your critical thinking skills by following tried-and-true data analysisLearn how to use conclusions from data analyses to make better business decisionsBook Description Businesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you'll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst. What you will learnGet to grips with the fundamental concepts and conventions of data analysisUnderstand how different algorithms help you to analyze the data effectivelyDetermine the variation between groups of data using hypothesis testingVisualize your data correctly using appropriate plotting pointsUse correlation techniques to uncover the relationship between variablesFind hidden patterns in data using advanced techniques and strategiesWho this book is for The Data Analysis Workshop is for programmers who already know how to code in Python and want to use it to perform data analysis. If you are looking to gain practical experience in data science with Python, this book is for you. |
| chatgpt for data analysis: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| chatgpt for data analysis: Machine Learning Algorithms Using Scikit and TensorFlow Environments Baby Maruthi, Puvvadi, Prasad, Smrity, Tyagi, Amit Kumar, 2023-12-18 Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students. |
| chatgpt for data analysis: Design Computing and Cognition’24 John S. Gero, |
| chatgpt for data analysis: 300+ WAYS TO MAKE MONEY WITH CHATGPT D. L. Bailey, 2024-08-01 Discover the ultimate guide to leveraging Chat GPT for financial success in 300+ Ways to Make Money With Chat GPT. This comprehensive ebook provides over 300 creative and practical methods to harness the power of Chat GPT to generate income. Whether you're a seasoned entrepreneur or just starting, you'll find actionable strategies, tips, and real-world examples to turn your Chat GPT interactions into lucrative ventures. Unlock the potential of AI and start your journey towards financial independence today! |
| chatgpt for data analysis: Basics of Microsoft Excel Manish Soni, 2024-11-10 As we embark on this journey together, this book aims to demystify Excel's features and functionalities, providing step-by-step instructions, practical tips, and real-world examples to ensure that you not only understand the concepts but also learn how to apply them in your day-to-day tasks. |
| chatgpt for data analysis: CSS3 and SVG with GPT-4 Oswald CAMPESATO, 2024-07-14 This book is designed to equip you with the knowledge and skills necessary to navigate the intersection of web development and artificial intelligence (AI). It covers various aspects of modern web development and AI technologies, with a particular emphasis on Generative AI, CSS3, SVG, JavaScript, HTML, and popular web features like 3D animations and gradients. By exploring these topics, readers will gain a deeper understanding of how AI can enhance web development processes and how to leverage AI models like GPT-4 to streamline development workflows. Web developers, UI/UX designers, and software engineers seeking to blend traditional web development skills with the latest AI technologies will find this book to be a valuable resource. |
| chatgpt for data analysis: GPT-4 For Developers Oswald Campesato, 2024-01-30 This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code samples that were generated by ChatGPT and GPT-4. Chapter 1 provides an overview of ChatGPT and GPT-4, followed by a chapter which contains Python 3.x code samples for solving various programming tasks in Python. Chapter 3 contains code samples for data visualization, and Chapter 4 contains code samples for linear regression. The final chapter covers visualization with Gen AI (Generative AI) and DALL-E. Companion files with source code and figures are available for downloading. FEATURES Offers an all-encompassing view of ChatGPT and GPT-4, from basics to advanced topics, including functionalities, capabilities, and limitations Contains Python 3.x code samples demonstrating the application of GPT-4 in real-world scenarios Provides a forward-looking perspective on Generative AI and its integration with data visualization and DALL-E Includes companion files with source code, data sets, and figures |
| chatgpt for data analysis: Proceedings of Ninth International Congress on Information and Communication Technology Xin-She Yang, |
| chatgpt for data analysis: Chat GPT for Retail Managers Matt Parmaks, 2023-11-18 ChatGPT for Retail Managers is an insightful book designed to help retail managers leverage the power of advanced AI like ChatGPT in their businesses. |
| chatgpt for data analysis: AI and Chatbots in FinTech Gioia Arnone, |
| chatgpt for data analysis: The AI Revolution in Project Management Vijay Kanabar, 2023-12-08 In a world where technology is rapidly evolving, the fusion of project management and artificial intelligence stands at the forefront of innovation. The AI Revolution in Project Management delves deep into the transformative power of generative AI tools that promise to reshape industries, and revolutionize how we manage projects. Whether you're looking to build dynamic teams using AI, choose a project development approach, or monitor project performance, this book has got you covered. Each chapter provides insightful narratives and includes a supplemental Technical Guide that provides tips on using the AI technology. With case studies and prompts, the dialogues showcase AI in action, from stakeholder engagement to risk management. Dive in with experts who’ve spent countless hours using these AI tools in project scenarios to offer a transparent view into generative AI-driven project management. In this book you'll learn: How to create prompts that generate meaningful and actionable insights tailored for your projects When to use AI to enhance decision-making, super-charge productivity, and elevate overall project efficiency Which generative AI models and plug-ins to use for specific project scenarios, ensuring seamless integration and maximum efficiency AI is not just a buzzword; it’s a tool reshaping how we manage projects and engage with stakeholders. - From the Foreward by Ricardo Viana Vargas, Ph.D. Ricardo is an experienced leader in global operations, project management, business transformation, and crisis management. As founder and managing director of Macrosolutions, a consulting firm with international operations in energy, infrastructure, IT, oil, and finance, he managed more than $20 billion in international projects in the past 25 years. Update As AI products continue to evolve, information published in this book may change. Please note that as of February 2024, there is a name change for Bing Chat and Bard Chat. Microsoft Bing Chat is now Copilot: https://copilot.microsoft.com/. Google Bard is now Gemini: https://gemini.google.com/. |
| chatgpt for data analysis: Unlocking ChatGPT's Potential: Practical Tips and Tricks for Everyday Use Colin Tandy, 2024-07-16 Unlocking ChatGPT's Potential: Practical Tips and Tricks for Everyday Use In a world rapidly evolving with the advances of artificial intelligence, ChatGPT emerges as a groundbreaking tool designed to transform the way we work, learn, and create. Unlocking ChatGPT's Potential: Practical Tips and Tricks for Everyday Use is your comprehensive guide to mastering this powerful AI assistant and integrating it seamlessly into your daily life. This book takes you on an enlightening journey, starting with the basics of setting up ChatGPT and understanding its core functionalities. As you delve deeper, you'll uncover practical applications that enhance productivity, streamline tasks, and organize information effortlessly. Whether you're drafting emails, generating creative content, conducting research, or managing personal projects, this guide provides step-by-step instructions and expert tips to maximize ChatGPT's potential. Explore the transformative power of ChatGPT across various domains: Productivity: Automate routine tasks, manage schedules, and boost efficiency. Creative Writing: Generate ideas, draft compelling content, and refine your writing. Research and Learning: Retrieve information, summarize articles, and gain clear explanations on complex topics. Personal Assistant: Plan events, manage tasks, and receive personalized assistance for everyday activities. Professional Settings: Enhance workplace communication, collaboration, marketing, and customer support. Advanced Features: Customize ChatGPT to suit your needs and integrate it with other tools. Ethical Use: Navigate the ethical considerations and best practices for responsible AI usage. Troubleshooting: Resolve common issues and optimize performance. Packed with real-world case studies, success stories, and answers to frequently asked questions, this book is an invaluable resource for both beginners and experienced users. It empowers you to leverage the full capabilities of ChatGPT, ensuring you stay ahead in an AI-driven future. About OPENAI: OpenAI, a leading force in artificial intelligence research and development, is committed to ensuring AI technologies benefit all of humanity. Our team of experts has crafted ChatGPT, an advanced language model designed to assist with a myriad of tasks, from simple conversations to complex problem-solving. Unlock the future of AI with Unlocking ChatGPT's Potential: Practical Tips and Tricks for Everyday Use and revolutionize the way you live and work |
| chatgpt for data analysis: HCI International 2023 – Late Breaking Papers Helmut Degen, Stavroula Ntoa, Abbas Moallem, 2023-11-25 This seven-volume set LNCS 14054-14060 constitutes the proceedings of the 25th International Conference, HCI International 2023, in Copenhagen, Denmark, in July 2023. For the HCCII 2023 proceedings, a total of 1578 papers and 396 posters was carefully reviewed and selected from 7472 submissions. Additionally, 267 papers and 133 posters are included in the volumes of the proceedings published after the conference, as “Late Breaking Work”. These papers were organized in the following topical sections: HCI Design and User Experience; Cognitive Engineering and Augmented Cognition; Cultural Issues in Design; Technologies for the Aging Population; Accessibility and Design for All; Designing for Health and Wellbeing; Information Design, Visualization, Decision-making and Collaboration; Social Media, Creative Industries and Cultural Digital Experiences; Digital Human Modeling, Ergonomics and Safety; HCI in Automated Vehicles and Intelligent Transportation; Sustainable Green Smart Cities and Smart Industry; eXtended Reality Interactions; Gaming and Gamification Experiences; Interacting with Artificial Intelligence; Security, Privacy, Trust and Ethics; Learning Technologies and Learning Experiences; eCommerce, Digital Marketing and eFinance. |
| chatgpt for data analysis: Applications of GPT in Finance, Compliance, and Audit Alexander Hüsch, |
| chatgpt for data analysis: Java Basics Using Chatgpt/Gpt-4 OSWALD. CAMPESATO, 2023-12-28 This book is designed for those new to Java and interested in understanding how ChatGPT/GPT-4 can enhance programming. It offers a unique approach to learning Java, combining traditional hand-written code with cutting-edge ChatGPT-generated examples. The book covers the basics of Java programming and development environments, including understanding recursion, strings, arrays, fundamental data structures, algorithm analysis, queues and stacks, and follows with the role of ChatGPT in generating, explaining, and debugging code. Companion files with source code and figures available for downloading. It's an essential resource for those starting Java programming and for anyone curious about the applications of ChatGPT in coding. FEATURES Combines hand-crafted Java code with ChatGPT-generated examples for a multifaceted learning experience Offers practical Java coding skills, with examples in recursion, data structures, and algorithm analysis Covers the capabilities of ChatGPT for code generation, debugging, and explanation, providing a modern perspective on programming Includes companion files for downloading with source code and figures |
| chatgpt for data analysis: Data Science in Production Ben Weber, 2020 Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production. From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: Translate models developed on a laptop to scalable deployments in the cloud Develop end-to-end systems that automate data science workflows Own a data product from conception to production The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries (github.com/bgweber/DS_Production). Book Contents Here are the topics covered by Data Science in Production: Chapter 1: Introduction - This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the book, and provide an overview of automated feature engineering. Chapter 2: Models as Web Endpoints - This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We'll start with scikit-learn models and also set up a deep learning endpoint with Keras. Chapter 3: Models as Serverless Functions - This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions. Chapter 4: Containers for Reproducible Models - This chapter will show how to use containers for deploying models with Docker. We'll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash. Chapter 5: Workflow Tools for Model Pipelines - This chapter focuses on scheduling automated workflows using Apache Airflow. We'll set up a model that pulls data from BigQuery, applies a model, and saves the results. Chapter 6: PySpark for Batch Modeling - This chapter will introduce readers to PySpark using the community edition of Databricks. We'll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database. Chapter 7: Cloud Dataflow for Batch Modeling - This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore. Chapter 8: Streaming Model Workflows - This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment. After working through this material, readers will learn how to use these message brokers to create streaming model pipelines with PySpark and Dataflow that provide near real-time predictions. Excerpts of these chapters are available on Medium (@bgweber), and a book sample is available on Leanpub. |
| chatgpt for data analysis: Higher Education Learning Methodologies and Technologies Online Gabriella Casalino, |
| chatgpt for data analysis: Artificial Intelligence Applications and Innovations Ilias Maglogiannis, |
| chatgpt for data analysis: Good Practices and New Perspectives in Information Systems and Technologies Álvaro Rocha, |
| chatgpt for data analysis: Infonomics Douglas B. Laney, 2017-09-05 Many senior executives talk about information as one of their most important assets, but few behave as if it is. They report to the board on the health of their workforce, their financials, their customers, and their partnerships, but rarely the health of their information assets. Corporations typically exhibit greater discipline in tracking and accounting for their office furniture than their data. Infonomics is the theory, study, and discipline of asserting economic significance to information. It strives to apply both economic and asset management principles and practices to the valuation, handling, and deployment of information assets. This book specifically shows: CEOs and business leaders how to more fully wield information as a corporate asset CIOs how to improve the flow and accessibility of information CFOs how to help their organizations measure the actual and latent value in their information assets. More directly, this book is for the burgeoning force of chief data officers (CDOs) and other information and analytics leaders in their valiant struggle to help their organizations become more infosavvy. Author Douglas Laney has spent years researching and developing Infonomics and advising organizations on the infinite opportunities to monetize, manage, and measure information. This book delivers a set of new ideas, frameworks, evidence, and even approaches adapted from other disciplines on how to administer, wield, and understand the value of information. Infonomics can help organizations not only to better develop, sell, and market their offerings, but to transform their organizations altogether. Doug Laney masterfully weaves together a collection of great examples with a solid framework to guide readers on how to gain competitive advantage through what he labels the unruly asset – data. The framework is comprehensive, the advice practical and the success stories global and across industries and applications. Liz Rowe, Chief Data Officer, State of New Jersey A must read for anybody who wants to survive in a data centric world. Shaun Adams, Head of Data Science, Betterbathrooms.com Phenomenal! An absolute must read for data practitioners, business leaders and technology strategists. Doug's lucid style has a set a new standard in providing intelligible material in the field of information economics. His passion and knowledge on the subject exudes thru his literature and inspires individuals like me. Ruchi Rajasekhar, Principal Data Architect, MISO Energy I highly recommend Infonomics to all aspiring analytics leaders. Doug Laney’s work gives readers a deeper understanding of how and why information should be monetized and managed as an enterprise asset. Laney’s assertion that accounting should recognize information as a capital asset is quite convincing and one I agree with. Infonomics enjoyably echoes that sentiment! Matt Green, independent business analytics consultant, Atlanta area If you care about the digital economy, and you should, read this book. Tanya Shuckhart, Analyst Relations Lead, IRI Worldwide |
| chatgpt for data analysis: Leveraging ChatGPT and Artificial Intelligence for Effective Customer Engagement Bansal, Rohit, Ngah, Abdul Hafaz, Chakir, Aziza, Pruthi, Nishita, 2024-01-24 Academic scholars find themselves confronted with a formidable challenge: staying abreast of the ever-evolving landscape of Artificial Intelligence(AI). The intricate interplay between AI and its profound impact on various facets of society, including customer engagement, remains an enigma for many. This knowledge gap not only hampers their ability to contribute meaningfully to their fields but also leaves them trailing behind the dynamic developments taking place in industries worldwide. As AI continues to reshape the business environment, it becomes imperative for academia to bridge this chasm between theory and practice. Leveraging ChatGPT and Artificial Intelligence for Effective Customer Engagement is an effective solution to the pressing problem at hand. With meticulous clarity, it unravels the complexities of ChatGPT, an innovative AI technology, and its revolutionary potential in the realm of customer engagement. It offers a lifeline to academic scholars seeking to navigate the uncharted territory of AI, providing them with an in-depth understanding of how ChatGPT can reshape customer interactions. |
| chatgpt for data analysis: Text Generative AI courseware Fabienne Mouris, Wahbe Rezek, 2023-10-20 The course is mainly practical (applying generative AI/prompt-engineering), in action activities where you make prompts and learn best practices on Text Generative AI, while doing. Gain a comprehensive understanding of Text Generative AI and its capabilities Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases Understanding of the architecture behind the model Master the skill of prompt engineering for effective interaction with Text Generative AI Learn how to integrate Text Generative AI into existing workflows Understand the limitations and risks associated with Text Generative AI Individuals who need a basic understanding Text Generative AI Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with it Business Analyst. The course is mainly practical, with interactive activities where you make prompts and learn by doing. You will also get feedback and guidance from experienced instructors and peers. By the end of the course, you will be able to: Gain a comprehensive understanding of Text Generative AI and its capabilities Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases Master the skill of prompt engineering for effective interaction with Text Generative AI Learn how to integrate Text Generative AI into existing workflows Understand the limitations and risks associated with Text Generative AI The course is suitable for anyone who needs a basic understanding of Text Generative AI, such as: Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with them Business analysts, marketers, content creators, educators, researchers, and other roles that can benefit from using Text Generative AI Students who want to explore the field of Text Generative AI and its applications The course prepares you for the EDF Certified (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field. |
| chatgpt for data analysis: Summary of Jake L. Kent's ChatGPT 4 10000 Per Month Milkyway Media, 2024-01-18 Get the Summary of Jake L. Kent's ChatGPT 4 10000 Per Month in 20 minutes. Please note: This is a summary & not the original book. ChatGPT 4 10000 Per Month by Jake L. Kent explores the multifaceted applications of ChatGPT, an AI language model developed by OpenAI. The book delves into the model's training on diverse internet text data, its deep learning techniques, and its ability to generate human-like text, making it suitable for various uses such as customer support, content creation, and more. Kent discusses the potential of ChatGPT to inspire creativity, particularly in the entertainment industry, and its role in generating online income... |
| chatgpt for data analysis: Ebook: Doing Your Research Project: A Guide for First-Time Researchers 8 WATERS, 2024-05-08 This new edition retains the excellent structure and tone of previous editions whilst bringing the text and examples up to date, reflecting the changing and dynamic social world we live and research in. Dr Steven Gascoigne, Assistant Professor, Centre for Lifelong Learning, University of Warwick, UK This book combines theoretical knowledge and practical skills with case studies, examples, and reflections in one easy-to-read book... A must for novice researchers. Dr Christina Cooper, Assistant Professor in Community Wellbeing, Northumbria University, UK Now on its eighth edition, Doing Your Research Project remains the authoritative guide to conducting an outstanding research project. Guiding readers through each stage of the research journey, this book gives students the confidence to successfully conceptualise and complete their research. Written in its trademark, student-friendly style, each chapter includes reflective questions to help students apply the advice to their own work. The authors cover a range of disciplines and methodologies, empowering students to make an informed choice about what best suits their research. While retaining its most-loved features from previous editions, the latest edition: Provides updated coverage of digital research techniques Examines the nature of research and the roles that researchers occupy Expands the discussion of research methods This bestselling resource is the ultimate companion to any research project, whether you are a first-time or experienced researcher. Practical, clear and concise, Doing Your Research Project is vital reading for anyone embarking on a research project. |
GitHub - ChatGPT-CN-Guide/chatgpt-4o: ChatGPT中文版：国内访 …
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ChatGPT 国内使用保姆教程以及无限制使用 ChatGPT 4.0 的方法（ …
May 27, 2025 · 中文版 ChatGPT 是 OpenAI 针对中文用户需求精心优化的智能对话工具，旨在提供更加流畅和精准的中文服务。与原版相比，中文版 ChatGPT 在多个方面更贴合国内用户的 …
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May 27, 2025 · ChatGPT 中文版和官网有何不同？ 中文版是专为国内用户优化的服务，通过镜像站提供更快、更稳定的访问，而官网需要翻墙访问。 ChatGPT 中文版是否支持 GPT-4？ 是 …
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chatgpt-chinese-gpt/ChatGPT-site-mirrors - GitHub
4 days ago · 无需翻墙，轻松访问 GPT-4 和 ChatGPT 的最新服务！本项目为您全面整理了国内可用的 ChatGPT 镜像站资源，涵盖站点推荐、功能对比、免费额度和详细使用教程，助您快速 …
Qualitative Content Analysis With ChatGPT
Jan 29, 2025 · interviews for data collection, to first enter the material into ChatGPT and ask the AI to perform a qualitative content analysis according to MAYRING (2022b). I was perplexed. …
Leveraging Gen AI (ChatGPT, Gemini) API for Advanced …
Fourth, it will show how to read output data to obtain the results from ChatGPT. The paper also presents a document-based question-answering system leveraging LangChain, Retrieval …
Leveraging Large Language Models to Analyze Continuous …
Apr 6, 2024 · medical time-series data and provide a comprehensive summary. In this work, assess the viability of using LLMs to analyze CGM data. In particular, we use GPT-4 [14] …
Google DeepMind s gemini AI versus ChatGPT: a …
Fig. 2 Output responses generated by Bard and ChatGPT. A Output generated by Bard from the prompt “How often should I have an eye exam?” (Left Panel). B Output generated by ChatGPT …
AI-Ready Workforce: Building an
use ChatGPT, and among these users, over one-quarter of their messages are about learning, tutoring, and school work, according to OpenAI user data. A new analysis of OpenAI user data …
Ozan.Evkaya@ed.ac.uk arXiv:2404.08480v1 [cs.LG] 12 Apr …
Figure 1: ChatGPT window to turn on the Data Analysis feature. After uploading the data, the user can immediately ask data analysis-related questions to the DA extension. In a seamless way, …
ChatGPT in education: A discourse analysis of worries and …
use. We analyzed Twitter data to identify key concerns related to the use of ChatGPT in education. We employed BERT-based topic modeling to conduct a discourse analysis and …
Shared Chat: Cleaned, Transposed CO2 Data - MIT Sloan …
Shared Chat: Cleaned, Transposed CO2 Data September 8, 2023 This chat contains files or images produced by Advanced Data Analysis which are not yet visible in Shared Chats. User: …
Evaluating ChatGPT-4.0's data analytic PAPERS proficiency …
conducting data analysis in the field of epidemiology, which encompassed aspects such as the reliability of analysis outcomes and operational complexity. In this study, based on a real-world …
Implementation of Naive Bayes classification algorithm for …
on 5000 Twitter users. Data was collected by scraping technique and Python programming language was used in data analysis. The results showed that the majority of Twitter users had …
Exploring the Power of ChatGPT - Springer
The response generated by ChatGPT is based on a thorough analysis of the previous text data it learned from your previous questions. It also uses advanced natural language processing …
Prompts, pearls, imperfections; comparing ChatGPT and a …
qualitative data analysis. Supplemental file 2: ChatGPT’s description of its own limitations and general analytic ... Theoretical saturation occurs when the data analysis reaches a point where …
Assisting Static Analysis with Large Language Models: A …
employs a bottom-up summary-based static analysis of the kernel. The analysis is a MAY analysis, where function summaries indicate potential bug occurrences, resulting in many bugs …
International Journal of Qualitative Methods Thematic …
ChatGPT for systematic thematic analysis. A more detailed explanation of each step is provided below. · Step 1: Familiarization, and Selection of Quotations: The first stage of systematic …
SWOT Analysis of the Use of ChatGPT in Education - jesma.net
SWOT Analysis of the Use of ChatGPT ... through SWOT analysis. Data Collection A survey form was used to collect data for the study. The survey form was prepared based on the related …
Data Analysis In Sports Science Utilizing Spss And Chatgpt
Data Quality is King: Ensure accurate and reliable data collection for meaningful analysis. Start Small: Begin with simple analyses and gradually increase complexity as your expertise grows. …
Harnessing AI for Breakthroughs in Bioinformatics: The Role …
analyze data, generate insights, and even assist in writing scientific papers. In this manuscript, we will explore how these tools can be utilized in various stages of biological research, from data …
Viewpoint Harnessing ChatGPT for thematic analysis: Are …
Utilizing ChatGPT in Thematic Analysis Given ChatGPT’s ability to handle large textual data and provide sets of meaningful codes and themes, as demonstrated by De Paoli [13], ChatGPT …
(UNIT 6) CHATGPT ADVANCE DATA ANALYSIS (UN
ChatGPT Advanced Data Analysis refers to the application of ChatGPT, enhanced with additional capabilities, for performing various data analysis tasks. This includes processing structured …
Chat GPT vs. Clinical Decision Support Systems in the …
multiple clinical decision support systems (CDSSs). These data-bases support healthcare professionals in determining the pres-ence, severity, and possible consequences of pDDIs.2,3 …
ChatGPT as Research Scientist: Probing GPT’s Capabilities …
ChatGPT’s abilities and limits across four domains related to scientific research: As a Research Librarian, Research Ethicist, Data Generator, and Novel Data Predictor. To what degree can …
Generative AI and ChatGPT: Applications, challenges, and …
consolidate data from different sources for analysis (Dasborough, 2023). ChatGPT passed the Turing test by fooling people to believe that its responses were from humans rather than …
Data augmentation using instruction-tuned models improves …
The eectiveness of ChatGPT has not yet been evaluated for emotion analysis in tweets. This paper investigates how effective ChatGPT is for tweet emotion analysis. Experimental results …
Evaluating ChatGPT and Bard AI on Arabic Sentiment …
analysis, focusing on single language models like AraT5 (Elmadany et al.,2022) or multiple models, including ChatGPT and others (Khondaker et al., 2023), to the best of our knowledge, …
ChatGPT adoption and anxiety: a cross-country analysis …
adoption intention of ChatGPT in two higher education contexts– the UK and Nepal. 239 and 226 questionnaires were deemed sufficient for data analysis for Nepal and the UK, respectively. …
Study and Analysis of Chat GPT and its Impact on Different …
necessary to supplement ChatGPT's training data with additional sources of information or to use alternative tools in situations where ChatGPT's knowledge is insufficient. ChatGPT's lack of …
Strengths, Weaknesses, Opportunities, and Threats of Using …
Figure 1. SWOT Analysis of Using ChatGPT in Scientific Research Results and Discussion In this section, we discuss the strengths, weaknesses, opportunities, and threats associated with …
The use of generative AI in statistical data analysis and its …
Xing uses ChatGPT 3.5, he was not able to test ChatGPT for the analysis of any data sets.19, pp. 98–100 These are the only contributions to date that examine the data analysis capacity of …
ChatGPT in Data Visualization Education: A Student …
riences with ChatGPT. Our analysis examined the advantages and barriers of using ChatGPT, students’ querying behavior, the types of assistance sought, and its impact on assignment …
ChatGPT vs Gemini vs LLaMA on Multilingual Sentiment …
underlying data, to improve their performance, interpretability and applicability. Index Terms—Sentiment Analysis, ChatGPT, Gemini, LLaMA, Large Language Models, Artificial …
Potential of ChatGPT in predicting stock market trends based …
investigates ChatGPT's capacity to predict stock market movements using only social media tweets and sentiment analysis. We aim to see if ChatGPT can tap into the vast sentiment data …
From Big to Small Without Losing It All: Text Augmentation …
Abstract—In the era of artificial intelligence, data is gold but costly to annotate. The paper demonstrates a groundbreaking solution to this dilemma using ChatGPT for text augmentation …
Summary of ChatGPT/GPT-4 Research and Perspective …
Solving dataset, ChatGPT performed poorly, receiving only two 4-point scores (out of a total of 5), with the majority of scores being 2 points. In the Holes-in-Proofs dataset, ChatGPT received …
The Role of ChatGPT in Democratizing Data Science: An …
within data analysis serves to showcase the practical benefits and potential challenges of integrating AI tools into data science workflows. 1.2 Potential Benefits and Challenges of Using …
Log Parsing: How Far Can ChatGPT Go? - arXiv.org
performance of ChatGPT under two experimental settings: 1) Few-shot scenarios: Since log data is heterogeneous, we follow a recent study [18] to provide a few demonstrations (1, 2, and 4) …
Making AI Less “Thirsty”: Uncovering and Addressing the …
Importantly, the company’s data center water consumption increased by ∼20% from 2021 to 2022 and by ∼17% from 2022 to 2023 [4], and another technology company’s data center water …
The Role of ChatGPT in Democratizing Data Science: An …
within data analysis serves to showcase the practical benefits and potential challenges of integrating AI tools into data science workflows. 1.2 Potential Benefits and Challenges of Using …
Human versus artificial intelligence: evaluating ChatGPT s …
Feb 15, 2025 · Klasova et al 18 as the primary source of data for this study. Specif - ically, we used ChatGPT to screen studies from the executed search strategy, select final included …
A Systematic Review of the Limitations and Associated …
3.2. Data analysis A thematic analysis, following the guidelines outlined by Braun and Clarke (2006), was utilized to uncover and cat-egorize the reported limitations of ChatGPT across the …
A STUDY ON CHAT GPT AND ITS IMPACT ON THE …
DATA ANALYSIS Out of the 83 respondents 63.9% (53 students) said they use ChatGpt regularly 26.5% (22 students) said that they have never tried the service 9.6% (8 students) said they are …
The advent of ChatGPT: Job Made Easy or Job Loss to Data …
data to examine this chatbot's prowess in performing data analysis. ChatGPT 3.5 and 4.0 accurately predicted the suitable statistical tool for analyzing the simulated datasets.
Using ChatGPT as a tool for training nonprogrammers to …
ders ChatGPT a strong tool for Python programming, particu-larly in tasks related to data handling. 12 In short, ChatGPT can allow a user to successfully generate a unique, …
arXiv:2303.05349v1 [stat.AP] 9 Mar 2023 - ResearchGate
Sample Regarding the data collection and analysis, we use similar strategies as other studies using TikTok data (Basch et al., 2021; Fiallos et al., 2021; Fowler et al., 2022; McCashin and …
Generative AI for corpus approaches to discourse studies: A …
representing specialised genres and/or contexts. For concordance analysis, ChatGPT performs poorly, as the results include false inferences about the concordance lines and, at times, …
ChatGPT: Understanding Code Syntax and Semantics
explore the capability of ChatGPT for code analysis. We are the first to explore ChatGPT’s capability in understanding code syntax, static behaviors, and dynamic behaviors. We have …
ChatGPT Tweets Sentiment Analysis Using Machine …
In machine learning and data analysis, feature extraction is a key concept. It describes the method of choosing and modifying pertinent data (features) from raw data to be utilized as …
Using Chat Gpt For Data Analysis - media.wickedlocal.com
This isn't science fiction; it's the reality ChatGPT is bringing to data analysis. While it's not a replacement for dedicated data science tools like R or Python, it acts as a powerful assistant, …
ChatGPT for Economic Analysis - REMI
How to leverage ChatGPT for Economic Analysis. Capability 1: Write-Up Provide brief write-up on economic forecast or simulation results. Copy REMI National Baseline Data. ... Google Sheets …
ChatGPT in Dermatology: A Comprehensive Systematic …
Jun 11, 2023 · ensure a thorough analysis. The synthesis of findings utilized network analysis and thematic synthesis methodologies. Results: There was a total of 87 manuscripts that fulfilled …
ChatGPT in Science Education: A Visualization Analysis of …
Nov 24, 2024 · C. Data Analysis The data was sourced from Scopus journal research publications in the range of 2022-2024. After searching, the data from Scopus was exported to Microsoft …
