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| can chatgpt do 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. |
| can chatgpt do 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. |
| can chatgpt do 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. |
| can chatgpt do 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. |
| can chatgpt do 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. |
| can chatgpt do 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. |
| can chatgpt do data analysis: Data Analysis with Excel® Les Kirkup, 2002-03-07 An essential introduction to data analysis techniques using spreadsheets, for undergraduate and graduate students. |
| can chatgpt do data analysis: Data Analytics and Machine Learning Pushpa Singh, |
| can chatgpt do 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. |
| can chatgpt do 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! |
| can chatgpt do data analysis: R Data Analysis Projects Gopi Subramanian, 2017-11-17 Get valuable insights from your data by building data analysis systems from scratch with R. About This Book A handy guide to take your understanding of data analysis with R to the next level Real-world projects that focus on problems in finance, network analysis, social media, and more From data manipulation to analysis to visualization in R, this book will teach you everything you need to know about building end-to-end data analysis pipelines using R Who This Book Is For If you are looking for a book that takes you all the way through the practical application of advanced and effective analytics methodologies in R, then this is the book for you. A fundamental understanding of R and the basic concepts of data analysis is all you need to get started with this book. What You Will Learn Build end-to-end predictive analytics systems in R Build an experimental design to gather your own data and conduct analysis Build a recommender system from scratch using different approaches Use and leverage RShiny to build reactive programming applications Build systems for varied domains including market research, network analysis, social media analysis, and more Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively Communicate modeling results using Shiny Dashboards Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling In Detail R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it's one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You'll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You'll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You'll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you'll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this book, you'll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle. Style and approach This book takes a unique, learn-as-you-do approach, as you build on your understanding of data analysis progressively with each project. This book is designed in a way that implementing each project will empower you with a unique skill set, and enable you to implement the next project more confidently. |
| can chatgpt do 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. |
| can chatgpt do data analysis: Data Analysis with Stata Prasad Kothari, 2015-10-28 Explore the big data field and learn how to perform data analytics and predictive modelling in STATA About This Book Visualize and analyse data in STATA to devise a business strategy Learn STATA programming and predictive modeling Discover how you can become a data scientist with the power of STATA Who This Book Is For This book is for all the professionals and students who want to learn STATA programming and apply predictive modelling concepts. This book is also very helpful for experienced STATA programmers as it provides advanced statistical modelling concepts and their application. What You Will Learn Perform important statistical tests to become a STATA data scientist Be guided through how to program in STATA Implement logistic and linear regression models Visualize and program the data in STATA Analyse survey data, time series data, and survival data Perform database management in STATA In Detail STATA is an integrated software package that provides you with everything you need for data analysis, data management, and graphics. STATA also provides you with a platform to efficiently perform simulation, regression analysis (linear and multiple) [and custom programming. This book covers data management, graphs visualization, and programming in STATA. Starting with an introduction to STATA and data analytics you'll move on to STATA programming and data management. Next, the book takes you through data visualization and all the important statistical tests in STATA. Linear and logistic regression in STATA is also covered. As you progress through the book, you will explore a few analyses, including the survey analysis, time series analysis, and survival analysis in STATA. You'll also discover different types of statistical modelling techniques and learn how to implement these techniques in STATA. Style and approach This book is a hands-onguide to STATA programming and statistical modelling providing many STATA code examples and taking. You through the working of the code in detail. |
| can chatgpt do data analysis: Mastering Data Analysis with R Gergely Daroczi, 2015-09-30 Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples. |
| can chatgpt do 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 |
| can chatgpt do data analysis: Getting Started with Haskell Data Analysis James Church, 2018-10-31 Put your Haskell skills to work and generate publication-ready visualizations in no time at all Key FeaturesTake your data analysis skills to the next level using the power of HaskellUnderstand regression analysis, perform multivariate regression, and untangle different cluster varietiesCreate publication-ready visualizations of dataBook Description Every business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner. This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world. By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis. What you will learnLearn to parse a CSV file and read data into the Haskell environmentCreate Haskell functions for common descriptive statistics functionsCreate an SQLite3 database using an existing CSV fileLearn the versatility of SELECT queries for slicing data into smaller chunksApply regular expressions in large-scale datasets using both CSV and SQLite3 filesCreate a Kernel Density Estimator visualization using normal distributionWho this book is for This book is intended for people who wish to expand their knowledge of statistics and data analysis via real-world examples. A basic understanding of the Haskell language is expected. If you are feeling brave, you can jump right into the functional programming style. |
| can chatgpt do 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 |
| can chatgpt do data analysis: Java Data Analysis John R. Hubbard, 2017-09-19 Get the most out of the popular Java libraries and tools to perform efficient data analysis About This Book Get your basics right for data analysis with Java and make sense of your data through effective visualizations. Use various Java APIs and tools such as Rapidminer and WEKA for effective data analysis and machine learning. This is your companion to understanding and implementing a solid data analysis solution using Java Who This Book Is For If you are a student or Java developer or a budding data scientist who wishes to learn the fundamentals of data analysis and learn to perform data analysis with Java, this book is for you. Some familiarity with elementary statistics and relational databases will be helpful but is not mandatory, to get the most out of this book. A firm understanding of Java is required. What You Will Learn Develop Java programs that analyze data sets of nearly any size, including text Implement important machine learning algorithms such as regression, classification, and clustering Interface with and apply standard open source Java libraries and APIs to analyze and visualize data Process data from both relational and non-relational databases and from time-series data Employ Java tools to visualize data in various forms Understand multimedia data analysis algorithms and implement them in Java. In Detail Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you'll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you'll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java. Style and approach The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy-to-follow examples, this book will turn you into an ace data analyst in no time. |
| can chatgpt do data analysis: Python Data Analysis Cookbook Ivan Idris, 2016-07-22 Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books Who This Book Is For This book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed. What You Will Learn Set up reproducible data analysis Clean and transform data Apply advanced statistical analysis Create attractive data visualizations Web scrape and work with databases, Hadoop, and Spark Analyze images and time series data Mine text and analyze social networks Use machine learning and evaluate the results Take advantage of parallelism and concurrency In Detail Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios. Style and Approach The book is written in “cookbook” style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained. |
| can chatgpt do 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. |
| can chatgpt do data analysis: Data Analytics Made Easy Andrea de Mauro, 2021-08-30 Make informed decisions using data analytics, machine learning, and data visualizations Key Features: Take raw data and transform it to add value to your organization Learn the art of telling stories with your data to engage with your audience Apply machine learning algorithms to your data with a few clicks of a button Book Description: Data analytics has become a necessity in modern business, and skills such as data visualization, machine learning, and digital storytelling are now essential in every field. If you want to make sense of your data and add value with informed decisions, this is the book for you. Data Analytics Made Easy is an accessible guide to help you start analyzing data and quickly apply these skills to your work. It focuses on how to generate insights from your data at the click of a few buttons, using the popular tools KNIME and Microsoft Power BI. The book introduces the concepts of data analytics and shows you how to get your data ready and apply ML algorithms. Implement a full predictive analytics solution with KNIME and assess its level of accuracy. Create impressive visualizations with Microsoft Power BI and learn the greatest secret in successful analytics - how to tell a story with your data. You'll connect the dots on the various stages of the data-to-insights process and gain an overview of alternative tools, including Tableau and H20 Driverless AI. By the end of this book, you will have learned how to implement machine learning algorithms and sell the results to your customers without writing a line of code. What You Will Learn: Understand the potential of data and its impact on any business Influence business decisions with effective data storytelling when delivering insights Use KNIME to import, clean, transform, combine data feeds, and automate recurring workflows Learn the basics of machine learning and AutoML to add value to your organization Build, test, and validate simple supervised and unsupervised machine learning models with KNIME Use Power BI and Tableau to build professional-looking and business-centric visuals and dashboards Who this book is for: Whether you are working with data experts or want to find insights in your business' data, you'll find this book an effective way to add analytics to your skill stack. No previous math, statistics, or computer science knowledge is required. |
| can chatgpt do data analysis: Analyzing Data with Power BI and Power Pivot for Excel Alberto Ferrari, Marco Russo, 2017-04-28 Renowned DAX experts Alberto Ferrari and Marco Russo teach you how to design data models for maximum efficiency and effectiveness. How can you use Excel and Power BI to gain real insights into your information? As you examine your data, how do you write a formula that provides the numbers you need? The answers to both of these questions lie with the data model. This book introduces the basic techniques for shaping data models in Excel and Power BI. It’s meant for readers who are new to data modeling as well as for experienced data modelers looking for tips from the experts. If you want to use Power BI or Excel to analyze data, the many real-world examples in this book will help you look at your reports in a different way–like experienced data modelers do. As you’ll soon see, with the right data model, the correct answer is always a simple one! By reading this book, you will: • Gain an understanding of the basics of data modeling, including tables, relationships, and keys • Familiarize yourself with star schemas, snowflakes, and common modeling techniques • Learn the importance of granularity • Discover how to use multiple fact tables, like sales and purchases, in a complex data model • Manage calendar-related calculations by using date tables • Track historical attributes, like previous addresses of customers or manager assignments • Use snapshots to compute quantity on hand • Work with multiple currencies in the most efficient way • Analyze events that have durations, including overlapping durations • Learn what data model you need to answer your specific business questions About This Book • For Excel and Power BI users who want to exploit the full power of their favorite tools • For BI professionals seeking new ideas for modeling data |
| can chatgpt do data analysis: Become a Python Data Analyst Alvaro Fuentes, 2018-08-31 Enhance your data analysis and predictive modeling skills using popular Python tools Key Features Cover all fundamental libraries for operation and manipulation of Python for data analysis Implement real-world datasets to perform predictive analytics with Python Access modern data analysis techniques and detailed code with scikit-learn and SciPy Book Description Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this book, you will have hands-on experience performing data analysis with Python. What you will learn Explore important Python libraries and learn to install Anaconda distribution Understand the basics of NumPy Produce informative and useful visualizations for analyzing data Perform common statistical calculations Build predictive models and understand the principles of predictive analytics Who this book is for Become a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book |
| can chatgpt do data analysis: Qualitative Research with Socio-technical Grounded Theory Rashina Hoda, 2024 This book is a timely and practical guide to conducting qualitative research with a socio-technical approach. It covers the foundations of research including research design, research philosophy, and literature review; describes qualitative data collection, qualitative data preparation and filtering; explains qualitative data analysis using the techniques of socio-technical grounded theory (STGT); and presents the advanced techniques of qualitative theory development using emergent or structured modes. It provides guidance on evaluating qualitative research application and outcomes; and explores the possible role of Artificial Intelligence (AI) in qualitative research in the future. The book is structured into five parts. Part I - Introduction includes three chapters that serve to provide: an overview of the book in Chapter 1; a brief history of the origins and evolution of the GT methods in Chapter 2; and an introduction to STGT in Chapter 3. Part II - Foundations of Research includes three chapters that cover: the building blocks of empirical research through a simple yet powerful approach to designing research methods (the research design canvas) in Chapter 4; the fundamental concepts of research philosophy in Chapter 5; and the myriad of literature review methods including those suited to STGT in Chapter 6. Part III - Qualitative Data Collection and Analysis includes four chapters that explain: the key concepts related to collecting qualitative data in Chapter 7; techniques used for collecting qualitative data in Chapter 8; how to go about preparing and filtering qualitative data in Chapter 9; and the qualitative data analysis procedures of open coding, constant comparison, and memoing in Chapter 10. Part IV - Theory Development includes two chapters that explain: what is considered theory (or theoretical outcomes) in Chapter 11; and the advanced STGT steps of theory development in Chapter 12. Eventually, Part V - Evaluation and Future Directions includes two chapters that: present the evaluation guidelines for assessing STGT applications and outcomes in Chapter 13; and explore new opportunities in qualitative research using large language models in Chapter 14. This book enables new and experienced researchers in modern as well as traditional disciplines to conduct rigorous qualitative research on socio-technical topics in the digital world. They will be able to approach qualitative research with confidence and produce valuable research outcomes in the form of rich descriptive findings, taxonomies, theoretical models, theoretical frameworks, preliminary and mature theories, recommendations, and guidelines, all grounded in empirical evidence. |
| can chatgpt do data analysis: Practical Data Analysis Using Jupyter Notebook Marc Wintjen, 2020-06-19 Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book. |
| can chatgpt do 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. |
| can chatgpt do data analysis: Real-Time Big Data Analytics Sumit Gupta, Shilpi,, 2016-02-26 Design, process, and analyze large sets of complex data in real time About This Book Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm Implement strategies to solve the challenges of real-time data processing Load datasets, build queries, and make recommendations using Spark SQL Who This Book Is For If you are a Big Data architect, developer, or a programmer who wants to develop applications/frameworks to implement real-time analytics using open source technologies, then this book is for you. What You Will Learn Explore big data technologies and frameworks Work through practical challenges and use cases of real-time analytics versus batch analytics Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm Handle and process real-time transactional data Optimize and tune Apache Storm for varied workloads and production deployments Process and stream data with Amazon Kinesis and Elastic MapReduce Perform interactive and exploratory data analytics using Spark SQL Develop common enterprise architectures/applications for real-time and batch analytics In Detail Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time. Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases. From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm. Moving on, we'll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program. You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark. At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data. Style and approach This step-by-step is an easy-to-follow, detailed tutorial, filled with practical examples of basic and advanced features. Each topic is explained sequentially and supported by real-world examples and executable code snippets. |
| can chatgpt do data analysis: Applications, Challenges, and the Future of ChatGPT Sharma, Priyanka, Jyotiyana, Monika, Kumar, A.V. Senthil, 2024-05-28 The rapid progress of artificial intelligence (AI) technologies has resulted in a complicated landscape for researchers and practitioners. Understanding and navigating the complexities of AI applications, particularly in the context of ChatGPT and its interactions with other AI tools, can be challenging. Researchers and academics need guidance to keep up with these technologies' evolving trends and implications, which leads to gaps in knowledge and implementation strategies. Additionally, the ethical and societal impacts of integrating AI into various domains remain a significant concern, requiring a comprehensive approach to address. Applications, Challenges, and the Future of ChatGPT provide a comprehensive solution to these issues by offering a detailed analysis of the current research trends in AI, focusing on ChatGPT and its interactions with other AI tools. The book delves into how we can effectively utilize ChatGPT and other AI tools to address complex problems by exploring AI applications' collaborative potentials and emerging paradigms. By identifying research gaps and suggesting future directions, this book equips researchers and practitioners with the knowledge and tools necessary to navigate the evolving landscape of AI. |
| can chatgpt do data analysis: Large Language Models Oswald Campesato, 2024-10-02 This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher. |
| can chatgpt do data analysis: Revolutionizing the Service Industry With OpenAI Models Demir, Mahmut, Dalgic, Ali, 2024-04-23 The pressing challenges businesses face are rising, including the ethical considerations associated with artificial intelligence (AI) in the service industry. Revolutionizing the Service Industry with OpenAI Models is a book rooted in real-world examples and expert insights, which provides practical solutions by implementing the latest technology and explores the impact of AI-powered conversational agents on the service sector. The book commences with an enlightening introduction that underscores the transformative power of ChatGPT and OpenAI models, setting the stage for a deep dive into their role in reshaping the service industry. Through a systematic examination of current challenges and opportunities, the text unveils the innovations brought about by ChatGPT in customer service, revealing its potential to enhance response times, handle complex inquiries, and deliver personalized experiences on an unprecedented scale. Delving into designing conversational user interfaces, addressing language barriers, and deploying AI in sensitive sectors like healthcare, the book guides readers through responsible and effective implementation. It balances the exploration of AI-driven automation with understanding the importance of maintaining a human touch in service interactions. This book is ideal for business owners, managers, and decision-makers seeking to leverage AI-powered conversational agents for strategic advantage. Simultaneously, AI enthusiasts, researchers, and developers will find invaluable insights into the current issues, best practices, and future trends in AI-driven customer interactions. |
| can chatgpt do data analysis: c't Working with AI c't-Redaktion, 2024-01-24 The special issue of c't KI-Praxis provides tests and practical instructions for working with chatbots. It explains why language models make mistakes and how they can be minimised. This not only helps when you send questions and orders to one of the chatbots offered online. If you do not want to or are not allowed to use the cloud services for data protection reasons, for example, you can also set up your own voice AI. The c't editorial team explains where to find a suitable voice model, how to host it locally and which service providers can host it. The fact that generative AI is becoming increasingly productive harbours both opportunities and risks. Suitable rules for the use of AI in schools, training and at work help to exploit opportunities and minimise risks. |
| can chatgpt do 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/. |
| can chatgpt do data analysis: Intelligent Computing Kohei Arai, 2024 Explore the forefront of computing with the proceedings of the Computing Conference 2024. Featuring 165 carefully selected papers from a pool of 457 submissions, this collection encapsulates the cutting-edge research and innovation presented during the conference. Delve into a diverse range of topics, insights, and methodologies that shape the future of computing. Whether you're an academic, researcher, or enthusiast, this concise volume offers a snapshot of the dynamic and collaborative spirit defining the Computing Conference 2024. |
| can chatgpt do 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. |
| can chatgpt do data analysis: Applications of GPT in Finance, Compliance, and Audit Alexander Hüsch, |
| can chatgpt do data analysis: Towards Digitally Transforming Accounting and Business Processes Tankiso Moloi, Babu George, 2024-01-11 This conference volume discusses the findings of the iCAB 2023 conference that took place in Johannesburg, South Africa. The University of Johannesburg (UJ School of Accounting and Johannesburg Business School) in collaboration with Alcorn State University (USA), Salem State University (USA) and Universiti Teknologi Mara (Malaysia) hosted the iCAB 2023 conference with the aim to bring together researchers from different Accounting and Business Management fields to share ideas and discuss how new disruptive technological developments are impacting the field of accounting. The conference was sponsored by the Association of International Certified Professional Accountants AICPA & CIMA. |
| can chatgpt do data analysis: Aesthetic Clinic Marketing in the Digital Age Wendy Lewis, 2024-04-16 Aesthetic practitioners and medical spas have flourished, and global statistics indicate that consumer engagement is at an all-time high. The rise of social media provides new ways to connect with consumers and differentiate clinics that stand out in the marketplace. For busy clinic managers and practitioners, keeping up with the nuances of digital media trends is practically a full-time job. With over 25 years of experience in medical aesthetics, Wendy Lewis shares her deep understanding of the challenges aesthetic practitioners face to effectively market their products and services, manage patients, and increase profits. This updated text is the definitive expert user’s guide written specifically for healthcare professionals and medical spa managers to present best practices and actionable strategies for mastering digital marketing, social media, branding, and the advent of AI. It is a must-read for aesthetic practitioners to keep up with the latest developments they need to manage their businesses effectively in this highly competitive field. This second edition features: NEW chapters on hot topics you need to know MORE tips, charts, tables, and resources UP-TO-THE-MINUTE strategies for success |
| can chatgpt do data analysis: Hands-On Data Analysis with Scala Rajesh Gupta, 2019-05-03 Master scala's advanced techniques to solve real-world problems in data analysis and gain valuable insights from your data Key FeaturesA beginner's guide for performing data analysis loaded with numerous rich, practical examplesAccess to popular Scala libraries such as Breeze, Saddle for efficient data manipulation and exploratory analysisDevelop applications in Scala for real-time analysis and machine learning in Apache SparkBook Description Efficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease. The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint. By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insights What you will learnTechniques to determine the validity and confidence level of dataApply quartiles and n-tiles to datasets to see how data is distributed into many bucketsCreate data pipelines that combine multiple data lifecycle stepsUse built-in features to gain a deeper understanding of the dataApply Lasso regression analysis method to your dataCompare Apache Spark API with traditional Apache Spark data analysisWho this book is for If you are a data scientist or a data analyst who wants to learn how to perform data analysis using Scala, this book is for you. All you need is knowledge of the basic fundamentals of Scala programming. |
| can chatgpt do data analysis: Proceedings of IAC 2024 in Prague Group of Authors, 2024-03-21 International Academic Conferences: - Teaching, Learning and E-learning (IAC-TLEl) - Management, Economics and Marketing (IAC-MEM) |
| can chatgpt do data analysis: Advances in Accounting Education Thomas G. Calderon, 2023-12-14 Advances in Accounting Education: Teaching and Curriculum Innovations Volume 27 features 11 peer-reviewed papers surrounding the themes of applied professional research and skills building, generative artificial intelligence and analytics in the accounting curriculum then innovative practices in cost accounting and other areas. |
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