| deep dive data analysis: Analytics for the Internet of Things (IoT) Andrew Minteer, 2017-07-24 Break through the hype and learn how to extract actionable intelligence from the flood of IoT data About This Book Make better business decisions and acquire greater control of your IoT infrastructure Learn techniques to solve unique problems associated with IoT and examine and analyze data from your IoT devices Uncover the business potential generated by data from IoT devices and bring down business costs Who This Book Is For This book targets developers, IoT professionals, and those in the field of data science who are trying to solve business problems through IoT devices and would like to analyze IoT data. IoT enthusiasts, managers, and entrepreneurs who would like to make the most of IoT will find this equally useful. A prior knowledge of IoT would be helpful but is not necessary. Some prior programming experience would be useful What You Will Learn Overcome the challenges IoT data brings to analytics Understand the variety of transmission protocols for IoT along with their strengths and weaknesses Learn how data flows from the IoT device to the final data set Develop techniques to wring value from IoT data Apply geospatial analytics to IoT data Use machine learning as a predictive method on IoT data Implement best strategies to get the most from IoT analytics Master the economics of IoT analytics in order to optimize business value In Detail We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You'll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We'll also review the economics of IoT analytics and you'll discover ways to optimize business value. By the end of the book, you'll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling. Style and approach This book follows a step-by-step, practical approach to combine the power of analytics and IoT and help you get results quickly |
| deep dive data analysis: Practical Machine Learning for Data Analysis Using Python Abdulhamit Subasi, 2020-06-05 Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features |
| deep dive data analysis: SPSS Statistics for Data Analysis and Visualization Keith McCormick, Jesus Salcedo, 2017-05-01 Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These hidden tools can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need. |
| deep dive data analysis: Beloved Brands Graham Robertson, 2018-01-06 Beloved Brands is a book every CMO or would-be CMO should read. Al Ries With Beloved Brands, you will learn everything you need to know so you can build a brand that your consumers will love. You will learn how to think strategically, define your brand with a positioning statement and a brand idea, write a brand plan everyone can follow, inspire smart and creative marketing execution, and be able to analyze the performance of your brand through a deep-dive business review. Marketing pros and entrepreneurs, this book is for you. Whether you are a VP, CMO, director, brand manager or just starting your marketing career, I promise you will learn how to realize your full potential. You could be in brand management working for an organization or an owner-operator managing a branded business. Beloved Brands provides a toolbox intended to help you every day in your job. Keep it on your desk and refer to it whenever you need to write a brand plan, create a brand idea, develop a creative brief, make advertising decisions or lead a deep-dive business review. You can even pass on the tools to your team, so they can learn how to deliver the fundamentals needed for your brands. This book is also an excellent resource for marketing professors, who can use it as an in-class textbook to develop future marketers. It will challenge communications agency professionals, who are looking to get better at managing brands, including those who work in advertising, public relations, in-store marketing, digital advertising or event marketing. Most books on branding are really for the MARCOM crowd. They sound good, but you find it's all fluff when you try to take it from words to actions. THIS BOOK IS DIFFERENT! Graham does a wonderful job laying out the steps in clear language and goes beyond advertising and social media to show how branding relates to all aspects of GENERAL as well as marketing management. Make no mistake: there is a strong theoretical foundation for all he says...but he spares you the buzzwords. Next year my students will all be using this book. Kenneth B. (Ken) Wong, Queen's University If you are an entrepreneur who has a great product and wants to turn it into a brand, you can use this book as a playbook. These tips will help you take full advantage of branding and marketing, and make your brand more powerful and more profitable. You will learn how to think, define, plan, execute and analyze, and I provide every tool you will ever need to run your brand. You will find models and examples for each of the four strategic thinking methods, looking at core strength, competitive, consumer and situational strategies. To define the brand, I will provide a tool for writing a brand positioning statement as well as a consumer profile and a consumer benefits ladder. I have created lists of potential functional and emotional benefits to kickstart your thinking on brand positioning. We explore the step-by-step process to come up with your brand idea and bring it all together with a tool for writing the ideal brand concept. For brand plans, I provide formats for a long-range brand strategy roadmap and the annual brand plan with definitions for each planning element. From there, I show how to build a brand execution plan that includes the creative brief, innovation process, and sales plan. I provide tools for how to create a brand calendar and specific project plans. To grow your brand, I show how to make smart decisions on execution around creative advertising and media choices. When it comes time for the analytics, I provide all the tools you need to write a deep-dive business review, looking at the marketplace, consumer, channels, competitors and the brand. Write everything so that it is easy to follow and implement for your brand. My promise to help make you smarter so you can realize your full potential. |
| deep dive data analysis: Effective Python Brett Slatkin, 2015 Effective Python will help students harness the full power of Python to write exceptionally robust, efficient, maintainable, and well-performing code. Utilizing the concise, scenario-driven style pioneered in Scott Meyers's best-selling Effective C++, Brett Slatkin brings together 53 Python best practices, tips, shortcuts, and realistic code examples from expert programmers. Each section contains specific, actionable guidelines organized into items, each with carefully worded advice supported by detailed technical arguments and illuminating examples. |
| deep dive data analysis: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| deep dive data analysis: Data Analysis Foundations with Python Cuantum Technologies LLC, 2024-06-12 Dive into data analysis with Python, starting from the basics to advanced techniques. This course covers Python programming, data manipulation with Pandas, data visualization, exploratory data analysis, and machine learning. Key Features From Python basics to advanced data analysis techniques. Apply your skills to practical scenarios through real-world case studies. Detailed projects and quizzes to help gain the necessary skills. Book DescriptionEmbark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently. Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling. To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.What you will learn Develop a strong foundation in Python for data analysis. Manipulate and analyze data using NumPy and Pandas. Create insightful data visualizations with Matplotlib and Seaborn. Understand and apply probability theory and hypothesis testing. Implement supervised and unsupervised machine learning algorithms. Execute real-world data analysis projects with confidence. Who this book is for This course adopts a hands-on approach, seamlessly blending theoretical lessons with practical exercises and real-world case studies. Practical exercises are designed to apply theoretical knowledge, providing learners with the opportunity to experiment and learn through doing. Real-world applications and examples are integrated throughout the course to contextualize concepts, making the learning process engaging, relevant, and effective. By the end of the course, students will have a thorough understanding of the subject matter and the ability to apply their knowledge in practical scenarios. |
| deep dive data analysis: Mathematical Foundations for Data Analysis Jeff M. Phillips, 2021-03-29 This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques. |
| deep dive data analysis: Big Data Analysis and Deep Learning Applications Thi Thi Zin, Jerry Chun-Wei Lin, 2018-06-06 This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities. |
| deep dive data analysis: Data Analysis and Visualization Using Python Dr. Ossama Embarak, 2018-11-20 Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far. What You Will LearnUse Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems Who This Book Is For Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python. |
| deep dive 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. |
| deep dive data analysis: Data Analysis with Python David Taieb, 2018-12-31 Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key FeaturesBridge your data analysis with the power of programming, complex algorithms, and AIUse Python and its extensive libraries to power your way to new levels of data insightWork with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time seriesExplore this modern approach across with key industry case studies and hands-on projectsBook Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learnA new toolset that has been carefully crafted to meet for your data analysis challengesFull and detailed case studies of the toolset across several of today’s key industry contextsBecome super productive with a new toolset across Python and Jupyter NotebookLook into the future of data science and which directions to develop your skills nextWho this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development. |
| deep dive data analysis: R in Action Robert Kabacoff, 2015-03-03 R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data. R in Action, Second Edition is a language tutorial focused on practical problems. Written by a research methodologist, it takes a direct and modular approach to quickly give readers the information they need to produce useful results. Focusing on realistic data analyses and a comprehensive integration of graphics, it follows the steps that real data analysts use to acquire their data, get it into shape, analyze it, and produce meaningful results that they can provide to clients. Purchase of the print book comes with an offer of a free PDF eBook from Manning. Also available is all code from the book. |
| deep dive data analysis: Ultimate Python Libraries for Data Analysis and Visualization Abhinaba Banerjee, 2024-04-04 Test your Data Analysis skills to its fullest using Python and other no-code tools KEY FEATURES ● Comprehensive coverage of Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, Julius AI for data acquisition, preparation, analysis, and visualization ● Real-world projects and practical applications for hands-on learning ● In-depth exploration of low-code and no-code tools for enhanced productivity DESCRIPTION Ultimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets. Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more. By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax. WHAT WILL YOU LEARN ● Acquire data from various sources using Python, including web scraping, APIs, and databases. ● Clean and prepare datasets for analysis, handling missing values, outliers, and inconsistencies. ● Conduct exploratory data analysis to uncover patterns, trends, and relationships within your data. ● Perform statistical analysis using Python libraries such as NumPy and Pandas, including hypothesis testing and regression analysis. ● Master time series analysis techniques for forecasting future trends and making data-driven decisions. ● Apply signal processing methods to analyze and interpret signals in data, such as audio, image, and sensor data. ● Engage in real-world projects across diverse industries, from finance to healthcare, to reinforce your skills and experience. ● Utilize Python for in-depth analysis of real-world datasets, gaining practical experience and insights. ● Refer to the Python cheat sheet in the appendix for quick access to common functions and syntax, aiding your learning and development. WHO IS THIS BOOK FOR? This book is ideal for beginners, professionals, or students aiming to enhance their careers through hands-on experience in data acquisition, preparation, analysis, time series, and signal processing. Prerequisite knowledge includes basic Python and introductory statistics. Whether starting fresh or seeking to refresh skills, this comprehensive guide helps readers upskill effectively. TABLE OF CONTENTS 1. Introduction to Data Analysis and Data Visualization using Python 2. Data Acquisition 3. Data Cleaning and Preparation 4. Exploratory Data Analysis 5. Statistical Analysis 6. Time Series Analysis and Forecasting 7. Signal Processing 8. Analyzing Real-World Data Sets using Python APPENDIX A Python Cheat Sheet Index |
| deep dive data analysis: Big Data Analytics Kiran Chaudhary, Mansaf Alam, 2021-12-27 Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics. |
| deep dive data analysis: Clinical Analytics and Data Management for the DNP Martha L. Sylvia, PhD, MBA, RN, Mary F. Terhaar, PhD, RN, ANEF, FAAN, 2023-01-18 Praise for the first edition: DNP students may struggle with data management, since their projects are not research but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects. Score: 98, 5 Stars -- Doody's Medical Reviews This unique text and reference—the only book to address the full spectrum of clinical data management for the DNP student—instills a fundamental understanding of how clinical data is gathered, used, and analyzed, and how to incorporate this data into a quality DNP project. The new third edition is updated to reflect changes in national health policy such as quality measurements, bundled payments for specialty care, and Advances to the Affordable Care Act (ACA) and evolving programs through the Centers for Medicare and Medicaid Services (CMS). The third edition reflects the revision of 2021 AACN Essentials and provides data sets and other examples in Excel and SPSS format, along with several new chapters. This resource takes the DNP student step-by-step through the complete process of data management, from planning through presentation, clinical applications of data management that are discipline-specific, and customization of statistical techniques to address clinical data management goals. Chapters are brimming with descriptions, resources, and exemplars that are helpful to both faculty and students. Topics spotlight requisite competencies for DNP clinicians and leaders such as phases of clinical data management, statistics and analytics, assessment of clinical and economic outcomes, value-based care, quality improvement, benchmarking, and data visualization. A progressive case study highlights multiple techniques and methods throughout the text. New to the Third Edition: New Chapter: Using EMR Data for the DNP Project New chapter solidifies link between EBP and Analytics for the DNP project New chapter highlights use of workflow mapping to transition between current and future state, while simultaneously visualizing process measures needed to ensure success of the DNP project Includes more examples to provide practical application exercises for students Key Features: Disseminates robust strategies for using available data from everyday practice to support trustworthy evaluation of outcomes Uses multiple tools to meet data management objectives [SPSS, Excel®, Tableau] Presents case studies to illustrate multiple techniques and methods throughout chapters Includes specific examples of the application and utility of these techniques using software that is familiar to graduate nursing students Offers real world examples of completed DNP projects Provides Instructor’s Manual, PowerPoint slides, data sets in SPSS and Excel, and forms for completion of data management and evaluation plan |
| deep dive data analysis: Behavioral Data Analysis with R and Python Florent Buisson, 2021-06-15 Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis. Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately. Understand the specifics of behavioral data Explore the differences between measurement and prediction Learn how to clean and prepare behavioral data Design and analyze experiments to drive optimal business decisions Use behavioral data to understand and measure cause and effect Segment customers in a transparent and insightful way |
| deep dive data analysis: Deep Dive Rich Horwath, 2009-08 Get competitive by learning to think strategically.The inability to set good strategy can sink a company¿and a leader¿s career. A recent Wall Street Journal study revealed that the most sought-after executive skill is strategic thinking, but only three out of ten managers have this skill set.Horwath explains the three keys to strategic thinking, breaks them down into simple, attainable skills, and gives you practical tools to apply them every day, providing managers with a clear path to mastery of the three disciplines: 1. Acumen¿generate critical insights through a step-by-step evaluation of your business and its environment2. Allocation¿focus your limited resources through strategic trade-offs 3. Action¿implement a system to guarantee effective execution of strategy at all levels of your organization Based on new research with senior executives from 150 companies and the author¿s experience as a thought-leading strategist, Deep Dive is the first book to focus on the most important level of strategy¿you. Armed with this knowledge and dozens of effective tools, you can become a truly strategic leader for your organization.--Rich Horwath is the president of the Strategic Thinking Institute, a former chief strategy officer, and professor of strategy at the Lake Forest Graduate School of Management. As a thought-leading strategist, he has worked with such giants as Adidas, Amgen, and Pfizer. He is the author of four books and more than fifty articles on strategic thinking and has been profiled in business publications around the world, including Investor¿s Business Daily. |
| deep dive data analysis: Data Analytics Made Easy Andrea De Mauro, Francesco Marzoni, Andrew J. Walter, 2021-08-30 Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboards Key FeaturesEnhance your presentation skills by implementing engaging data storytelling and visualization techniquesLearn the basics of machine learning and easily apply machine learning models to your dataImprove productivity by automating your data processesBook Description Data Analytics Made Easy is an accessible beginner's guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don't worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You'll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You'll not only be able to understand data scientists' machine learning models; you'll be able to challenge them and build your own. Creating interactive dashboards – Follow the book's simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results. What you will learnUnderstand the potential of data and its impact on your businessImport, clean, transform, combine data feeds, and automate your processesInfluence business decisions by learning to create engaging presentationsBuild real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and moreCreate professional-looking and business-centric visuals and dashboardsOpen the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning modelsWho this book is for This book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers. No previous math, statistics, or computer science knowledge is required. |
| deep dive data analysis: Fraud and Fraud Detection, + Website Sunder Gee, 2014-12-03 Detect fraud faster—no matter how well hidden—with IDEA automation Fraud and Fraud Detection takes an advanced approach to fraud management, providing step-by-step guidance on automating detection and forensics using CaseWare's IDEA software. The book begins by reviewing the major types of fraud, then details the specific computerized tests that can detect them. Readers will learn to use complex data analysis techniques, including automation scripts, allowing easier and more sensitive detection of anomalies that require further review. The companion website provides access to a demo version of IDEA, along with sample scripts that allow readers to immediately test the procedures from the book. Business systems' electronic databases have grown tremendously with the rise of big data, and will continue to increase at significant rates. Fraudulent transactions are easily hidden in these enormous datasets, but Fraud and Fraud Detection helps readers gain the data analytics skills that can bring these anomalies to light. Step-by-step instruction and practical advice provide the specific abilities that will enhance the audit and investigation process. Readers will learn to: Understand the different areas of fraud and their specific detection methods Identify anomalies and risk areas using computerized techniques Develop a step-by-step plan for detecting fraud through data analytics Utilize IDEA software to automate detection and identification procedures The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for even experienced examiners. With datasets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification. |
| deep dive data analysis: Think Like a Data Scientist Brian Godsey, 2017-03-09 Summary Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Table of Contents PART 1 - PREPARING AND GATHERING DATA AND KNOWLEDGE Philosophies of data science Setting goals by asking good questions Data all around us: the virtual wilderness Data wrangling: from capture to domestication Data assessment: poking and prodding PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS Developing a plan Statistics and modeling: concepts and foundations Software: statistics in action Supplementary software: bigger, faster, more efficient Plan execution: putting it all together PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP Delivering a product After product delivery: problems and revisions Wrapping up: putting the project away |
| deep dive data analysis: The Art and Science of Technical Analysis Adam Grimes, 2012-05-31 A breakthrough trading book that provides powerful insights on profitable technical patterns and strategies The Art and Science of Technical Analysis is a groundbreaking work that bridges the gaps between the academic view of markets, technical analysis, and profitable trading. The book explores why randomness prevails in markets most, but not all, of the time and how technical analysis can be used to capture statistically validated patterns in certain types of market conditions. The belief of the book is that buying and selling pressure causes patterns in prices, but that these technical patterns are only effective in the presence of true buying/selling imbalance. The Art and Science of Technical Analysis is supported by extensive statistical analysis of the markets, which will debunk some tools and patterns such as Fibonacci analysis, and endorse other tools and trade setups. In addition, this reliable resource discusses trader psychology and trader learning curves based on the author's extensive experience as a trader and trainer of traders. Offers serious traders a way to think about market problems, understand their own performance, and help find a more productive path forward Includes extensive research to validate specific money-making patterns and strategies Written by an experienced market practitioner who has trained and worked with many top traders Filled with in-depth insights and practical advice, The Art and Science of Technical Analysis will give you a realistic sense of how markets behave, when and how technical analysis works, and what it really takes to trade successfully. |
| deep dive data analysis: Techniques and Environments for Big Data Analysis B. S.P. Mishra, Satchidananda Dehuri, Euiwhan Kim, Gi-Name Wang, 2016-02-05 This volume is aiming at a wide range of readers and researchers in the area of Big Data by presenting the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it. The book includes 10 distinct chapters providing a concise introduction to Big Data Analysis and recent Techniques and Environments for Big Data Analysis. It gives insight into how the expensive fitness evaluation of evolutionary learning can play a vital role in big data analysis by adopting Parallel, Grid, and Cloud computing environments. |
| deep dive data analysis: 14 GRASPED Personas in Web Analytics Steven Brough, 2024-02-21 A comprehensive exploration into the roles pivotal to web analytics in internet marketing, illuminating the synergy between data interpretation and strategic application for optimizing user experiences and enhancing digital strategies. Unique Selling Point (USP) Spotlights the multifaceted expertise within web analytics, from data scientists to SEO specialists, emphasizing the collaborative approach to transforming data into actionable insights for informed decision-making in digital marketing. Introduction Initiates the journey into web analytics, underscoring its importance in deciphering user behavior and driving data-informed strategies in the digital marketing realm. List of Personas Web Analyst Digital Marketing Manager SEO Specialist UX/UI Designer Conversion Rate Optimization Specialist Data Scientist/Big Data Expert These personas encapsulate the essence of web analytics in internet marketing, showcasing the diverse skills required to leverage data for strategic advantage. |
| deep dive data analysis: Handbook of Statistical Analysis and Data Mining Applications Ken Yale, Robert Nisbet, Gary D. Miner, 2017-11-09 Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications |
| deep dive data analysis: Data Analytics and Machine Learning Pushpa Singh, |
| deep dive data analysis: Handbook of Statistical Analysis Robert Nisbet, Gary D. Miner, Keith McCormick, 2024-09-16 Handbook of Statistical Analysis: AI and ML Applications, third edition, is a comprehensive introduction to all stages of data analysis, data preparation, model building, and model evaluation. This valuable resource is useful to students and professionals across a variety of fields and settings: business analysts, scientists, engineers, and researchers in academia and industry. General descriptions of algorithms together with case studies help readers understand technical and business problems, weigh the strengths and weaknesses of modern data analysis algorithms, and employ the right analytical methods for practical application. This resource is an ideal guide for users who want to address massive and complex datasets with many standard analytical approaches and be able to evaluate analyses and solutions objectively. It includes clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques; offers accessible tutorials; and discusses their application to real-world problems. - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data analytics to build successful predictive analytic solutions - Provides in-depth descriptions and directions for performing many data preparation operations necessary to generate data sets in the proper form and format for submission to modeling algorithms - Features clear, intuitive explanations of standard analytical tools and techniques and their practical applications - Provides a number of case studies to guide practitioners in the design of analytical applications to solve real-world problems in their data domain - Offers valuable tutorials on the book webpage with step-by-step instructions on how to use suggested tools to build models - Provides predictive insights into the rapidly expanding Intelligence Age as it takes over from the Information Age, enabling readers to easily transition the book's content into the tools of the future |
| deep dive data analysis: Ultimate Enterprise Data Analysis and Forecasting using Python Shanthababu Pandian, 2023-12-28 Practical Approaches to Time Series Analysis and Forecasting using Python for Informed Decision-Making KEY FEATURES ● Comprehensive Resource for Python-Based Time Series Analysis and Forecasting. ● Delve into real-world applications with industry-specific case studies. ● Extract valuable insights by solving time series challenges across various sectors. ● Understand the significance of Azure Time Series Insights and AWS Forecast components. ● Practical insights into leveraging cloud platforms for efficient time series forecasting. DESCRIPTION Embark on a transformative journey through the intricacies of time series analysis and forecasting with this comprehensive handbook. Beginning with the essential packages for data science and machine learning projects you will delve into Python's prowess for efficient time series data analysis, exploring the core components and real-world applications across various industries through compelling use-case studies. From understanding classical models like AR, MA, ARMA, and ARIMA to exploring advanced techniques such as exponential smoothing and ETS methods, this guide ensures a deep understanding of the subject. It will help you navigate the complexities of vector autoregression (VAR, VMA, VARMA) and elevate your skills with a deep dive into deep learning techniques for time series analysis. By the end of this book, you will be able to harness the capabilities of Azure Time Series Insights and explore the cutting-edge AWS Forecast components, unlocking the cloud's power for advanced and scalable time series forecasting. WHAT WILL YOU LEARN ● Explore Time Series Data Analysis and Forecasting, covering components and significance. ● Gain a practical understanding through hands-on examples and real-world case studies. ● Master Time Series Models (AR, MA, ARMA, ARIMA, VAR, VMA, VARMA) with executable samples. ● Delve into Deep Learning for Time Series Analysis, demystified with classical examples. ● Actively engage with Azure Time Series Insights and AWS Forecast components for a contemporary perspective. WHO IS THIS BOOK FOR? This book caters to beginners, intermediates, and practitioners in data-related fields such as Data Analysts, Data Scientists, and Machine Learning Engineers, as well as those venturing into Time Series Analysis and Forecasting. It assumes readers have a foundational understanding of programming languages (C, C++, Python), data structures, statistics, and visualization concepts. With a focus on specific projects, it also functions as a quick reference for advanced users. TABLE OF CONTENTS 1. Introduction to Python and its key packages for DS and ML Projects 2. Python for Time Series Data Analysis 3. Time Series Analysis and its Components 4. Time Series Analysis and Forecasting Opportunities in Various Industries 5. Exploring various aspects of Time Series Analysis and Forecasting 6. Exploring Time Series Models - AR, MA, ARMA, and ARIMA 7. Understanding Exponential Smoothing and ETS Methods in TSA 8. Exploring Vector Autoregression and its Subsets (VAR, VMA, and VARMA) 9. Deep Learning for Time Series Analysis and Forecasting 10. Azure Time Series Insights 11. AWSForecast Index |
| deep dive data analysis: Summary of the Proceedings of the Workshop at Airlie, Virginia on Perspectives for Ocean Exploration and Survey Systems 1975-1985 , 1972 |
| deep dive data analysis: Generative Artificial Intelligence and Ethics: Standards, Guidelines, and Best Practices Gaur, Loveleen, 2024-10-10 The rapid advancement of generative artificial intelligence (AI) has brought about significant ethical challenges. As machines become more adept at creating human-like content, concerns about misuse, bias, privacy, and accountability have emerged. Without clear guidelines and regulations, there is a risk of unethical use, such as creating deepfake videos or disseminating misinformation, which could have severe societal consequences. Additionally, questions about intellectual property rights and the ownership of AI-generated creations still need to be solved, further complicating the ethical landscape. The book, Generative Artificial Intelligence and Ethics: Standards, Guidelines, and Best Practices, comprehensively solves these ethical challenges. By providing insights into the historical development and key milestones of Generative AI, the book lays a foundation for understanding its complex ethical implications. It examines existing ethical frameworks and proposes new ones tailored to AI's unique characteristics, helping readers apply traditional ethics to AI development and deployment. |
| deep dive data analysis: Mastering Marketing Data Science Iain Brown, 2024-04-29 Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable. |
| deep dive data analysis: Unstructured Data Analytics Jean Paul Isson, 2018-03-02 Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis. |
| deep dive data analysis: Data Analytics and Digital Transformation Erik Beulen, Marla A. Dans, 2023-12-01 Understanding the significance of data analytics is paramount for digital transformation but in many organizations they are separate units without fully aligned goals. As organizations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role in their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This not only creates new opportunities but also requires well-considered and continuously assessed decision-making as competitiveness is at stake. This book details approaches, concepts, and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organizations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid, and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organizational studies, and strategic management. It is also relevant for policy makers, regulators, and executives of private and public organizations looking to implement successful transformation policies. |
| deep dive data analysis: Research Methods in Politics and International Relations Christopher Lamont, Mieczyslaw P. Boduszynski, 2020-02-24 This is the perfect guide to conducting a research project in politics and international relations. From formulating a research question and conducting a literature review to writing up and disseminating your work, this book guides you through the research process from start to finish. The book: - Is focused specifically on research methods in politics and IR - Introduces the central methodological debates in a clear, accessible style - Considers the key questions of ethics and research design - Covers both qualitative and quantitative approaches - Shows you how to choose and implement the right methods in your own project The book features two example research projects – one from politics, one from IR – that appear periodically throughout the book to show you how real research looks at each stage of the process. Packed full of engaging examples, it provides you with all you need to know to coordinate your own research project in politics and international relations. |
| deep dive data analysis: Common Data Sense for Professionals Rajesh Jugulum, 2022-01-27 Data is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections of data to determine strategy and marketing. Data scientists take data, analyze it, and create models to help solve problems. You may have heard of companies having data management teams or chief information officers (CIOs) or chief data officers (CDOs), etc. They are all people who work with data, but their function is more related to vetting data and preparing it for use by data scientists. The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Although advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic analytical software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data-related challenges. The goal of this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study, illustrating how the various topics discussed can be applied. Essentially, this book helps traditional businesspeople solve data-related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity. |
| deep dive data analysis: Artificial Intelligence in Accounting Othmar M. Lehner, Carina Knoll, 2022-08-05 Artificial intelligence (AI) and Big Data based applications in accounting and auditing have become pervasive in recent years. However, research on the societal implications of the widespread and partly unregulated use of AI and Big Data in several industries remains scarce despite salient and competing utopian and dystopian narratives. This book focuses on the transformation of accounting and auditing based on AI and Big Data. It not only provides a thorough and critical overview of the status-quo and the reports surrounding these technologies, but it also presents a future outlook on the ethical and normative implications concerning opportunities, risks, and limits. The book discusses topics such as future, human-machine collaboration, cybernetic approaches to decision-making, and ethical guidelines for good corporate governance of AI-based algorithms and Big Data in accounting and auditing. It clarifies the issues surrounding the digital transformation in this arena, delineates its boundaries, and highlights the essential issues and debates within and concerning this rapidly developing field. The authors develop a range of analytic approaches to the subject, both appreciative and sceptical, and synthesise new theoretical constructs that make better sense of human-machine collaborations in accounting and auditing. This book offers academics a variety of new research and theory building on digital accounting and auditing from and for accounting and auditing scholars, economists, organisations, and management academics and political and philosophical thinkers. Also, as a landmark work in a new area of current policy interest, it will engage regulators and policy makers, reflective practitioners, and media commentators through its authoritative contributions, editorial framing and discussion, and sector studies and cases. |
| deep dive data analysis: Encyclopedia of Organizational Knowledge, Administration, and Technology Khosrow-Pour D.B.A., Mehdi, 2020-09-29 For any organization to be successful, it must operate in such a manner that knowledge and information, human resources, and technology are continually taken into consideration and managed effectively. Business concepts are always present regardless of the field or industry – in education, government, healthcare, not-for-profit, engineering, hospitality/tourism, among others. Maintaining organizational awareness and a strategic frame of mind is critical to meeting goals, gaining competitive advantage, and ultimately ensuring sustainability. The Encyclopedia of Organizational Knowledge, Administration, and Technology is an inaugural five-volume publication that offers 193 completely new and previously unpublished articles authored by leading experts on the latest concepts, issues, challenges, innovations, and opportunities covering all aspects of modern organizations. Moreover, it is comprised of content that highlights major breakthroughs, discoveries, and authoritative research results as they pertain to all aspects of organizational growth and development including methodologies that can help companies thrive and analytical tools that assess an organization’s internal health and performance. Insights are offered in key topics such as organizational structure, strategic leadership, information technology management, and business analytics, among others. The knowledge compiled in this publication is designed for entrepreneurs, managers, executives, investors, economic analysts, computer engineers, software programmers, human resource departments, and other industry professionals seeking to understand the latest tools to emerge from this field and who are looking to incorporate them in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to business, management science, organizational development, entrepreneurship, sociology, corporate psychology, computer science, and information technology will benefit from the research compiled within this publication. |
| deep dive data analysis: Science of Diving Bruce Wienke, 2015-06-01 While the effects of pressure change are readily quantified in physics, chemistry, and engineering applications, the physiology, medicine, and biology of pressure changes in living systems are much more complicated. This complex science translated to technical diving is discussed in a five-part series, with each topic self-contained and strategical |
| deep dive data analysis: Advancing into Analytics George Mount, 2021-01-22 Data analytics may seem daunting, but if you're an experienced Excel user, you have a unique head start. With this hands-on guide, intermediate Excel users will gain a solid understanding of analytics and the data stack. By the time you complete this book, you'll be able to conduct exploratory data analysis and hypothesis testing using a programming language. Exploring and testing relationships are core to analytics. By using the tools and frameworks in this book, you'll be well positioned to continue learning more advanced data analysis techniques. Author George Mount, founder and CEO of Stringfest Analytics, demonstrates key statistical concepts with spreadsheets, then pivots your existing knowledge about data manipulation into R and Python programming. This practical book guides you through: Foundations of analytics in Excel: Use Excel to test relationships between variables and build compelling demonstrations of important concepts in statistics and analytics From Excel to R: Cleanly transfer what you've learned about working with data from Excel to R From Excel to Python: Learn how to pivot your Excel data chops into Python and conduct a complete data analysis |
| deep dive data analysis: Ecology and Behaviour of Free-Ranging Animals Studied by Advanced Data-Logging and Tracking Techniques Thomas Wassmer, Dennis Murray, Stan Boutin, Andreas Fahlman, Frants Havmand Jensen, 2020-06-15 |
D7.1 Common standardised format for deep dives - TRIGGER …
Each deep dive will cover four topic areas with several research questions related to four main domains: Governance, Actorness, Effectiveness and Challenges/Opportunities.
What is a Deep Dive?.indd - Good Governance
A deep dive analysis has been recorded as ‘a strategy of immersing a team rapidly into a situation, to provide solutions or create ideas. Deep dive analysis will normally focus in areas …
FROM DATA TO DECISIONS: THE POWER OF DEEP DIVE …
By blending publicly and commercially available quantitative data with qualitative assessment and scoring methods into our overall risk management process, this enables both rapid initial …
Bringing risk discussions to life through `deep dives'
So what is a "deep dive"? It is a comprehensive review of an individual risk that provides detailed information which cannot be reasonably captured on a risk register.
Deep Dive into Deep Learning
Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Deep learning uses deep neural network with several …
Healthcare’s Digital Backbone: a Deep Dive into Data …
Key aspects such as interoperability, governance, and visualization enable seamless data flow, ensuring that stakeholders, from clinicians to researchers, can derive meaningful insights.
Deep-dive Analysis - Semantic Scholar
Deep-dive Analysis of the Data Analytics Workload in CloudSuite Ahmad Yasin - Intel Corporation & University of Haifa Avi Mendelson - Technion, Yosi Ben-Asher - University of Haifa
Deep-dive Analysis of the Data Analytics Workload in …
Through a deep-dive analysis, our work identified the specific application constructs that do benefit from more cache, and suggested possible application, JVM and microarchitecture...
DEEP DIVE
DEEP DIVE DATA ANALYSIS TYPES The following types of analyses represent useful tools in examining current practice, understanding changes and trends in practice, breaking down data …
Exploratory Data Analysis Module III: Deep Dive
Exploratory Data Analysis Module III: Deep Dive Dr. Mark Williamson DaCCoTA University of North Dakota
DEEP DIVE #2 Data Insights - SEforALL
Build on existing tools and establish a central dynamic and standardized dataset that captures aforementioned data content and quality gaps including PHC functionality, electrification …
DATA STRATEGY AND INSIGHT - Charity Fundraising Services …
Recommendations 5 core recommendations came from the deep dive data analysis. 1 Continue to grow active player volumes through warm conversion and cold acquisition 2 Retain active …
DeepDive: A Data Management System for Automatic …
DeepDive is a data management system that we built to study this problem; its ultimate goal is to allow scientists to build a KBC system by declaratively specifying domain knowledge without …
Spatial Data Mining: A Deep Dive into Cluster Analysis - Esri
Spatial Data Mining: A Deep Dive into Cluster Analysis Spatial Data Mining: A Deep Dive into Cluster Analysis s t u v w Techni< al WOI shop esri ster Anal sis Dive into Clu! thining: A Dee …
Data Quality Management - EY
By implementing these three pillars, organizations can effectively address data quality issues and ensure their data is accurate, reliable, and useful for decision making. 1. Define the …
The DIVE Approach: Using Case-Ordered Meta-Matrices and …
aking a deep dive into multiple case data through four stages of analysis: describe, integrate, visualize, and expand. Specifically, this approach involves cycli-cally analyzing case-ordered...
Reporting matters Digital deep dive analysis
machine-automated analysis and forecasting. Companies can apply AI to process environmental, social and governance (ESG)-related big data in an automated way – identifying accurate pat …
Deep Dive Data Analysis
Master Data Analysis with Python Teddy Petrou,2024-04-09 This comprehensive but easy-to-follow deep dive into data analysis and visualization in the Python programming language is …
Microsoft PowerPoint - Measurement_Data.pptx [Read-Only]
Solve problems involving measurement and unit conversations from a larger unit to a smaller unit, know relative sizes of metric as well as the U.S. Customary units, solve word problems …
PowerPoint Presentation
To ensure we get on track, all end-use sectors (industry, transport and building) need to become more energy efficient. As the economic structures are quite diverse from one country to …
D7.1 Common standardised format for deep dives - TRIGGER …
Each deep dive will cover four topic areas with several research questions related to four main domains: Governance, Actorness, Effectiveness and Challenges/Opportunities.
What is a Deep Dive?.indd - Good Governance
A deep dive analysis has been recorded as ‘a strategy of immersing a team rapidly into a situation, to provide solutions or create ideas. Deep dive analysis will normally focus in areas …
FROM DATA TO DECISIONS: THE POWER OF DEEP DIVE …
By blending publicly and commercially available quantitative data with qualitative assessment and scoring methods into our overall risk management process, this enables both rapid initial …
Bringing risk discussions to life through `deep dives'
So what is a "deep dive"? It is a comprehensive review of an individual risk that provides detailed information which cannot be reasonably captured on a risk register.
Deep Dive into Deep Learning
Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Deep learning uses deep neural network with several …
Healthcare’s Digital Backbone: a Deep Dive into Data …
Key aspects such as interoperability, governance, and visualization enable seamless data flow, ensuring that stakeholders, from clinicians to researchers, can derive meaningful insights.
Deep-dive Analysis - Semantic Scholar
Deep-dive Analysis of the Data Analytics Workload in CloudSuite Ahmad Yasin - Intel Corporation & University of Haifa Avi Mendelson - Technion, Yosi Ben-Asher - University of Haifa
Deep-dive Analysis of the Data Analytics Workload in …
Through a deep-dive analysis, our work identified the specific application constructs that do benefit from more cache, and suggested possible application, JVM and microarchitecture...
DEEP DIVE
DEEP DIVE DATA ANALYSIS TYPES The following types of analyses represent useful tools in examining current practice, understanding changes and trends in practice, breaking down data …
Exploratory Data Analysis Module III: Deep Dive
Exploratory Data Analysis Module III: Deep Dive Dr. Mark Williamson DaCCoTA University of North Dakota
DEEP DIVE #2 Data Insights - SEforALL
Build on existing tools and establish a central dynamic and standardized dataset that captures aforementioned data content and quality gaps including PHC functionality, electrification …
DATA STRATEGY AND INSIGHT - Charity Fundraising Services …
Recommendations 5 core recommendations came from the deep dive data analysis. 1 Continue to grow active player volumes through warm conversion and cold acquisition 2 Retain active …
DeepDive: A Data Management System for Automatic …
DeepDive is a data management system that we built to study this problem; its ultimate goal is to allow scientists to build a KBC system by declaratively specifying domain knowledge without …
Spatial Data Mining: A Deep Dive into Cluster Analysis - Esri
Spatial Data Mining: A Deep Dive into Cluster Analysis Spatial Data Mining: A Deep Dive into Cluster Analysis s t u v w Techni< al WOI shop esri ster Anal sis Dive into Clu! thining: A Dee …
Data Quality Management - EY
By implementing these three pillars, organizations can effectively address data quality issues and ensure their data is accurate, reliable, and useful for decision making. 1. Define the …
The DIVE Approach: Using Case-Ordered Meta-Matrices and …
aking a deep dive into multiple case data through four stages of analysis: describe, integrate, visualize, and expand. Specifically, this approach involves cycli-cally analyzing case-ordered...
Reporting matters Digital deep dive analysis
machine-automated analysis and forecasting. Companies can apply AI to process environmental, social and governance (ESG)-related big data in an automated way – identifying accurate pat …
Deep Dive Data Analysis
Master Data Analysis with Python Teddy Petrou,2024-04-09 This comprehensive but easy-to-follow deep dive into data analysis and visualization in the Python programming language is …
Microsoft PowerPoint - Measurement_Data.pptx [Read-Only]
Solve problems involving measurement and unit conversations from a larger unit to a smaller unit, know relative sizes of metric as well as the U.S. Customary units, solve word problems …
PowerPoint Presentation
To ensure we get on track, all end-use sectors (industry, transport and building) need to become more energy efficient. As the economic structures are quite diverse from one country to …
