Advertisement
The Power of Ad Hoc Data Analysis: Unlocking Insights in a Data-Driven World
By Dr. Evelyn Reed, PhD in Data Science and Chief Data Scientist at DataWise Solutions
Published by Analytics Insights, the leading publication for data science professionals.
Edited by Michael Davis, Senior Editor at Analytics Insights with 15 years of experience in technical journalism.
Summary: This article explores the growing importance of ad hoc data analysis in various industries, highlighting its benefits, challenges, and implications for future business strategies. We examine the techniques used, discuss the necessary skills, and consider the ethical considerations associated with this powerful form of data exploration.
Introduction: In today's data-saturated world, the ability to quickly analyze information and extract actionable insights is paramount. This is where ad hoc data analysis steps in. Unlike pre-planned, scheduled analyses, ad hoc data analysis is characterized by its spontaneous nature, driven by immediate needs and unexpected questions. This flexible approach allows businesses to react swiftly to changing market conditions, uncover hidden trends, and make informed decisions in real-time. This article delves into the multifaceted world of ad hoc data analysis, exploring its impact across various sectors and discussing the skills and tools needed for success.
H1: What is Ad Hoc Data Analysis?
Ad hoc data analysis refers to the process of analyzing data without a pre-defined plan or hypothesis. It's a reactive approach, often triggered by a specific event, question, or unexpected trend. This type of analysis is characterized by its flexibility and agility, allowing analysts to explore data freely and uncover insights that might be missed with a more structured approach. The process often involves querying databases, using data visualization tools, and applying statistical methods to understand the data's underlying patterns and relationships.
H2: The Benefits of Ad Hoc Data Analysis
The advantages of ad hoc data analysis are numerous:
Improved Decision-Making: By providing quick answers to urgent questions, ad hoc data analysis enables rapid and informed decision-making, giving businesses a competitive edge.
Faster Problem Solving: When unexpected problems arise, ad hoc data analysis allows for a swift identification of root causes and the implementation of solutions.
Enhanced Business Agility: Businesses can adapt quickly to changing market conditions and customer needs through the flexible nature of ad hoc data analysis.
Uncovering Hidden Opportunities: The exploratory nature of this analysis allows for the identification of unforeseen trends and opportunities that might otherwise be overlooked.
Improved Customer Understanding: Ad hoc data analysis can help businesses understand their customers better by quickly analyzing customer feedback and behavior data.
H3: Techniques Used in Ad Hoc Data Analysis
Several techniques are employed in ad hoc data analysis, including:
Data Querying: Using SQL or other query languages to extract specific data sets from databases.
Data Visualization: Employing tools like Tableau, Power BI, or even simple spreadsheets to visually represent data and identify patterns.
Statistical Analysis: Applying basic statistical methods, such as descriptive statistics or correlation analysis, to summarize and interpret data.
Data Mining: Utilizing techniques to uncover hidden patterns and relationships within large datasets.
Machine Learning: In some cases, simple machine learning models can be applied to generate quick predictions or classifications.
H4: Challenges of Ad Hoc Data Analysis
While immensely powerful, ad hoc data analysis also faces challenges:
Data Quality: The accuracy and reliability of results depend heavily on the quality of the underlying data.
Data Security and Privacy: Care must be taken to ensure that sensitive data is handled responsibly and ethically.
Skill Requirements: Performing effective ad hoc data analysis requires a strong understanding of data analysis techniques and tools.
Time Constraints: While quick, the analysis needs to be efficient to remain valuable in fast-paced environments.
Bias and Interpretation: Subjectivity can influence the interpretation of results, leading to flawed conclusions.
H5: The Future of Ad Hoc Data Analysis and its Industry Implications
The future of ad hoc data analysis is bright. Advancements in data visualization, artificial intelligence, and machine learning will further enhance its capabilities. We can expect to see more sophisticated tools that automate parts of the process, making it even more accessible to a wider range of users. The implications for various industries are profound:
Healthcare: Faster diagnosis and treatment through rapid analysis of patient data.
Finance: Immediate risk assessment and fraud detection.
Retail: Real-time optimization of pricing and inventory management.
Marketing: Targeted advertising and personalized customer experiences.
Conclusion:
Ad hoc data analysis is an indispensable tool for businesses operating in today's data-rich environment. Its ability to provide timely insights, facilitate agile decision-making, and uncover hidden opportunities makes it crucial for competitiveness and success. By embracing this powerful technique and addressing its associated challenges, organizations can unlock the full potential of their data and drive significant growth. The future of business hinges on the effective utilization of data, and ad hoc data analysis will undoubtedly play a pivotal role in shaping that future.
FAQs:
1. What are the essential software tools for ad hoc data analysis? Popular options include SQL query tools, data visualization software (Tableau, Power BI), spreadsheet software (Excel), and statistical packages (R, Python).
2. How can I improve my ad hoc data analysis skills? Take online courses, attend workshops, and practice regularly with real-world datasets.
3. What are the ethical considerations in ad hoc data analysis? Ensure data privacy, avoid bias in analysis, and be transparent about methodologies and results.
4. Can ad hoc data analysis be used for predictive modeling? Yes, but it’s typically used for simpler models or initial exploratory analysis before more complex predictive models are developed.
5. What’s the difference between ad hoc and scheduled data analysis? Ad hoc is reactive, spontaneous, and driven by immediate needs; scheduled analysis is pre-planned and routine.
6. How can I integrate ad hoc analysis into my existing business processes? Start by identifying key questions requiring quick answers, then select appropriate tools and train relevant personnel.
7. What are the limitations of ad hoc data analysis? It can be less rigorous than formal analytical methods and susceptible to biases if not carefully managed.
8. Is ad hoc data analysis suitable for all types of data? Generally yes, but the specific techniques used will depend on the data’s structure and type (structured, unstructured, etc.).
9. How can I ensure the quality of my ad hoc data analysis? Establish clear processes for data cleaning, validation, and verification.
Related Articles:
1. "Ad Hoc Querying with SQL: A Practical Guide": A step-by-step guide on using SQL for efficient data querying in ad hoc analysis.
2. "Data Visualization for Ad Hoc Analysis: Techniques and Best Practices": Explores various techniques for creating effective data visualizations for quick insights.
3. "The Role of Data Storytelling in Ad Hoc Data Analysis": Discusses how to effectively communicate findings from ad hoc analysis using compelling narratives.
4. "Overcoming Challenges in Ad Hoc Data Analysis: A Case Study": Presents real-world challenges encountered during ad hoc analysis and solutions for overcoming them.
5. "Ethical Considerations in Ad Hoc Data Analysis: A Framework for Responsible Practices": Delves deeper into the ethical implications and provides a framework for ethical conduct.
6. "The Future of Ad Hoc Data Analysis: Trends and Predictions": Explores emerging technologies and their influence on the future of ad hoc analysis.
7. "Integrating Ad Hoc Analysis into Business Intelligence Systems": Examines strategies for seamless integration of ad hoc analysis within existing BI infrastructure.
8. "Ad Hoc Analysis in Healthcare: Improving Patient Outcomes": Focuses on the specific applications and benefits of ad hoc analysis within the healthcare sector.
9. "Ad Hoc Analysis and Machine Learning: A Powerful Combination": Explores how simple machine learning techniques can augment ad hoc data analysis capabilities.
| ad hoc data analysis: Cloud Computing Enabled Big-Data Analytics in Wireless Ad-hoc Networks Sanjoy Das, Ram Shringar Rao, Indrani Das, Vishal Jain, Nanhay Singh, 2022-03-20 This book discusses intelligent computing through the Internet of Things (IoT) and Big-Data in vehicular environments in a single volume. It covers important topics, such as topology-based routing protocols, heterogeneous wireless networks, security risks, software-defined vehicular ad-hoc networks, vehicular delay tolerant networks, and energy harvesting for WSNs using rectenna. FEATURES Covers applications of IoT in Vehicular Ad-hoc Networks (VANETs) Discusses use of machine learning and other computing techniques for enhancing performance of networks Explains game theory-based vertical handoffs in heterogeneous wireless networks Examines monitoring and surveillance of vehicles through the vehicular sensor network Investigates theoretical approaches on software-defined VANET The book is aimed at graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science, and engineering. |
| ad hoc data analysis: Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks Rao, Ram Shringar, Singh, Nanhay, Kaiwartya, Omprakash, Das, Sanjoy, 2020-09-11 Vehicular traffic congestion and accidents remain universal issues in today’s world. Due to the continued growth in the use of vehicles, optimizing traffic management operations is an immense challenge. To reduce the number of traffic accidents, improve the performance of transportation systems, enhance road safety, and protect the environment, vehicular ad-hoc networks have been introduced. Current developments in wireless communication, computing paradigms, big data, and cloud computing enable the enhancement of these networks, equipped with wireless communication capabilities and high-performance processing tools. Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks is a pivotal reference source that provides vital research on cloud and data analytic applications in intelligent transportation systems. While highlighting topics such as location routing, accident detection, and data warehousing, this publication addresses future challenges in vehicular ad-hoc networks and presents viable solutions. This book is ideally designed for researchers, computer scientists, engineers, automobile industry professionals, IT practitioners, academicians, and students seeking current research on cloud computing models in vehicular networks. |
| ad hoc data analysis: Text Analysis Pipelines Henning Wachsmuth, 2015-12-02 This monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. Based on state-of-the-art techniques from machine learning and other areas of artificial intelligence, novel pipeline construction and execution algorithms are developed and implemented in prototypical software. Formal analyses of the algorithms and extensive empirical experiments underline that the proposed approach represents an essential step towards the ad-hoc use of text mining in web search and big data analytics. Both web search and big data analytics aim to fulfill peoples’ needs for information in an adhoc manner. The information sought for is often hidden in large amounts of natural language text. Instead of simply returning links to potentially relevant texts, leading search and analytics engines have started to directly mine relevant information from the texts. To this end, they execute text analysis pipelines that may consist of several complex information-extraction and text-classification stages. Due to practical requirements of efficiency and robustness, however, the use of text mining has so far been limited to anticipated information needs that can be fulfilled with rather simple, manually constructed pipelines. |
| ad hoc data analysis: Handbook of Data Analysis Melissa A Hardy, Alan Bryman, 2009-06-17 A fundamental book for social researchers. It provides a first-class, reliable guide to the basic issues in data analysis. Scholars and students can turn to it for teaching and applied needs with confidence. |
| ad hoc data analysis: Data Analysis Peter J. Huber, 2012-01-09 This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy – when to use which technique – are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics. |
| ad hoc data analysis: Business Intelligence Guidebook Rick Sherman, 2014-11-04 Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources. |
| ad hoc data analysis: Data Analytics Initiatives Ondřej Bothe, Ondřej Kubera, David Bednář, Martin Potančok, Ota Novotný, 2022-04-20 The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges. |
| ad hoc data analysis: Visual Intelligence Mark Stacey, Joe Salvatore, Adam Jorgensen, 2013-04-10 Go beyond design concepts and learn to build state-of-the-art visualizations The visualization experts at Microsoft's Pragmatic Works have created a full-color, step-by-step guide to building specific types of visualizations. The book thoroughly covers the Microsoft toolset for data analysis and visualization, including Excel, and explores best practices for choosing a data visualization design, selecting tools from the Microsoft stack, and building a dynamic data visualization from start to finish. You'll examine different types of visualizations, their strengths and weaknesses, and when to use each one. Data visualization tools unlock the stories within the data, enabling you to present it in a way that is useful for making business decisions This full-color guide introduces data visualization design concepts, then explains the various Microsoft tools used to store and display data Features a detailed discussion of various classes of visualizations, their uses, and the appropriate tools for each Includes practical implementations of various visualizations and best practices for using them Covers out-of-the-box Microsoft tools, custom-developed illustrations and implementations, and code examples Visual Intelligence: Microsoft Tools and Techniques for Visualizing Data arms you with best practices and the knowledge to choose and build dynamic data visualizations. |
| ad hoc data analysis: Real Data Analysis Shlomo S. Sawilowsky, 2007-01-01 The invited authors of this edited volume have been prolific in the arena of Real Data Analysis (RDA) as it applies to the social and behavioral sciences, especially in the disciplines of education and psychology. Combined, this brain trust represents 3,247 articles in refereed journals, 127 books published, US $45.3 Million in extramural research funding, 34 teaching and 92 research awards, serve(d) as Editor/Assistant Editor/Editorial Board Member for 95 peer reviewed journals, and provide (d) ad hoc reviews for 362 journals. Their enormous footprint on real data analysis is showcased for professors, researchers, educators, administrators, and graduate students in the second text in the AERA/SIG ES Quantitative Methods series. |
| ad hoc data analysis: Practical DataOps Harvinder Atwal, 2019-12-09 Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production. |
| ad hoc data analysis: Group Privacy Linnet Taylor, Luciano Floridi, Bart van der Sloot, 2016-12-28 The goal of the book is to present the latest research on the new challenges of data technologies. It will offer an overview of the social, ethical and legal problems posed by group profiling, big data and predictive analysis and of the different approaches and methods that can be used to address them. In doing so, it will help the reader to gain a better grasp of the ethical and legal conundrums posed by group profiling. The volume first maps the current and emerging uses of new data technologies and clarifies the promises and dangers of group profiling in real life situations. It then balances this with an analysis of how far the current legal paradigm grants group rights to privacy and data protection, and discusses possible routes to addressing these problems. Finally, an afterword gathers the conclusions reached by the different authors and discuss future perspectives on regulating new data technologies. |
| ad hoc data analysis: Python Data Analytics Fabio Nelli, 2015-08-25 Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Author Fabio Nelli expertly shows the strength of the Python programming language when applied to processing, managing and retrieving information. Inside, you will see how intuitive and flexible it is to discover and communicate meaningful patterns of data using Python scripts, reporting systems, and data export. This book examines how to go about obtaining, processing, storing, managing and analyzing data using the Python programming language. You will use Python and other open source tools to wrangle data and tease out interesting and important trends in that data that will allow you to predict future patterns. Whether you are dealing with sales data, investment data (stocks, bonds, etc.), medical data, web page usage, or any other type of data set, Python can be used to interpret, analyze, and glean information from a pile of numbers and statistics. This book is an invaluable reference with its examples of storing and accessing data in a database; it walks you through the process of report generation; it provides three real world case studies or examples that you can take with you for your everyday analysis needs. |
| ad hoc data analysis: Data Analytics: Principles, Tools, and Practices Gaurav Aroraa, Chitra Lele, Dr. Munish Jindal, 2022-01-24 A Complete Data Analytics Guide for Learners and Professionals. KEY FEATURES ● Learn Big Data, Hadoop Architecture, HBase, Hive and NoSQL Database. ● Dive into Machine Learning, its tools, and applications. ● Coverage of applications of Big Data, Data Analysis, and Business Intelligence. DESCRIPTION These days critical problem solving related to data and data sciences is in demand. Professionals who can solve real data science problems using data science tools are in demand. The book “Data Analytics: Principles, Tools, and Practices” can be considered a handbook or a guide for professionals who want to start their journey in the field of data science. The journey starts with the introduction of DBMS, RDBMS, NoSQL, and DocumentDB. The book introduces the essentials of data science and the modern ecosystem, including the important steps such as data ingestion, data munging, and visualization. The book covers the different types of analysis, different Hadoop ecosystem tools like Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. It also includes the different machine learning techniques that are useful for data analytics and how to visualize data with different graphs and charts. The book discusses useful tools and approaches for data analytics, supported by concrete code examples. After reading this book, you will be motivated to explore real data analytics and make use of the acquired knowledge on databases, BI/DW, data visualization, Big Data tools, and statistical science. WHAT YOU WILL LEARN ● Familiarize yourself with Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. ● Learn to manage data warehousing with real time transaction processing. ● Explore various machine learning techniques that apply to data analytics. ● Learn how to visualize data using a variety of graphs and charts using real-world examples from the industry. ● Acquaint yourself with Big Data tools and statistical techniques for machine learning. WHO THIS BOOK IS FOR IT graduates, data engineers and entry-level professionals who have a basic understanding of the tools and techniques but want to learn more about how they fit into a broader context are encouraged to read this book. TABLE OF CONTENTS 1. Database Management System 2. Online Transaction Processing and Data Warehouse 3. Business Intelligence and its deeper dynamics 4. Introduction to Data Visualization 5. Advanced Data Visualization 6. Introduction to Big Data and Hadoop 7. Application of Big Data Real Use Cases 8. Application of Big Data 9. Introduction to Machine Learning 10. Advanced Concepts to Machine Learning 11. Application of Machine Learning |
| ad hoc data analysis: Handbook of Big Data Technologies Albert Y. Zomaya, Sherif Sakr, 2017-02-25 This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field. |
| ad hoc data analysis: Learning Analytics: Fundaments, Applications, and Trends Alejandro Peña-Ayala, 2017-02-17 This book provides a conceptual and empirical perspective on learning analytics, its goal being to disseminate the core concepts, research, and outcomes of this emergent field. Divided into nine chapters, it offers reviews oriented on selected topics, recent advances, and innovative applications. It presents the broad learning analytics landscape and in-depth studies on higher education, adaptive assessment, teaching and learning. In addition, it discusses valuable approaches to coping with personalization and huge data, as well as conceptual topics and specialized applications that have shaped the current state of the art. By identifying fundamentals, highlighting applications, and pointing out current trends, the book offers an essential overview of learning analytics to enhance learning achievement in diverse educational settings. As such, it represents a valuable resource for researchers, practitioners, and students interested in updating their knowledge and finding inspirations for their future work. |
| ad hoc data analysis: Statistical Analysis of Microbiome Data with R Yinglin Xia, Jun Sun, Ding-Geng Chen, 2018-10-06 This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research. |
| ad hoc data analysis: Big Data Imperatives Soumendra Mohanty, Madhu Jagadeesh, Harsha Srivatsa, 2013-08-23 Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data. |
| ad hoc data analysis: Database Development and Management Lee Chao, 2006-01-13 Today's database professionals must understand how to apply database systems to business processes and how to develop database systems for both business intelligence and Web-based applications. Database Development and Management explains all aspects of database design, access, implementation, application development, and management, as well |
| ad hoc data analysis: Real World Health Care Data Analysis Douglas Faries, Xiang Zhang, Zbigniew Kadziola, Uwe Siebert, Felicitas Kuehne, Robert L Obenchain, Josep Maria Haro, 2020-01-15 Discover best practices for real world data research with SAS code and examples Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient. The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include: propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods methods for comparing two interventions as well as comparisons between three or more interventions algorithms for personalized medicine sensitivity analyses for unmeasured confounding |
| ad hoc data analysis: Microsoft 365 Excel: The Only App That Matters MrExcel's Holy Macro! Books, Mike Girvin, 2024-09-26 Master Microsoft 365 Excel from basics to advanced with practical examples and expert guidance. Perfect for professionals and students aiming to excel in data analysis, financial modeling, and beyond. Key Features Comprehensive coverage from Excel basics to advanced functions Practical examples for real-world application Step-by-step guidance on data analysis and automation. Book DescriptionUnlock the full potential of Microsoft 365 Excel with this extensive guide, crafted for both beginners and seasoned users alike. Begin by uncovering the foundational reasons behind Excel’s creation and its unmatched significance in the business world. Dive deep into the structure of Excel files, worksheets, and key concepts that underscore the application’s versatility. As you progress, master efficient workflows, keyboard shortcuts, and powerful formulas, making Excel an indispensable tool for solving complex problems. Moving forward, the book will guide you through advanced topics, including logical tests, lookup functions, and the latest features like LET and LAMBDA functions. Gain hands-on experience with data analysis, exploring the full capabilities of standard pivot tables, advanced Power Query, and Power BI. Each chapter builds on the last, ensuring that you gain both practical skills and a deep understanding of Excel’s capabilities, preparing you to confidently tackle even the most challenging data tasks. By the end of this guide, you’ll not only be adept at using Excel but also equipped with strategies to apply Excel's advanced features to real-world scenarios—whether you’re interested in financial modeling, big data analysis, or simply enhancing efficiency in your day-to-day tasks.What you will learn Master Excel's interface and shortcuts Build efficient worksheets Apply formulas for problem-solving Leverage data analysis tools Utilize advanced Excel functions Create automated solutions with VBA. Who this book is for The ideal audience for this book includes professionals, data analysts, financial analysts, and students who are familiar with basic Excel functions but want to advance their skills. A basic understanding of Excel is recommended. |
| ad hoc data analysis: Ultimate Statistical Analysis System (SAS) for Data Analytics Vishesh Dhingra, 2024-07-24 TAGLINE Elevate Your Data Analytics Skills, Optimize Workflows, and Drive Informed Decision-Making Across the Spectrum of Data Professions! KEY FEATURES ● Solve practical problems using SAS with real-world case studies that provide hands-on experience. ● Clear, step-by-step tutorials that guide you through various SAS procedures, ensuring easy understanding and application. ● Explore an extensive range of SAS capabilities, from basic data management to advanced analytics and reporting techniques. DESCRIPTION The Ultimate Statistical Analysis System (SAS) for Data Analytics is your go-to resource for mastering SAS, a powerful software suite for statistical analysis. This comprehensive book covers everything from the basics of SAS for data professionals to advanced topics like clustering analysis and association rules. With practical examples and clear explanations, this book equips readers with the knowledge and skills needed to excel in their roles as data scientists, analysts, researchers, and more. Whether you're a beginner looking to build a solid foundation in SAS or an experienced user seeking to expand your proficiency, this handbook has something for everyone. You'll learn essential techniques for importing, cleaning, and visualizing data, as well as conducting hypothesis testing, regression analysis, and inferential statistics. Advanced topics like SAS programming concepts and generating reports are also covered in detail, providing readers with the tools to tackle complex data challenges with confidence. With its accessible writing style and emphasis on real-world applications, this book is a practical guide that empowers readers to unlock the full potential of their data. Whether you're analyzing customer behavior, optimizing business processes, or conducting academic research, this handbook will be your trusted companion on the journey to mastering SAS and making informed decisions based on data-driven insights. WHAT WILL YOU LEARN ● Master the skills to import, clean, and transform data using SAS's powerful data manipulation tools. ● Gain the ability to conduct hypothesis testing to build regression models to analyze data relationships. ● Learn to design and produce compelling data visualizations that effectively communicate your data findings. ● Develop proficiency in advanced SAS programming techniques to tackle intricate analytical tasks. ● Discover the use of clustering analysis and association rules to identify meaningful patterns and relationships in your data. ● Generate professional reports to clearly present your analytical results. WHO IS THIS BOOK FOR? This book is ideal for data professionals, analysts, researchers, and anyone seeking to enhance their statistical analysis skills with SAS. Prior familiarity with basic statistical concepts and some experience with data analysis tools would be beneficial for readers to fully leverage the content of this handbook. TABLE OF CONTENTS 1. Introduction to SAS for Data Professionals 2. Data Import and Export in SAS 3. Data Cleaning and Transformation 4. Data Visualizations with SAS 5. Hypothesis Testing and Regression Analysis 6. Descriptive and Inferential Statistics 7. Advanced SAS Programming Concepts 8. Clustering Analysis with PROC CLUSTER 9. Association Rules in SAS 10. Generating Reports in SAS Index |
| ad hoc data analysis: High-Performance In-Memory Genome Data Analysis Hasso Plattner, Matthieu-P. Schapranow, 2013-11-19 Recent achievements in hardware and software developments have enabled the introduction of a revolutionary technology: in-memory data management. This technology supports the flexible and extremely fast analysis of massive amounts of data, such as diagnoses, therapies, and human genome data. This book shares the latest research results of applying in-memory data management to personalized medicine, changing it from computational possibility to clinical reality. The authors provide details on innovative approaches to enabling the processing, combination, and analysis of relevant data in real-time. The book bridges the gap between medical experts, such as physicians, clinicians, and biological researchers, and technology experts, such as software developers, database specialists, and statisticians. Topics covered in this book include - amongst others - modeling of genome data processing and analysis pipelines, high-throughput data processing, exchange of sensitive data and protection of intellectual property. Beyond that, it shares insights on research prototypes for the analysis of patient cohorts, topology analysis of biological pathways, and combined search in structured and unstructured medical data, and outlines completely new processes that have now become possible due to interactive data analyses. |
| ad hoc data analysis: Reliability and Performance with IBM DB2 Analytics Accelerator V4.1 Paolo Bruni, Jason Arnold, Leticia Cruz, Jeff Feinsmith, Willie Favero, Anna Griner, James Guo, Chris Harlander, Johannes Kern, Ravi Kumar, Ruiping Li, Andy Perkins, Jonathan Sloan, Steve Speller, Dino Tonelli, IBM Redbooks, 2015-05-11 The IBM® DB2® Analytics Accelerator for IBM z/OS® is a high-performance appliance that integrates the IBM zEnterprise® infrastructure with IBM PureDataTM for Analytics, powered by IBM Netezza® technology. With this integration, you can accelerate data-intensive and complex queries in a DB2 for z/OS highly secure and available environment. DB2 and the Analytics Accelerator appliance form a self-managing hybrid environment running online transaction processing and online transactional analytical processing concurrently and efficiently. These online transactions run together with business intelligence and online analytic processing workloads. DB2 Analytics Accelerator V4.1 expands the value of high-performance analytics. DB2 Analytics Accelerator V4.1 opens to static Structured Query Language (SQL) applications and row set processing, minimizes data movement, reduces latency, and improves availability. This IBM Redbooks® publication provides technical decision-makers with an understanding of the benefits of version 4.1 of the Analytics Accelerator with DB2 11 for z/OS. It describes the installation of the new functions, and the advantages to existing analytical processes as measured in our test environment. This book also introduces the DB2 Analytics Accelerator Loader V1.1, a tool that facilitates the data population of the DB2 Analytics Accelerator. |
| ad hoc data analysis: Intelligent Data Analysis and Applications Ajith Abraham, Xin Hua Jiang, Václav Snášel, Jeng-Shyang Pan, 2015-07-14 This volume of Advances in Intelligent Systems and Computing contains accepted papers presented in the main track of ECC 2015, the Second Euro-China Conference on Intelligent Data Analysis and Applications. The aim of ECC is to provide an internationally respected forum for scientific research in the broad area of intelligent data analysis, computational intelligence, signal processing, and all associated applications of AIs. The second edition of ECC was organized jointly by VSB - Technical University of Ostrava, Czech Republic, and Fujian University of Technology, Fuzhou, China. The conference, organized under the patronage of Mr. Miroslav Novak, President of the Moravian-Silesian Region, took place in late June and early July 2015 in the Campus of the VSB - Technical University of Ostrava, Czech Republic. |
| ad hoc data analysis: Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies Frédéric Noël, Felix Nyffenegger, Louis Rivest, Abdelaziz Bouras, 2023-01-31 This book constitutes the refereed proceedings of the 19th IFIP WG 5.1 International Conference, PLM 2022, Grenoble, France, July 10–13, 2022, Revised Selected Papers. The 67 full papers included in this book were carefully reviewed and selected from 94 submissions. They were organized in topical sections as follows: Organisation: Knowledge Management, Business Models, Sustainability, End-to-End PLM, Modelling tools: Model-Based Systems Engineering, Geometric modelling, Maturity models, Digital Chain Process, Transversal Tools: Artificial Intelligence, Advanced Visualization and Interaction, Machine learning, Product development: Design Methods, Building Design, Smart Products, New Product Development, Manufacturing: Sustainable Manufacturing, Lean Manufacturing, Models for Manufacturing. |
| ad hoc data analysis: Guerrilla Data Analysis Using Microsoft Excel MrExcel's Holy Macro! Books, Oz du Soleil, Bill Jelen, 2024-09-26 Master Excel data analysis with this hands-on guide. Learn efficient techniques, advanced functions, and best practices for real-world scenarios. Key Features Hands-on techniques for efficient Excel data analysis Advanced functions and best practices for real-world scenarios Step-by-step guidance on complex tasks like data validation and dynamic arrays Book DescriptionUnlock Microsoft Excel's hidden potential with this dynamic guide designed for data professionals and enthusiasts. You'll start by reviewing Excel basics before advancing to powerful tools like Excel Tables, Pivot Tables, and Power Query. Each chapter enhances your ability to analyze and visualize data efficiently, from complex lookups and dynamic arrays to essential data validation techniques that ensure accuracy and integrity in your spreadsheets. As you progress, you'll learn how to protect your work with advanced sheet protection methods and collaboration tools for seamless teamwork. The book also covers sophisticated functions like INDIRECT, OFFSET, and LET, preparing you to tackle complex data challenges. Additionally, you'll receive critical advice on avoiding the pitfalls of machine learning-driven features and maintaining clean, organized data. By the end of the guide, you'll have mastered Excel's advanced capabilities, empowering you to streamline workflows, optimize data processes, and make confident, data-driven decisions. This guide is your comprehensive resource for transforming your approach to data analysis with Excel.What you will learn Master Excel tables and dynamic spreadsheets Use VLOOKUP and XLOOKUP effectively Create and manipulate PivotTables Clean and validate data with Excel tools Apply conditional formatting and de-duping techniques Implement data models and relationships in Excel Who this book is for This book is ideal for data analysts, business professionals, and Excel users who need to enhance their data analysis skills. Readers should have a basic understanding of Excel and be familiar with its interface. No advanced Excel knowledge is required, but a willingness to learn and apply new techniques is essential. |
| ad hoc data analysis: Guerilla Data Analysis Using Microsoft Excel Bill Jelen, 2002-09-30 This book includes step-by-step examples and case studies that teach users the many power tricks for analyzing data in Excel. These are tips honed by Bill Jelen, &“MrExcel,&” during his 10-year run as a financial analyst charged with taking mainframe data and turning it into useful information quickly. Topics include perfectly sorting with one click every time, matching lists of data, data consolidation, data subtotals, pivot tables, and much more. |
| ad hoc data analysis: Intelligent Techniques for Data Analysis in Diverse Settings Celebi, Numan, 2016-04-20 Data analysis forms the basis of many forms of research ranging from the scientific to the governmental. With the advent of machine intelligence and neural networks, extracting, modeling, and approaching data has been unimpeachably altered. These changes, seemingly small, affect the way societies organize themselves, deliver services, or interact with each other. Intelligent Techniques for Data Analysis in Diverse Settings addresses the specialized requirements of data analysis in a comprehensive way. This title contains a comprehensive overview of the most innovative recent approaches borne from intelligent techniques such as neural networks, rough sets, fuzzy sets, and metaheuristics. Combining new data analysis technologies, applications, emerging trends, and case studies, this publication reviews the intelligent, technological, and organizational aspects of the field. This book is ideally designed for IT professionals and students, data analysis specialists, healthcare providers, and policy makers. |
| ad hoc 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. |
| ad hoc data analysis: Solid State Development and Processing of Pharmaceutical Molecules Michael Gruss, 2021-09-14 Solid State Development and Processing of Pharmaceutical Molecules A guide to the lastest industry principles for optimizing the production of solid state active pharmaceutical ingredients Solid State Development and Processing of Pharmaceutical Molecules is an authoritative guide that covers the entire pharmaceutical value chain. The authors—noted experts on the topic—examine the importance of the solid state form of chemical and biological drugs and review the development, production, quality control, formulation, and stability of medicines. The book explores the most recent trends in the digitization and automation of the pharmaceutical production processes that reflect the need for consistent high quality. It also includes information on relevant regulatory and intellectual property considerations. This resource is aimed at professionals in the pharmaceutical industry and offers an in-depth examination of the commercially relevant issues facing developers, producers and distributors of drug substances. This important book: Provides a guide for the effective development of solid drug forms Compares different characterization methods for solid state APIs Offers a resource for understanding efficient production methods for solid state forms of chemical and biological drugs Includes information on automation, process control, and machine learning as an integral part of the development and production workflows Covers in detail the regulatory and quality control aspects of drug development Written for medicinal chemists, pharmaceutical industry professionals, pharma engineers, solid state chemists, chemical engineers, Solid State Development and Processing of Pharmaceutical Molecules reviews information on the solid state of active pharmaceutical ingredients for their efficient development and production. |
| ad hoc data analysis: Ultimate Big Data Analytics with Apache Hadoop Simhadri Govindappa, 2024-09-09 TAGLINE Master the Hadoop Ecosystem and Build Scalable Analytics Systems KEY FEATURES ● Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management. ● Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics. ● Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics. DESCRIPTION In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape. The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises. You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with big data visualization and statistical analysis using Python. Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively. Don’t miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop. WHAT WILL YOU LEARN ● Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce. ● Master real-time analytics and data processing with Apache Spark’s powerful features. ● Develop skills in using Apache Hive for efficient data warehousing and complex queries. ● Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem. ● Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta. ● Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes. ● Build and deploy machine learning models with tools integrated into the Hadoop ecosystem. WHO IS THIS BOOK FOR? This book is tailored for data engineers, analysts, software developers, data scientists, IT professionals, and engineering students seeking to enhance their skills in big data analytics with Hadoop. Prerequisites include a basic understanding of big data concepts, programming knowledge in Java, Python, or SQL, and basic Linux command line skills. No prior experience with Hadoop is required, but a foundational grasp of data principles and technical proficiency will help readers fully engage with the material. TABLE OF CONTENTS 1. Introduction to Hadoop and ASF 2. Overview of Big Data Analytics 3. Hadoop and YARN MapReduce and Tez 4. Distributed Query Engines: Apache Hive 5. Distributed Query Engines: Apache Spark 6. File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta) 7. Python and the Hadoop Ecosystem for Big Data Analytics - BI 8. Data Science and Machine Learning with Hadoop Ecosystem 9. Introduction to Cloud Computing and Other Apache Projects Index |
| ad hoc data analysis: Microsoft Business Intelligence For Dummies Ken Withee, 2010-04-05 Learn to create an effective business strategy using Microsoft's BI stack Microsoft Business Intelligence tools are among the most widely used applications for gathering, providing access to, and analyzing data to enable the enterprise to make sound business decisions. The tools include SharePoint Server, the Office Suite, PerformancePoint Server, and SQL Server, among others. With so much jargon and so many technologies involved, Microsoft Business Intelligence For Dummies provides a much-needed step-by-step explanation of what's involved and how to use this powerful package to improve your business. Microsoft Business Intelligence encompasses a broad collection of tools designed to help business owners and managers direct the enterprise effectively This guide provides an overview of SharePoint, PerformancePoint, the SQL Server suite, Microsoft Office, and the BI development technologies Explains how the various technologies work together to solve functional problems Translates the buzzwords and shows you how to create your business strategy Examines related technologies including data warehousing, data marts, Online Analytical Processing (OLAP), data mining, reporting, dashboards, and Key Performance Indicators (KPIs) Simplifies this complex package to get you up and running quickly Microsoft Business Intelligence For Dummies demystifies these essential tools for enterprise managers, business analysts, and others who need to get up to speed. |
| ad hoc data analysis: Agriculture, Rural Development, Food and Drug Administration, and Related Agencies Appropriations, for 2010, 2009, 111-1 Hearings, * , 2009 |
| ad hoc data analysis: Speed, Data, and Ecosystems Jan Bosch, 2017-01-06 As software R&D investment increases, the benefits from short feedback cycles using technologies such as continuous deployment, experimentation-based development, and multidisciplinary teams require a fundamentally different strategy and process. This book will cover the three overall challenges that companies are grappling with: speed, data and ecosystems. Speed deals with shortening the cycle time in R&D. Data deals with increasing the use of and benefit from the massive amounts of data that companies collect. Ecosystems address the transition of companies from being internally focused to being ecosystem oriented by analyzing what the company is uniquely good at and where it adds value. |
| ad hoc data analysis: Feasibility Study - National Center for Statistical Analysis of Highway Operations. Highway Safety Act of 1973 (section 213). Volume I. Executive Summary. A Report to Congress from the Secretary of Transportation , 1975 |
| ad hoc data analysis: "A BEGINNER’S GUIDE TO PYTHON FOR DATA ANALYTICS " Henry Harvin , 2023-10-04 Want complete instructions on the Python library and its elements? Get solutions with practical case studies and implications of python in data analysis through this book. “A BEGINNER’S GUIDE TO PYTHON FOR DATA ANALYTICS” will help you to learn about the different aspects of python along with its implementation in data analysis in different industries. |
| ad hoc data analysis: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| ad hoc data analysis: OECD Public Governance Reviews Brazil's Federal Court of Accounts Insight and Foresight for Better Governance OECD, 2017-08-07 This report suggests concrete steps Brazil’s Federal Court of Accounts can take to adapt its own strategies, approaches and audit programming to provide valuable insight and foresight to policy makers in the centre of government. |
| ad hoc data analysis: Applied Multivariate Data Analysis J.D. Jobson, 2012-12-06 A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software. Business and government professionals, as well as academic researchers, are now regularly employing techniques that go far beyond the standard two-semester, introductory course in statistics. Even though for this group of users shorl courses in various specialized topics are often available, there is a need to improve the statistics training of future users of statistics while they are still at colleges and universities. In addition, there is a need for a survey reference text for the many practitioners who cannot obtain specialized courses. With the exception of the statistics major, most university students do not have sufficient time in their programs to enroll in a variety of specialized one-semester courses, such as data analysis, linear models, experimental de sign, multivariate methods, contingency tables, logistic regression, and so on. There is a need for a second survey course that covers a wide variety of these techniques in an integrated fashion. It is also important that this sec ond course combine an overview of theory with an opportunity to practice, including the use of statistical software and the interpretation of results obtained from real däta. |
| ad hoc data analysis: Data Analysis with Open Source Tools Philipp K. Janert, 2010-11-11 Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis Finally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, Mozilla An indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora |
Using PROC SQL to Create Ad Hoc Reports - lexjansen.com
The purpose of this paper is to demonstrate the powerful features of PROC SQL in the creation of ad hoc reports for clinical trial research. The reports created are for the display, summation …
Fully Automatic Tool Generation from Ad Hoc Data
In this paper, we demon-strate that it is possible to generate a suite of useful data process-ing tools, including a semi-structured query engine, several for-mat converters, a statistical …
Ad-hoc Big-Data Analysis with Lua - And LuaJIT
Pre-process the data so it can be handled by R or Excel or your favorite analytics tool (or Lua!). If the data is dynamic, then learn to pre-process it and build a data processing pipeline. Use Lua! …
QueryArtisan: Generating Data Manipulation Codes for Ad …
In this paper, we introduce QueryArtisan, a novel LLM-powered analytic tool specif-ically designed for data lakes. QueryArtisan transcends traditional ETL (Extract, Transform, Load) processes …
The 5 Styles of Business Intelligence: INDUSTRIAL-STRENGTH …
Ad Hoc Query and Analysis – Full investigative query into all data, as well as automated slice-and-dice OLAP analysis of the entire database – down to the transaction level of detail if …
Data & Data Science Notes - ib.barclays
Ad hoc workflows in Excel have their limits, either because of the size of the data or because of the complexity of models required to make sense of the data, or both. In this entry, we …
Ad Hoc Data Analysis (PDF) - x-plane.com
Ad hoc data analysis refers to the process of analyzing data without a pre-defined plan or hypothesis. It's a reactive approach, often triggered by a specific event, question, or …
PADS: A domain-specific language for processing ad hoc data
PADS is a declarative data description language that allows data an- alysts to describe both the physical layout of ad hoc data sources and semantic properties of that data.
Introduction to Ad-Hoc Reporting - United Nations
Aug 11, 2015 · What is an Ad hoc Report? A report built from an “ANALYSIS AREA” or pre-defined aggregated transactional data table for on-demand reporting. Who can create ad hoc …
Automatic Example Queries for Ad Hoc Databases
Analysts who assemble ad hoc databases frequently do not have significant SQL expertise, but we find that by providing a rich set of examples is sufficient to empower non-experts
Is Your Data Viable? Preparing Your Data for SAS® Visual …
One of the key criteria for the successful use of a BI application is being able to import users’ ad hoc data sources in a self-service manner for data analysis without depending on IT resources.
USING STANDARD / AD HOC REPORTS IN SAM DataBank
• For Standard, Admin and Ad Hoc Reports, there is a 12-year or 150,000-row limit of the report records to ensure acceptable system performance. • You can export to EXCEL, CSV, HTML, …
Syllabus for the HMIS Data Analytics On-Demand Course
Download, structure, transform, and clean data in preparation for data analysis. Quiz: Ad hoc reporting and Data Analysis Plans. Begin demo workbook analysis. Community Work: Draft …
Pig Latin: A Not-So-Foreign Language for Data Processing
We describe a new language called Pig Latin that we have designed to t in a sweet spot between the declarative style of SQL, and the low-level, procedural style of map-reduce. The …
From Dirt to Shovels: Fully Automatic Tool Generation from …
In this paper, we demon-strate that it is possible to generate a suite of useful data process-ing tools, including a semi-structured query engine, several for-mat converters, a statistical …
SMART VIEW TRAINING GUIDE - University of California, Los …
This feature can be used for retrieving data into excel on an ad hoc basis. It can be for one-time use or the Excel file can be saved and reused to refresh data at a later time. The following …
WWA Ad Hoc Query Overview - Office of Financial Management
It provides an interactive way for you to analyze workforce-related data as part of the Washington Workforce Analytics (WWA) Enterprise Data Warehouse (EDW). Ad hoc queries are created …
CDC WONDER An informatics tool for public health practice
Mar 5, 2014 · WONDER users query record-level data and produce ad-hoc summary statistics, such as frequency counts, rates, confidence intervals, standard errors, and percentages. …
Simple, ad-hoc methods for coping with missing data and …
Here, we use the completers to calculate the regression of the incomplete variable on the other complete variables. Then, we substitute the predicted mean for each unit with a missing value. …
What is ad hoc analysis? | Definition from TechTarget
Sep 4, 2024 · Ad hoc analysis -- also called ad hoc reporting -- in the sphere of BI refers to analysis aimed at answering a specific, immediate question or questions. It takes a quick dive into …
What Is Ad Hoc Reporting & Analysis? Definition, Benefits
Jun 10, 2021 · What Is Ad Hoc Analysis? Ad hoc analysis is similar to ad hoc reporting in that it’s a business intelligence process that aims to answer specific business questions on an as-needed …
What is Ad Hoc Analysis and Reporting? Process, Examples
Mar 26, 2024 · Ad hoc analysis involves the flexible and on-demand exploration of data to gain insights or solve specific problems. It allows analysts to dig deeper into datasets, ask ad hoc …
What is Ad Hoc Analysis and How Does it Work? - Chartio
Ad hoc analysis (aka ad hoc reporting) is the process of using business data to find specific answers to in-the-moment, often one-off, questions. It introduces flexibility and spontaneity to the …
What is ad hoc analysis and reporting? All You Need to Know
Jan 19, 2023 · Ad hoc analysis refers to the process of using ad hoc reporting to identify patterns or trends in data. It is a data-driven approach that allows users to unearth insights quickly in their …
Ad Hoc Analysis 101: A Beginner's Guide to Exploring Data
Apr 3, 2023 · Ad hoc analysis is a valuable tool that enables companies to make data-driven decisions by quickly answering specific questions with relevant data. By conducting ad hoc …
Ad Hoc Analysis: Making Sense of Data with Ad Hoc Data Analysis
Apr 22, 2023 · Ad hoc analysis is a method of data analysis that seeks to make sense of complex datasets by exploring the relationships between different variables. It enables researchers and …
Ad Hoc Analysis - Meaning, Types, Uses, Benefits & Examples
Ad hoc analysis is a specifically designed business intelligence process; it helps you to answer specific business questions by using the current data available. Since it uses the current data, …
Unveiling the Power of Ad Hoc Analysis: A Comprehensive Guide
Explore the transformative power of ad hoc analysis in data-driven decision-making. This comprehensive guide delves into its principles, benefits, key components, and real-world …
What are ad hoc analysis? Challenges and best practices - Toucan …
In a fast-paced and data-driven business landscape, ad hoc analysis has become a must-have tool for companies aiming to maintain a competitive edge. It helps you understand the “why” hiding …
