Datafangst og dataanalyse

Developing predictive models through data mining is a multifaceted process that involves extracting valuable insights and patterns from vast datasets to make informed predictions about future trends or outcomes. At its core, data mining encompasses various techniques and methodologies aimed at uncovering hidden patterns and relationships within data. The journey begins with data collection, where a diverse range of information is amassed from various sources. This raw data then undergoes preprocessing, involving cleaning, integration, normalization, and feature selection, to ensure its suitability for analysis. The selected features may include numerical, textual, or categorical data, each requiring specific transformations. Machine learning algorithms are then employed to build predictive models based on historical data patterns, allowing the model to learn and generalize from past experiences. Supervised learning approaches, such as regression and classification, are common for predictive modeling, while unsupervised learning techniques, like clustering, can reveal hidden structures within the data. The model's performance is assessed through rigorous evaluation metrics, and iterative refinement may be performed to enhance accuracy. Cross-validation techniques are often employed to assess the model's robustness and generalizability. Additionally, ensembling methods, combining multiple models, can further improve predictive accuracy. Interpretability is crucial, and efforts are made to comprehend and explain the model's decision-making processes, especially in fields where transparency is essential. The development of predictive models through data mining finds applications across diverse domains, including finance, healthcare, marketing, and more. Ultimately, this process empowers decision-makers with valuable insights, enabling them to anticipate future trends, mitigate risks, and make informed decisions based on the patterns extracted from the wealth of available data.

Data preprocessing is a crucial step in the realm of software agents and classification data mining, playing a pivotal role in enhancing the accuracy and efficiency of the subsequent analysis. This multifaceted process involves the transformation and manipulation of raw data into a format suitable for classification algorithms, ensuring that the extracted patterns and insights are meaningful and reliable. The initial phase encompasses data cleaning, where missing values, outliers, and inconsistencies are addressed, preventing these anomalies from influencing the classification results. Subsequently, data integration combines disparate sources, providing a comprehensive dataset for analysis. The normalization or scaling phase standardizes numerical features, eliminating potential biases introduced by varying scales. Feature selection then optimizes the dataset by identifying and retaining the most relevant attributes, reducing computational complexity and enhancing model interpretability. Textual and categorical data are often transformed through techniques like tokenization and one-hot encoding to facilitate the application of classification algorithms. Dimensionality reduction methods, such as principal component analysis (PCA), may be employed to further streamline the dataset. Additionally, addressing issues of class imbalance ensures that the classifier is not skewed towards the majority class, fostering a more accurate representation of the underlying patterns. Through these meticulous preprocessing steps, software agents are equipped with a refined dataset that sets the stage for effective classification in data mining, allowing for the extraction of meaningful patterns and insights from complex and diverse datasets.

Define security in the context of software applications Security in the context of software applications is the ability to protect application users and their data from unauthorized access, use, modification, or destruction. Secure applications are reliable and useful, and can be used by their users without fear.** Here are some specific examples of security threats that software applications can face: Unauthorized access: This is when someone who is not authorized to use an application gains access to it. This can be done through a variety of methods, such as hacking, social engineering, or exploiting vulnerabilities in the application's code.Unauthorized use: This is when someone who is authorized to use an application uses it for unauthorized purposes. For example, a user might use an application to access sensitive data that they are not authorized to see.Modification: This is when someone changes the data or code of an application. This can be done to steal data, insert malicious code, or simply to disrupt the application's functionality.Destruction: This is when someone intentionally destroys an application or its data. This can be done through a variety of methods, such as deleting files, corrupting data, or disabling the application's infrastructure.¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿, ¿¿¿¿¿¿, ¿¿¿¿¿¿, ¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿. ¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿, ¿¿¿¿¿¿ ¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿

Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.

"Publication stock no. TIM230601"--Title page verso.

Empowering IoT with Big Data Analytics provides comprehensive coverage of major topics, tools, and techniques related to empowering IoT with big data technologies and big data analytics solutions, thus allowing for better processing, analysis, protection, distribution, and visualization of data for the benefit of IoT applications and second, a better deployment of IoT applications on the ground. This book covers big data in the IoT era, its application domains, current state-of-the-art in big data and IoT technologies, standards, platforms, and solutions. This book provides a holistic view of the big data value-chain for IoT, including storage, processing, protection, distribution, analytics, and visualization. Big data is a multi-disciplinary topic involving handling intensive, continuous, and heterogeneous data retrieved from different sources including sensors, social media, and embedded systems. The emergence of Internet of Things (IoT) and its application to many domains has led to the generation of huge amounts of both structured and unstructured data often referred to as big data.

Be part of the future of Web3, decoding blockchain data to build trust in the next-generation internetKey Features:Build a deep understanding of the fundamentals of blockchain analyticsExtract actionable business insights by modeling blockchain dataShowcase your work and gain valuable experience to seize opportunities in the Web3 ecosystemPurchase of the print or Kindle book includes a free PDF eBookBook Description:Data is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3.You'll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You'll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data.The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you'll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients.By the end of this book, you'll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.What You Will Learn:Understand the core components of blockchain transactions and blocksIdentify reliable sources of on-chain and off-chain data to build robust datasetsUnderstand key Web3 business questions and how data science can offer solutionsBuild your skills to create and query NFT- and DeFi-specific datasetsImplement a machine learning toolbox with real-world use cases in the Web3 spaceWho this book is for:This book is designed for data professionals-data analysts, data scientists, or data engineers- and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. If you seek hands-on experience, you'll find value in the shared repository, enabling you to experiment with the provided solutions. While not mandatory, a basic understanding of statistics, machine learning, and Python will enhance your learning experience.

Understanding the Importance of Clustering and Segmentation in Data ScienceIn the ever-evolving field of data science, engineers are constantly seeking innovative techniques to extract meaningful insights from vast amounts of data. One such technique that has gained significant popularity is clustering and segmentation. In this subchapter, we will dive deep into the importance of clustering and segmentation in data science and its applications within the niche of machine learning for data science.Clustering, in its essence, involves grouping similar data points together based on their inherent characteristics. By identifying patterns and relationships among data points, clustering enables engineers to uncover hidden structures within the data, leading to valuable insights. With the ability to handle large datasets efficiently, clustering has become an indispensable tool for engineers working on data science projects.Segmentation, on the other hand, takes clustering a step further by partitioning data into distinct subsets or segments. This process allows engineers to understand the heterogeneous nature of the data and tailor their analysis and decision-making accordingly. By segmenting data, engineers are able to identify specific target groups, understand their unique preferences, and devise personalized strategies that drive business growth and enhance customer satisfaction.The importance of clustering and segmentation in data science cannot be overstated. These techniques enable engineers to gain a comprehensive understanding of complex datasets, identify trends, and make data-driven decisions with confidence. Whether it is customer segmentation for targeted marketing campaigns, anomaly detection in cybersecurity, or analyzing patterns in healthcare data, clustering and segmentation play a vital role in extracting valuable insights that drive business success.Furthermore, with the advent of machine learning algorithms and techniques, clustering and segmentation have witnessed significant advancements. Engineers can now employ sophisticated algorithms such as k-means, hierarchical clustering, and density-based clustering to achieve more accurate results and handle complex datasets effectively. These advancements have opened up new avenues for innovation and application of clustering and segmentation techniques in various industries, including finance, healthcare, retail, and more.

"In today's data-driven world, understanding statistical models is crucial for effective analysis and decision making. Whether you're a beginner or an experienced user, this book equips you with the foundational knowledge to grasp and implement statistical models within Tableau. Gain the confidence to speak fluently about the models you employ, driving adoption of your insights and analysis across your organization. As AI continues to revolutionize industries, possessing the skills to leverage statistical models is no longer optional--it's a necessity. Stay ahead of the curve and harness the full potential of your data by mastering the ability to interpret and utilize the insights generated by these models. Whether you're a data enthusiast, analyst, or business professional, this book empowers you to navigate the ever-evolving landscape of data analytics with confidence and proficiency. Start your journey toward data mastery today."--

Get up and running with building data pipelines and creating interactive dashboards to visualize, monitor, and present a wide variety of time-series data with this comprehensive introductory guideKey FeaturesInstall, set up, and configure Grafana for real-time data analysis, visualization, and alertingVisualize and monitor data using data sources such as InfluxDB, Telegraf, Prometheus, and ElasticsearchExplore Grafana's cloud support with Microsoft Azure, Amazon CloudWatch, and Google Cloud MonitoringPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGet ready to unlock the full potential of the open-source Grafana observability platform, ideal for analyzing and monitoring time-series data with this updated second edition. This beginners guide will help you get up to speed with Grafana's latest features for querying, visualizing, and exploring logs and metrics, no matter where they are stored.Starting with the basics, this book demonstrates how to quickly install and set up a Grafana server using Docker. You'll then be introduced to the main components of the Grafana interface before learning how to analyze and visualize data from sources such as InfluxDB, Telegraf, Prometheus, Logstash, and Elasticsearch. The book extensively covers key panel visualizations in Grafana, including Time Series, Stat, Table, Bar Gauge, and Text, and guides you in using Python to pipeline data, transformations to facilitate analytics, and templating to build dynamic dashboards. Exploring real-time data streaming with Telegraf, Promtail, and Loki, you'll work with observability features like alerting rules and integration with PagerDuty and Slack. As you progress, the book addresses the administrative aspects of Grafana, from configuring users and organizations to implementing user authentication with Okta and LDAP, as well as organizing dashboards into folders, and more. By the end of this book, you'll have gained all the knowledge you need to start building interactive dashboards.What you will learnLearn the techniques of data visualization using GrafanaGet familiar with the major components of Time series visualizationExplore data transformation operations, query inspector, and time interval settingsWork with advanced dashboard features, such as annotations, variable-based templating, and dashboard linking and sharingConnect user authentication through Okta, Google, GitHub, and other external providersDiscover Grafana's monitoring support for cloud service infrastructuresWho this book is forThis book is for business intelligence developers, business analysts, data analysts, and anyone interested in performing time-series data analysis and monitoring using Grafana. You'll also find this book useful if you're looking to create and share interactive dashboards or get up to speed with the latest features of Grafana. Although no prior knowledge of Grafana is required, basic knowledge of data visualization and some Python programming experience will help you understand the concepts covered in the book.Table of ContentsIntroducing Data Visualization with GrafanaTouring the Grafana InterfaceDiving into Grafana's Time Series VisualizationConnecting Grafana to a Prometheus Data SourceExtracting and Visualizing Data with InfluxDB and GrafanaShaping Data with Grafana TransformationsSurveying Key Grafana VisualizationsSurveying Additional Grafana VisualizationsCreating Insightful DashboardsWorking with Advanced Dashboard Features and Elasticsearch(N.B. Please use the Look Inside option to see further chapters)

Analytics made easy with Base and Advance SASData Analytics with SAS is an attempt to learn concepts of Data Analytics with SAS tool. Starting with the fundamentals, the book introduces you to SAS by explaining its architecture, components, libraries and graphical user interface. It then delves into abilities like manipulating and exploring data, where both basic and advanced techniques are covered.The book outlines concepts and functions for data manipulation. Data manipulation is important as without it, we cannot define data in a proper format. Moreover, data without a proper format and features cannot be used for further analysis.The book outlines concepts and functions of data exploration. Data exploration or Exploratory Data Analysis (EDA) is the first step in data analysis. It is a very critical step as it helps us get insights from data to understand past behaviors. To facilitate a practical learning experience with SAS, the book offers examples and code snippets.In conclusion, this comprehensive guidebook serves as a valuable resource for individuals interested in data analytics using SAS. It caters to both novices and seasoned users alike while preparing them for roles, within the field of Data Analytics.WHAT YOU WILL LEARN¿ Get familiar with the functions for insightful data exploration.¿ Shape and transform data using data manipulation functions.¿ Improve efficiency of SAS Operations by combining power of SQL with SAS.¿ Learn how to automate data analysis tasks and share insights across your team with SAS macros.¿ Learn how to visualize your data with impact using a variety of data visualization functions.WHO THIS BOOK IS FORThis book is meant for Data Analysts, Data Engineers, Business Analysts, Data Scientists, Business Intelligence Experts, Data journalists, Market researchers, Financial analysts, Risk analysts and anyone who wants to pursue a career in Analytics.

Explore the Core of Computing with "30 Essential Algorithms in Python". Dive into the world of algorithms with this insightful guide, perfect for anyone passionate about computer science. Whether you're a student starting your journey, a professional seeking to deepen your understanding, or a coding enthusiast eager to explore, this book is your gateway to mastering some of the most fundamental and powerful algorithms in computing. Unlock the Secrets of Algorithms:From sorting and searching to graph theory and beyond, discover the mechanisms behind 30 essential algorithms.Programmed Solutions in Python. Each algorithm is accompanied by its implementation in Python, providing practical, ready-to-use solutions.Real-World Applications: Understand not just the "how" but also the "why" as each algorithm is tied to its real-world applications. "Essential Algorithms: A Practical Approach" is more than a book; it's a journey into the heart of algorithmic thinking. Equip yourself with the knowledge and skills to tackle real-world problems with confidence and creativity. Embrace the world of algorithms today!

Information Modeling and Relational Databases provides an introduction to ORM (Object Role Modeling)-and much more. In fact, it's the only book to go beyond introductory coverage and provide all of the in-depth instruction you need to transform knowledge from domain experts into a sound database design. Inside, ORM authority Terry Halpin blends conceptual information with practical instruction that will let you begin using ORM effectively as soon as possible. Supported by examples, exercises, and useful background information, his step-by-step approach teaches you to develop a natural-language-based ORM model and then, where needed, abstract ER and UML models from it. This book will quickly make you proficient in the modeling technique that is proving vital to the development of accurate and efficient databases that best meet real business objectives.The most in-depth coverage of Object Role Modeling available anywhere-written by a pioneer in the development of ORM.Provides additional coverage of Entity Relationship (ER) modeling and the Unified Modeling Language-all from an ORM perspective.Intended for anyone with a stake in the accuracy and efficacy of databases: systems analysts, information modelers, database designers and administrators, instructors, managers, and programmers.Explains and illustrates required concepts from mathematics and set theory.

The guide includes a downloadable Power BI file so you can work and experiment with the code.This comprehensive guide is intended for a quick resolution of the most common cases that can be encountered when performing a modeling from scratch in POWER BI. Through its use, it is intended to stop investing numerous hours in development and poorly written (non-optimized) code that generates serious bugs. The result of this guide is a knowledge base that guides us to find the DAX code we need or to generate it by ourselves.In it you will find the resolution of the same case from different perspectives applying the different FUNCTIONS AND COMBINATIONS BETWEEN THEM so that you can find the best one that fits the need of the project.As you consult this guide, you will learn:Use and combine FUNCTIONS in DAX Language.Create code using a cleaner syntax.Acquire an agile development style.If you want to go deeper into DAX programming, this guide will help you write code cleanly and optimally.

The Importance of Data in Health CareIn the world of health care, data plays a crucial role in ensuring the delivery of efficient and effective services. From patient records to clinical trials, numerical data is invaluable in shaping policies, making informed decisions, and improving health outcomes. As students embarking on a journey in data analysis within the health care field, understanding the importance of data is essential.One of the primary uses of numerical data in health care is in patient care. By collecting and analyzing data on patients' medical history, symptoms, and treatment plans, healthcare professionals can gain insights into patterns and trends. This information enables them to make accurate diagnoses, develop personalized treatment plans, and monitor the progress of patients over time. Moreover, data analysis helps identify risk factors and predict potential health issues, allowing for proactive interventions and preventive measures.Another critical aspect of data in health care is research and development. Clinical trials and studies collect vast amounts of numerical data to test the efficacy and safety of new treatments, medications, and medical devices. This data is analyzed to determine the effectiveness of interventions, identify side effects, and provide evidence for regulatory approvals. Through rigorous data analysis, researchers can contribute to medical advancements, shaping the future of health care.Furthermore, data plays a crucial role in health care management. Administrators and policymakers rely on data to evaluate the performance of healthcare facilities, identify areas for improvement, and allocate resources effectively. By analyzing data on patient satisfaction, wait times, and cost-effectiveness, administrators can enhance the quality of care, streamline operations, and optimize resource allocation. This not only ensures better patient outcomes but also contributes to the overall sustainability of the health care system.

" Data and Analytics in Action: Project Ideas and Basic Code Skeleton in Python " is an indispensable guide for students navigating the dynamic realm of data science. This comprehensive book offers a diverse array of researchable project ideas spanning industries from finance to healthcare, e-commerce to environmental analysis. Each project is meticulously designed to bridge theory with practice, fostering critical thinking and problem-solving skills. With a forward-looking approach, the book explores cutting-edge concepts such as artificial intelligence, blockchain, and cybersecurity. It emphasizes not only technical proficiency but also ethical considerations, instilling a sense of responsibility in the use of data. Aspiring minds will find inspiration in the collaborative and interdisciplinary nature of the projects, preparing them for the multifaceted challenges of the evolving data science landscape. "Data and Analytics in Action" is more than a guide; it is a transformative tool shaping the next generation of data professionals.

Finance 2.0 addresses the following developmental move toward the universe of banking and money, a groundbreaking excursion filled by innovation, advancement, and changing client assumptions. This new period isn't simply a redesign yet a total reconsidering of how monetary administrations are conveyed, experienced, and made due. At the center of Money 2.0 is the use of state of the art advancements, for example, blockchain, computerized reasoning, and information investigation. These advancements have proactively started to alter customary financial activities, prompting further developed proficiency and security. Blockchain, for example, is introducing another time of straightforwardness and confidence in monetary exchanges. Shrewd agreements, empowered by blockchain, computerize complex monetary cycles with unparalleled accuracy. Man-made brainpower, one more foundation of Money 2.0, is changing client cooperations. Chatbots and menial helpers give every minute of every day customized help, while man-made intelligence driven calculations empower banks to offer modified monetary arrangements, survey credit risk, and distinguish misrepresentation continuously. These headways guarantee improved client encounters, quicker direction, and better gamble the board. Finance 2.0 additionally implies the ascent of decentralized finance (DeFi) and computerized monetary standards. DeFi stages are reshaping loaning, acquiring, and exchanging by killing middle people and offering clients more prominent command over their resources. In the interim, digital currencies like Bitcoin and Ethereum are testing the customary idea of cash and empowering cross-line exchanges with uncommon speed and cost-productivity. The fate of banking in the Money 2.0 period is additionally about monetary consideration. Innovation is spanning holes by giving financial administrations to unbanked populaces through versatile banking and computerized wallets. These arrangements engage people to get to monetary assets, construct credit, and partake in the worldwide economy. Be that as it may, this new scene accompanies its own arrangement of difficulties, including expanded online protection gambles and the requirement for powerful administrative structures to guarantee the security and trustworthiness of monetary frameworks

Explore the vast field of data science with "A Complete Guide to Data Science Essentials," an easy-to-use guide that breaks down the complicated parts of this ever-changing field. This book, written by Miguel, a seasoned data science expert, takes you step-by-step through the most important ideas, methods, and tools that will shape the data-driven world in 2023.Learn about important things like machine learning, statistical analysis, and computer languages (like Python, R, etc.) to get the skills you need to work in the ever-changing field of data science. Learn the mathematical ideas that support data science to build a strong base for advanced analytics and fixing problems.Stay ahead of the curve by learning how to use popular computer languages to do cutting edge data manipulation, analysis, and visualization. The book puts a lot of stress on practical scripting and coding, making sure that readers learn how to turn raw data into insights that they can use.Master the art of cleaning and preparing data to make it more reliable and of higher quality. Learn the newest ways to collect and source data. With an emphasis on real-world uses, the guide talks about the problems that come up when you have to deal with missing values and outliers and gives you useful ways to make your data analysis stronger."A Complete Guide to Data Science Essentials" is the book you need whether you're an experienced data professional or a beginner who can't wait to start your data science path. This is an important resource for staying current in the data-driven era. It includes the most popular keywords and methods that define the field of data science right now.

The general format of this book is I'll start with each concept, explaining it in a bunch of sections and graphical examples. I will introduce you to some of the notations and fancy terminologies that data scientists like to use so you can talk the same language, but the concepts themselves are generally pretty simple. After that, I'll throw you into some actual Python code that actually works that we can run and mess around with, and that will show you how to actually apply these ideas to actual data.

Streamline your workflow, transform raw data into actionable insights, and use Alteryx Designer to shape, design, and visualize dataKey FeaturesAcquire the skills necessary to perform analytics operations like an expertDiscover hidden trends and insights in your data from various sources to make accurate predictionsReduce the time and effort required to derive insights from your dataPurchase of the print or Kindle book includes a free eBook in the PDF formatBook DescriptionAlteryx allows you to create data manipulation and analytic workflows with a simple, easy-to-use, code-free UI, and perform fast-executing workflows, offering multiple ways to achieve the same results. The Alteryx Designer Cookbook is a comprehensive guide to maximizing your Alteryx skills and determining the best ways to perform data operations.This book's recipes will guide you through an analyst's complete journey, covering all aspects of the data life cycle. The first set of chapters will teach you how to read data from various sources to obtain reports and pass it through the required adjustment operations for analysis. After an explanation of the Alteryx platform components with a particular focus on Alteryx Designer, you'll be taken on a tour of what and how you can accomplish by using this tool. Along the way, you'll learn best practices and design patterns. The book also covers real-world examples to help you apply your understanding of the features in Alteryx to practical scenarios.By the end of this book, you'll have enhanced your proficiency with Alteryx Designer and an improved ability to execute tasks within the tool efficiently.What you will learnSpeed up the cleansing, data preparing, and shaping processPerform operations and transformations on the data to suit your needsBlend different types of data sources for analysisPivot and un-pivot the data for easy manipulationPerform aggregations and calculations on the dataEncapsulate reusable logic into macrosDevelop high-quality, data-driven reports to improve consistencyWho this book is forThis book is for data analysts, data professionals, and business intelligence professionals seeking to harness the full potential of the tool. A basic understanding of Alteryx Designer and Alteryx terminology, including macros, apps, and workflows, is all you need to get started with this book.Table of ContentsInput data from filesWorking with databasesData Preparation Data Transformations Data Parsing Grouping DataBlending and Merging dataAggregationsDynamic Operations/ ToolsMacros and AppsDownloads, APIs & Web ServicesDeveloper optionsReporting with AlteryxOutputting Data

Discover over 80 recipes for modeling and handling real-life biological data using modern libraries from the R ecosystemKey Features:Apply modern R packages to process biological data using real-world examplesRepresent biological data with advanced visualizations and workflows suitable for research and publicationsSolve real-world bioinformatics problems such as transcriptomics, genomics, and phylogeneticsPurchase of the print or Kindle book includes a free PDF eBookBook Description:The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You'll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools.This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses.By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.What You Will Learn:Set up a working environment for bioinformatics analysis with RImport, clean, and organize bioinformatics data using tidyrCreate publication-quality plots, reports, and presentations using ggplot2 and QuartoAnalyze RNA-seq, ChIP-seq, genomics, and next-generation genetics with BioconductorSearch for genes and proteins by performing phylogenetics and gene annotationApply ML techniques to bioinformatics data using mlr3Streamline programmatic work using iterators and functional tools in the base R and purrr packagesUse ChatGPT to create, annotate, and debug code and workflowsWho this book is for:This book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning via a recipe-based approach. Working knowledge of the R programming language and basic knowledge of bioinformatics are prerequisites.

Master the art of machine learning by using the one-of-a-kind Qlik platform, and take your data analytics skills to the next levelKey FeaturesGain a solid understanding of machine learning concepts and learn to effectively define a problemExplore the application of machine learning principles within the Qlik platformApply your knowledge of ML to real-world scenarios with the help of practical examplesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe ability to forecast future trends through data prediction, coupled with the integration of ML and AI, has become indispensable to global enterprises. Qlik, with its extensive machine learning capabilities, stands out as a leading analytics platform enabling businesses to achieve exhaustive comprehension of their data. This book helps you maximize these capabilities by using hands-on illustrations to improve your ability to make data-driven decisions.You'll begin by cultivating an understanding of machine learning concepts and algorithms, and build a foundation that paves the way for subsequent chapters. The book then helps you navigate through the process of framing machine learning challenges and validating model performance. Through the lens of Qlik Sense, you'll explore data preprocessing and analysis techniques, as well as find out how to translate these techniques into pragmatic machine learning solutions. The concluding chapters will help you get to grips with advanced data visualization methods to facilitate a clearer presentation of findings, complemented by an array of real-world instances to bolster your skillset.By the end of this book, you'll have mastered the art of machine learning using Qlik tools and be able to take your data analytics journey to new heights.What you will learnFind out how to build practical machine learning solutions with the Qlik platformDevelop the skills needed to generate and verify machine learning models using the Qlik platformDiscover techniques used for preparing and investigating data to craft machine learning solutionsUnderstand how to transform real-world business problems into machine learning modelsExpand your potential to new use cases with data analyticsExplore advanced visualization techniques to make your models come aliveWho this book is forIf you're interested in data and analytics and are looking to extend your skillset to machine learning, this book is for you. Basic working knowledge of data, preferably with Qlik tools, will help you get started with this book. This is an excellent guide for anyone who wants to start using machine learning as part of their data analytics journey.Table of ContentsIntroduction to Machine Learning with QlikMachine Learning Algorithms and Models with QlikData Literacy in Machine Learning ContextCreating a Good Machine Learning Solution with the Qlik PlatformSetting Up the EnvironmentsPreprocessing and Exploring Data with Qlik SenseDeploying and Monitoring Machine Learning ModelsUtilizing Qlik AutoMLAdvanced Data Visualisation Techniques for Machine Learning SolutionsExamples and Case StudiesFuture Direction

When you enter the world of time series analysis, you step into a labyrinth of numerical patterns, where each turn you take unveils another layer of complexity. Here, simple mathematical or statistical models struggle to keep pace.Reality is riddled with complex patterns in time series data, which, like cryptic pieces of a jigsaw puzzle, hold the key to unraveling insightful predictions. These complex patterns include non-linearity, non-stationarity, long memory or dependence, asymmetry, and stochasticity.But what creates these intricate patterns? Raghurami Reddy Etukuru, Ph.D., a distinguished and adaptable specialist in data science and artificial intelligence, delves into that question in this groundbreaking book, explaining that the factors are numerous and multifaceted, each adding their own measure of challenge. He doesn't just discuss problems but also addresses the forecasting of time series amidst intricate patterns.Take a deep dive deep into the world of numbers and patterns, so you can unravel complexities and leverage the power of artificial intelligence to enhance predictive capabilities. More than just a theoretical guide, this book is a practical companion in the often-turbulent journey of understanding and predicting complex time series data.

Empowering You to Master Business Intelligence and Solve Real-world Analytical Problems.DESCRIPTIONIn today's analytics landscape, proficiency in Excel and Power BI is practically a prerequisite for a successful career. This book provides a concise yet comprehensive exploration of these foundational elements of modern Business Intelligence (BI).Comprising ten chapters, this book covers the entire development journey of a Power BI analytical solution, spanning from data modeling and preparation to report creation, publication, and dashboard and app deployment. It offers insights into security measures and performance optimization, ensuring a well-rounded understanding of the BI ecosystem. Furthermore, it delves into advanced topics, such as leveraging data science algorithms within reports, offering readers an extensive learning experience. The book takes a holistic approach to these technologies, offering a contrast to the narrow perspectives often adopted by contemporary BI professionals who rely solely on a single tool or language. The book allows data enrichment through calculations that can be achieved using various languages, including SQL queries at the source, Power Query Formula Language, M, Python, R, and DAX. By the end of this book you will master these options but be able to also weigh their pros and cons to make informed decisions.TABLE OF CONTENTS 1. Introducing the Theoretical Background for Democratizing Analytics2. Acquiring and transforming data from multiple sources3. Power Query Transformations and Formula Language (M) Overview4. Building a simple analytical solution with Power Pivot in Excel5. Enhancing the model and business analysis with DAX6. Creating reports in Power BI Desktop7. Using the Power BI Service8. Data Science in Power BI 9. Optimizing Power BI models and reports10. Deploying, Maintaining and Securing Power BI Assets Index

Through the application of cutting-edge techniques like Big Data, Data Mining, and Data Science, it is possible to extract insights from massive datasets. These methodologies are crucial in enabling informed decision-making and driving transformative advancements across many fields, industries, and domains. This book offers an overview of latest tools, methods and approaches while also highlighting their practical use through various applications and case studies.

Une bible magistrale sur SASCe livre présente le socle de connaissances communes à tous les utilisateurs de SAS, le progiciel d'informatique décisionnelle le plus utilisé au monde. Il traite tout particulièrement des fonctionnalités de Base SAS, module au c1/2ur du système SAS. Pédagogique et complet, il peut servir aussi bien de guide d'initiation pour les utilisateurs débutants que d'ouvrage de référence pour les plus expérimentés, et concerne aussi bien les utilisateurs de SAS Foundation que ceux de SAS Enterprise Guide, SAS Studio et SAS University Edition.Cet ouvrage couvre les programmes des certifications SAS Certified Specialist: Base Programming Using SAS 9.4 et Advanced Programming for SAS 9.Parmi les sujets développés: la création, la manipulation et la gestion des tables de données;les procédures d'exploration des données: construction de tableaux, de rapports, de graphiques au moyen des procédures ODS Graphics;la production de documents au format HTML, PowerPoint, RTF, XLSX ou PDF avec ODS;la procédure PROC SQL et le langage SQL de SAS;le langage macro spécifique à SAS.Pour vous aider à bien assimiler tous les concepts, le livre comprend près de 500 programmes d'exemples, plus de 150 exercices et des liens vers une centaine d'articles en ligne.Une 4e édition mise à jour et augmentéeEnrichie de plus d'une centaine de pages, cette nouvelle édition propose des mises à jour importantes sur: les passerelles entre SAS et Excel;l'optimisation des ressources;la production de graphiques au moyen de PROC SGPLOT et PROC SGPANEL;la création et gestion de vos tables au moyen de PROC SQL.Le livre, qui porte essentiellement sur la version 9.4 de SAS, est également compatible avec les versions 9.2 et 9.3.Ce livre a le soutien de SAS France.À qui s'adresse cet ouvrage ?Aux professionnels souhaitant découvrir ou approfondir leurs connaissances de la programmation SASAux étudiants qui débutent avec SAS ou qui souhaitent préparer les examens de certification SAS Cert