Deep Learning with Python
In recent years, deep learning has emerged as one of the most transformative fields in technology, with applications spanning from computer vision and natural language processing to self-driving cars and healthcare. Python, with its powerful libraries and ease of use, has become the preferred programming language for deep learning, allowing developers and researchers to experiment, innovate, and scale their projects more effectively. This book, Deep Learning with Python, is crafted to guide readers through the essential concepts, methods, and tools necessary to master deep learning, empowering them to create impactful solutions using Python. This book is designed for anyone interested in learning how to build and deploy deep learning models. We start with the foundational principles, gradually introducing advanced topics such as convolutional and recurrent neural networks. Each chapter is complemented with hands-on examples and clear explanations, making complex concepts accessible without oversimplification. Whether you are a beginner exploring neural networks for the first time, or an experienced programmer expanding your skillset, Deep Learning with Python will serve as a comprehensive resource to deepen your understanding and bring your ideas to life in this exciting field.
Night Vision and Daylight Camera Systems for Wildlife
Only a small proportion of our most interesting wildlife is ever seen. This book describes how wildlife can be filmed in a non-intrusive way without disturbing behaviour. Step-by-step, clearly illustrated details lead the reader past technical challenges and allow many new insights.
Night Vision and Daylight Camera Systems for Wildlife
Only a small proportion of our most interesting wildlife is ever seen. This book describes how wildlife can be filmed in a non-intrusive way without disturbing behaviour. Step-by-step, clearly illustrated details lead the reader past technical challenges and allow many new insights.
Building Tomorrow: Unleashing the Potential of Artificial Intelligence in Construction
The Well-Grounded Data Analyst
Complete eight data science projects that lock in important real world skills-along with a practical process you can use to learn any new technique quickly and efficiently. The Well-Grounded Data Analyst guides you through eight common scenarios you'll encounter as a data scientist or analyst. As you explore each project, you'll also master a proven process for quickly learning new skills developed by author and Half Stack Data Science podcast host David Asboth. In The Well-Grounded Data Analyst you'll learn: - High-value skills to tackle specific analytical problems - Deconstructing problems for faster, practical solutions - Data modeling, PDF data extraction, and categorical data manipulation - Handling vague metrics, deciphering inherited projects, and defining customer records In The Well-Grounded Data Analyst you'll learn how to shift the way you think about data from the structured clean problems you get in a classroom, book, or bootcamp to the messy open-ended challenges of the workplace. As you work through eight problems you'll see over and over on the job, you'll discover a solutions-driven methodology that's focused on getting results. You'll learn how to determine a minimum viable answer for your stakeholders, identify and obtain the data you need to deliver, and reliably present and iterate on your findings. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book The Well-Grounded Data Analyst presents eight industry scenarios that you're sure to encounter in your data science career. The book can be read cover-to-cover, or opened up to whichever chapter is most relevant to your current challenges. You'll explore data modeling by identifying customer records from retail transactions, navigate badly defined metrics, and learn how to extract data from PDFs as you analyze how COVID-19 changed the film industry. Get your hands dirty with categorical data that defies conventional statistical analysis, and unearth hidden patterns in seemingly simple time series data. Plus, you'll learn what to do when you're handed someone else's work to expand on, and how to wow your stakeholders with your presentations. By the time you're done reading and working, you'll have the kind of on-the-job skills it normally takes years to learn--and an amazing portfolio of projects to show off at an interview. About the reader For early-career data scientists. You know the technical basics--this book will teach you how to apply them successfully on the job. About the author David Asboth is a "data generalist", with experience as a freelance consultant, educator, software architect, and more. He co-hosts the Half Stack Data Science podcast about data science in the real world, has spoken at multiple conferences including the Data Science Festival in London, and has taught a variety of data science courses to enterprise students including large banks and consultancies.
Generative AI Essentials
DESCRIPTION Generative AI is changing the way we think about creativity and problem-solving. This book is your go-to guide for understanding and working with this exciting technology. This book offers a clear introduction to generative AI, starting with basics like machine learning and deep learning. It explains key models, including GANs and VAEs, breaking down their architectures and training methods. You will discover how Transformer models like GPT have transformed natural language processing and enabled advancements in language generation. The book explores practical applications such as image synthesis, style transfer, and text generation, showing how generative AI merges technology with creativity. Advanced topics like reinforcement learning, AI ethics, and bias are also covered. Practical tips for creating your own generative AI models, along with insights into the future of this groundbreaking field, making it an essential resource for AI enthusiasts and professionals.By the end of this book, you will have a firm grasp of generative AI concepts and practical skills to get you started. You will be well-prepared to use cloud platforms like AWS, Azure, and GCP to build and launch powerful generative AI projects. From creating realistic images to crafting natural text, you will explore hands-on examples while tackling important ethical questions. This book gives you the skills and confidence to explore the limitless potential of generative AI.WHAT YOU WILL LEARN● Build foundational knowledge of generative AI principles and models.● Apply machine learning and deep learning for creative content generation.● Leverage GANs, VAEs, and Transformer models in real-world scenarios.● Master cloud tools for scalable generative AI development.● Address ethical challenges and implement responsible AI practices.● Explore advanced applications and future directions of generative AI.WHO THIS BOOK IS FORThis book is designed for data scientists, machine learning engineers, software developers, cloud professionals, educators, students, and creative professionals.
Statistics for Data Scientists and Analysts
DESCRIPTION Statistics is a powerful tool for data analysis, visualization, and inference. Python is a popular programming language that offers a rich set of libraries and frameworks for statistical computing. Together, they can help you solve real-world problems and make informed decisions based on data. This book teaches you how to use Python to implement statistical concepts and techniques in a practical and effective way. You will also learn how to perform data science and analysis to generate insights, patterns, and trends.This book introduces the basics of statistics, such as descriptive and inferential statistics, ML, probability distributions, hypothesis testing, and confidence intervals. It also covers advanced topics such as regression analysis, linear algebra, statistical tests, time series, survival, and correlation analysis. You will learn how to identify patterns, interpret data, and make data-driven decisions. The book emphasizes practical learning with examples, exercises, and code snippets using popular Python libraries like NumPy, Pandas, Matplotlib, Seaborn, and SciPy to perform various statistical tasks.By the end of this book, you will have a solid foundation in statistics and Python programming. You will be able to explore, analyze, and visualize data using Python. You will also be able to perform various statistical tests and interpret the results. WHAT YOU WILL LEARN● Master data manipulation, cleaning, and visualization techniques using Python.● Apply core statistical methods to analyze real-world datasets.● Build and evaluate statistical models for regression, classification, and clustering.● Interpret and communicate insights derived from statistical analyses effectively.● Explore advanced statistical techniques like time series and survival analysis.WHO THIS BOOK IS FORThis book is ideal for data scientists, ML engineers, statisticians, Python practitioners, researchers, and anyone who works with data and statistics.
Mastering Microsoft Power BI - Second Edition
Plan, design, develop, and manage robust Power BI solutions to generate meaningful insights and make data-driven decisions.Purchase of the print or Kindle book includes a free eBook in the PDF format.Key FeaturesMaster the latest dashboarding and reporting features of Microsoft Power BICombine data from multiple sources, create stunning visualizations and publish Power BI apps to thousands of usersGet the most out of Microsoft Power BI with real-world use cases and examplesBook DescriptionMastering Microsoft Power BI, Second Edition, provides an advanced understanding of Power BI to get the most out of your data and maximize business intelligence. This updated edition walks through each essential phase and component of Power BI, and explores the latest, most impactful Power BI features. Using best practices and working code examples, you will connect to data sources, shape and enhance source data, and develop analytical data models. You will also learn how to apply custom visuals, implement new DAX commands and paginated SSRS-style reports, manage application workspaces and metadata, and understand how content can be staged and securely distributed via Power BI apps. Furthermore, you will explore top report and interactive dashboard design practices using features such as bookmarks and the Power KPI visual, alongside the latest capabilities of Power BI mobile applications and self-service BI techniques. Additionally, important management and administration topics are covered, including application lifecycle management via Power BI pipelines, the on-premises data gateway, and Power BI Premium capacity. By the end of this Power BI book, you will be confident in creating sustainable and impactful charts, tables, reports, and dashboards with any kind of data using Microsoft Power BI.What you will learnBuild efficient data retrieval and transformation processes with the Power Query M language and dataflowsDesign scalable, user-friendly DirectQuery, import, and composite data modelsCreate basic and advanced DAX measuresAdd ArcGIS Maps to create interesting data storiesBuild pixel-perfect paginated reportsDiscover the capabilities of Power BI mobile applicationsManage and monitor a Power BI environment as a Power BI administratorScale up a Power BI solution for an enterprise via Power BI Premium capacityWho this book is forBusiness Intelligence professionals and intermediate Power BI users looking to master Power BI for all their data visualization and dashboarding needs will find this book useful. An understanding of basic BI concepts is required and some familiarity with Microsoft Power BI will be helpful to make the most out of this book.Table of ContentsPlanning Power BI ProjectsPreparing Data sourcesConnecting to Sources and Transforming Data with MDesigning Import, DirectQuery, and Composite Data ModelsDeveloping DAX Measures and Security RolesPlanning Power BI ReportsCreating and Formatting VisualizationsApplying Advanced AnalyticsDesigning DashboardsManaging Workspaces and ContentManaging the On-Premises Data GatewayDeploying Paginated ReportsCreating Power BI Apps and Content DistributionAdministering Power BI for an OrganizationBuilding Enterprise BI with Power BI Premium
Data Structures, Algorithms, and Invariants
Data Structures, Algorithms, and Invariants: A Practical Guide provides students with the knowledge and understanding they need to make sensible decisions regarding the choice of an abstract data type (ADT) for particular use cases. The book is a practical guide that helps readers with the process of building a software application utilizing a variety of list-based, tree-based, and graph-based ADTs.The opening chapters discuss solving problems, the concept of computational complexity, different types of ADTs, and why they are needed in software development. Additional chapters address unordered, positional, and ordered ADTs, the process of sorting, ways of determining order, and graphs. Each chapter features an introduction, learning objectives, key terms, takeaways, QR codes that link out to additional content such as videos, and chapter review questions to support the reading and learning experience. Additionally, students gain access to a companion source code repository that provides implementations of many of the ADTs.Data Structures, Algorithms, and Invariants is an ideal textbook for data structure and algorithms courses in information science, data science, and programming.
Big Data
In recent years, the application of artificial intelligence and big data analysis has become increasingly common, including in areas related to security and cybersecurity. The use of artificial intelligence and advanced big data analysis tools has enabled new critical perspectives on many different areas of the world around us. The purpose of this volume is to present the perspectives of researchers, experts and scientists, who are concerned with the problem of the distortion of reality by the authors of false narratives.The book focuses primarily on security matters, issues demanded by the international political and economic situation. Disinformation is a tool of hybrid war, shaping the social and economic environment long before the start of the Russian invasion of Ukraine. Since February 2022, the issues of post-truth, fake news, and disinformation have taken on additional gravity.
Data Science Essentials with R
DESCRIPTION This book teaches you to draw insights from your data. In today's data-driven business landscape, making informed decisions requires effective data analysis. This book guides you through the steps to import, structure, and visualize your data in R, and apply statistical and ML algorithms to drive better insights.This book offers a thorough introduction to data science, starting with R programming basics and advancing to ML and data visualization. Learn to clean, explore, and transform data using tools like dplyr. Key statistical concepts like probability, hypothesis testing, and modeling are covered, providing a foundation for data-driven decisions. Discover supervised and unsupervised ML techniques, feature engineering, and model evaluation. The book also provides career guidance in data science, including skill-building tips and job search strategies, equipping you to excel in this growing field.By the end of this book, you will be able to confidently use R to prepare data for analysis and apply ML algorithms to make predictions and drive business decisions.KEY FEATURES ● Master R for effective data analysis and ML.● Analyze data, identify patterns, and drive informed decision-making.● Learn by doing hands-on R codes and applying ML techniques.WHAT YOU WILL LEARN● Use R to clean, analyze, and visualize data effectively.● Apply statistical techniques to find patterns and trends in data.● Understand and implement key ML algorithms step-by-step.● Data visualization techniques using ggplot2 to create informative visualizations.● Strong foundation in statistical concepts, including probability theory, hypothesis testing, and statistical modeling.WHO THIS BOOK IS FORThis book is ideal for individuals with a basic understanding of programming and statistics who aspire to enter the field of data science. Professionals such as data analysts, software engineers, and researchers will find this book particularly valuable as it provides a practical approach to leveraging data for informed decision-making.
Man-Machine Speech Communication
This book constitutes the refereed proceedings of the 19th National Conference on Man-Machine Speech Communication, NCMMSC 2024, held in Urumqi, China, during August 15-18, 2024. The 33 papers included in these proceedings were carefully reviewed and selected from 205 submissions. They deal with topics such as speech technology and large language models, audio processing, prosody modeling and dialogue systems. Key areas include speech recognition, speaker identification and verification, speech/sound/music synthesis, speech enhancement, sound event detection, multimodal systems, conversational AI, phonetics, phonology and prosody analysis, auditory processing, and acoustic scene modeling etc.
Information and Communications Security
This two-volume proceedings set LNCS 15056-15057 constitutes the proceedings of 26th International Conference on Information and Communications Security, ICICS 2024, in Mytilene, Greece, during August 26-28, 2024. The 32 full papers presented in this book were carefully selected and reviewed from 123 submissions. They cover topics related to many aspects of security in information and communication systems, ranging from attacks, to defences, to trust issues, to anomaly-based intrusion detection, to privacy preservation, and to theory and applications of various cryptographic techniques.
Information and Communications Security
This two-volume proceedings set LNCS 15056-15057 constitutes the proceedings of 26th International Conference on Information and Communications Security, ICICS 2024, in Mytilene, Greece, during August 26-28, 2024. The 32 full papers presented in this book were carefully selected and reviewed from 123 submissions. They cover topics related to many aspects of security in information and communication systems, ranging from attacks, to defences, to trust issues, to anomaly-based intrusion detection, to privacy preservation, and to theory and applications of various cryptographic techniques.
Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
Kickstart Database Management System Fundamentals
From Concept to Implementation: Mastering Database Design Book DescriptionIn today's data-driven world, effective database management is essential for harnessing the full potential of raw information. A strong foundation in DBMS can set professionals apart in their roles, making them invaluable in maintaining and optimizing data systems.[Kickstart Database Management System Fundamentals] bridges the gap between database theory and practical application, empowering readers with the skills needed to design, build, and manage reliable database systems. The book provides an overview of key database concepts such as data modeling, normalization, and relational principles. It also delves into advanced topics like data integrity, query optimization, transaction management, and indexing. Each chapter features practical examples, case studies, and hands-on activities to reinforce learning and ensure readers can apply their knowledge effectively. By the end of this book, readers will grasp essential best practices for database design and management. They will be equipped to create scalable, secure database solutions, ensure data consistency, and enhance performance. Whether you are a student, educator, or professional, this book prepares you to tackle real-world database challenges with confidence. Table of Contents1. Introduction to Database Systems2. Data Modeling and Design3. Relational Database Management Systems4. Query Optimization5. Database Normalization and Normal Forms6. Transaction Management and Concurrency Control7. Data Warehousing and Business Intelligence8. Distributed Databases and NoSQL9. Data Security and Privacy10. Database Administration and Cloud Services Index
Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
Mastering PostgreSQL 17 - Sixth Edition
Learn advanced PostgreSQL techniques vital for everyday operation and security with a focus on PostgreSQL 17, its new features, and evolving real-world applicationsKey Features: - Optimize queries and performance for PostgreSQL installations- Secure databases with advanced access controls and encryption- Master replication, backups, and disaster recovery strategies- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Starting with new features introduced in PostgreSQL 17, the sixth edition of this book provides comprehensive insights into advanced database management, helping you elevate your PostgreSQL skills to an expert level. Written by Hans-J羹rgen Sch繹nig, a PostgreSQL expert with over 25 years of experience and the CEO of CYBERTEC PostgreSQL International GmbH, this guide distills real-world expertise from supporting countless global customers. It guides you through crucial aspects of professional database management, including performance tuning, replication, indexing, and security strategies.You'll learn how to handle complex queries, optimize execution plans, and enhance user interactions with advanced SQL features such as window functions and JSON support. Hans equips you with practical approaches for managing database locks, transactions, and stored procedures to ensure peak performance. With real-world examples and expert solutions, you'll also explore replication techniques for high availability, along with troubleshooting methods to detect and resolve bottlenecks, preparing you to tackle everyday challenges in database administration.By the end of the book, you'll be ready to deploy, secure, and maintain PostgreSQL databases efficiently in production environments.What You Will Learn: - Deploy and manage PostgreSQL in production environments- Improve database throughput and ensure speedy responses from PostgreSQL- Utilize indexes, partitions, and full-text search- Handle transactions, locking, and concurrency issues- Secure PostgreSQL with encryption and access controls- Implement replication for high availability- Get to grips with handling redundancy and data safety- Fix the most common issues and real-world problems faced by PostgreSQL usersWho this book is for: This book is for database administrators, PostgreSQL developers, and IT professionals who want to implement advanced functionalities and tackle complex administrative tasks using PostgreSQL 17. A foundational understanding of PostgreSQL and core database concepts is essential, along with familiarity with SQL. Prior experience in database administration will enhance your ability to leverage the advanced techniques presented in this book.Table of Contents- What is New in PostgreSQL 17- Understanding Transactions and Locking- Making Use of Indexes- Handling Advanced SQL- Log Files and System Statistics- Optimizing Queries for Good Performance- Writing Stored Procedures- Managing PostgreSQL Security- Handling Backup and Recovery- Making Sense of Backups and Replication- Deciding on Useful Extensions- Troubleshooting PostgreSQL- Migrating to PostgreSQL
Cracking the Data Code
Why do we continue to struggle with data? With all the powerful tools we have in processing power, data tools, and computer programming, we still search for some elusive truth to pervasive problems. AI hallucinates, 'good data' that we started with is suddenly unintelligible, systems that should talk to each other seamlessly continually experience errors and need correction.What we fail to incorporate into our data world is the fact that data is language and has entwined in that language its own code that does not get captured in databases, APIs, LLMs and the systems we use day in and day out. So, how can we crack this data code?By stepping back, we can incorporate the tools that already exist in applied linguistics used to crack the human language code into our approaches in how we tackle the data code challenge. Just because we call it data doesn't mean that it doesn't suffer from bias or the need for context. But by recognizing these linguistic challenges, and infusing that inside the data, we can create data code that can be cracked, data that tells us its biases, context, and purpose, and for who that data is actually useful to, and for whom it is not.If you are interested in data, and why understanding language and jargon can help you crack the data code, this book is for you. If you've had a data challenge and have struggled to find a way to understand it, the practical foundational principles inside can help you frame your problem in a different way. And in doing so, help you crack the data code.
Sustainability and Empowerment in the Context of Digital Libraries
The two-volume set LNCS 15493 and LNCS 15494 constitutes the refereed proceedings of the 26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024, held in Bandar Sunway, Malaysia, during December 4-6, 2024. The 19 full papers, 10 short papers, 7 posters and 2 practice papers presented were carefully reviewed and selected from 110 submissions. These papers are included in both volumes of the proceedings, grouped according to the following topics: Cultural Data Analysis, Design & Evaluation, Generative AI & Digital Libraries, Information Retrieval, Information Seeking & Use (Part I) and Knowledge Extraction, Scholarly Information Processing, and Social Media Analytics in Part II.
Duckdb: Up and Running
DuckDB, an open source in-process database created for OLAP workloads, provides key advantages over more mainstream OLAP solutions: It's embeddable and optimized for analytics. It also integrates well with Python and is compatible with SQL, giving you the performance and flexibility of SQL right within your Python environment. This handy guide shows you how to get started with this versatile and powerful tool. Author Wei-Meng Lee takes developers and data professionals through DuckDB's primary features and functions, best practices, and practical examples of how you can use DuckDB for a variety of data analytics tasks. You'll also dive into specific topics, including how to import data into DuckDB, work with tables, perform exploratory data analysis, visualize data, perform spatial analysis, and use DuckDB with JSON files, Polars, and JupySQL. Understand the purpose of DuckDB and its main functions Conduct data analytics tasks using DuckDB Integrate DuckDB with pandas, Polars, and JupySQL Use DuckDB to query your data Perform spatial analytics using DuckDB's spatial extension Work with a diverse range of data including Parquet, CSV, and JSON
Visual Analytics Using Tableau
DESCRIPTION Tableau is one of the leading business intelligence and data visualization tools that fulfill almost all the requirements for getting insights from huge data and solving complex business queries using simple and complex visualizations. This book covers all the features supported by Tableau, from basics to advanced.Master data storytelling with Tableau by learning to connect, clean, and analyze data from various sources. This book covers essential chart types like bar, line, and pie charts while introducing advanced features like filters, LOD expressions, and dual-axis charts. Create interactive dashboards by combining visualizations, adding controls, and customizing designs to engage your audience. Use storytelling techniques to present insights effectively. With advanced visualizations like combo and Gantt charts, this guide equips you with the skills to communicate data clearly and make informed, data-driven decisions.This book begins with very basic information that even a beginner can understand. Gradually, the book covers intermediate and advanced features of Tableau, so it can help readers of all levels become experts in Tableau.WHAT YOU WILL LEARN● Understand different types of data sources and how to connect them.● Learn techniques for cleaning and preparing data for visualization.● Perform calculations, aggregations, and level of detail (LOD) expressions.● Create both, simple and advanced visualizations to present data.● Design visually engaging dashboards and storyboards to answer business questions effectively.WHO THIS BOOK IS FORThis book is for students and professionals who want to learn and have a rewarding career in data visualization using Tableau. It is also for anyone who wants to become a data analyst, Tableau developer, business analyst, etc.
Graph Data Analytics
DESCRIPTION For most modern-day data, graph data models are proving to be advantageous since they facilitate a diverse range of data analyses. This has spiked the interest and usage of graph databases, especially Neo4j. We study Neo4j and cypher along with various plugins that augment database capabilities in terms of data types or facilitate applications in data science and machine learning using plugins like graph data science (GDS).A significant portion of the book is focused on discussing the structure and usage of graph algorithms. Readers will gain insights into well-known algorithms like shortest path, PageRank, or Label Propagation among others, and how one can apply these algorithms in real-world scenarios within a Neo4j graph.Once readers become acquainted with the various algorithms applicable to graph analysis, we transition to data science problems. Here, we explore how a graph's structure and algorithms can enhance predictive modeling, prediction of connections in the graph, etc. In conclusion, we demonstrate that beyond its prowess in data analysis, Neo4j can be tweaked in a production setup to handle large data sets and queries at scale, allowing more complex and sophisticated analyses to come to life.KEY FEATURES ● Utilizing graphs to improve search and recommendations on graph data models.● Understand GDS and Neo4j graph algorithms including cluster detection, link prediction, and centrality.● Complex problem-solving for predicting connections, application in ML pipelines and GNNs using graphs. WHAT YOU WILL LEARN● Understand Neo4j graphs and how to effectively query them with cypher.● Learn to employ graphs for effective search and recommendations around graph data.● Work with graph algorithms to solve problems like finding paths, centrality metrics, and detection of communities and clusters.● Explore Neo4j's GDS library through practical examples.● Integrate machine learning with Neo4j graphs, covering data prep, feature extraction, and model training.WHO THIS BOOK IS FORThe book is intended to serve as a reference for data scientists, business analysts, graph enthusiasts, and database developers and administrators who work or intend to work on extracting critical insights from graph-based data stores.
The Stories Behind the Numbers
Unlock the Power of Data to Make Smarter DecisionsIn Data-Driven Decisions: Making Better Decisions Using Insights, Roy Okonkwo demystifies the art of transforming raw numbers into meaningful narratives. Blending practical strategies with ethical considerations, this book empowers readers to leverage data for impactful decision-making in both personal and professional contexts. With accessible explanations of key concepts like predictive analytics, storytelling with data, and AI, Okonkwo offers tools to navigate the complexities of a data-driven world. Whether you're a novice or a seasoned professional, this book is your guide to making informed, ethical, and effective decisions.
The Semantic Web - Iswc 2024
This three-volume set constitutes the proceedings of the 23rd International Semantic Web Conference, ISWC 2023, held in Hanover, MD, USA, during November 11-15, 2024. The 44 full papers presented in these proceedings were carefully reviewed and selected from 155 submissions. This conference focuses on research on the Semantic Web, including benchmarks, knowledge graphs, tools and vocabularies.
The Semantic Web - Iswc 2024
This three-volume set constitutes the proceedings of the 23rd International Semantic Web Conference, ISWC 2023, held in Hanover, MD, USA, during November 11-15, 2024. The 44 full papers presented in these proceedings were carefully reviewed and selected from 155 submissions. This conference focuses on research on the Semantic Web, including benchmarks, knowledge graphs, tools and vocabularies.
Biostatistics with Python
Learn how to utilize biostatistics with Python for excelling in research and biomedical professions with practical exemplar projectsKey Features: - Bridge the gap between biostatistics and life sciences with Python- Work with practical exercises for real-world data analysis in biology and medicine- Access a portfolio of exemplar projects in the domains of biomedicine, biotechnology, and biology- Purchase of the print or Kindle book includes a free PDF eBookBook Description: This book leverages the author's decade-long experience in biostatistics and data science to simplify the practical use of biostatistics with Python. The chapters show you how to clean and describe your data effectively, setting a solid foundation for accurate analysis and proficiency in biostatistical inference to help you draw meaningful conclusions from your data through hypothesis testing and effect size analysis.The book walks you through predictive modeling to harness the power of Python to create robust predictive analytics that can drive your research and professional projects forward. You'll explore clinical biostatistics, learn how to design studies, conduct survival analysis, and synthesize evidence from multiple studies with meta-analysis - skills that are crucial for making informed decisions based on comprehensive data reviews. The concluding chapters will enhance your ability to analyze biological variables, enabling you to perform detailed and accurate data analysis for biological research. This book's unique blend of biostatistics and Python helps you find practical solutions that make complex concepts easy to grasp and apply.By the end of this biostatistics book, you'll have moved from theoretical knowledge to practical experience, allowing you to perform biostatistical analysis confidently and accurately.What You Will Learn: - Get to grips with the basics of biostatistics and Python programming- Clean and describe data using Python- Familiarize yourself with hypothesis testing and effect size analysis- Explore predictive modeling in biostatistics- Understand clinical study design and survival analysis- Gain a clear understanding of the meta-analysis of clinical research data- Analyze biological variables with Python- Discover practical data analysis for biological researchWho this book is for: This book is for life science professionals, researchers, biomedical professionals, and aspiring biostatisticians who want to integrate biostatistics into their work or research. A basic understanding of life sciences, biology, or medicine is recommended to fully benefit from this book.Table of Contents: - Introduction to Biostatistics- Getting Started with Python for Biostatistics- Exercise 1 - Cleaning and Describing Data Using Python- Part 1 Exemplar Project - Load, Clean, and Describe Diabetes Data in Python- Introduction to Python for Biostatistics- Biostatistical Inference Using Hypothesis Tests and Effect Sizes- Predictive Biostatistics Using Python- Part 2 Exercise - T-Test, ANOVA, and Linear and Logistic Regression- Biostatistical Inference and Predictive Analytics Using Cardiovascular Study Data- Clinical Study Design- Survival Analysis in Biomedical Research- Meta-Analysis - Synthesizing Evidence from Multiple Studies- Survival Predictive Analysis and Meta-Analysis Practice- Part 3 Exemplar Project - Meta-Analysis of Survival Data in Clinical Research- Understanding Biological Variables- Data Analysis Frameworks and Performance for Life Sciences Research- Part 4 Exercise - Performing Statistics for Biology Studies in Python
CI/CD Design Patterns
Learn CI/CD design patterns directly from industry leaders in this easy-to-follow guide, offering immediately applicable solutions for sustainable CI/CD adoptionKey Features: - Simplify CI/CD adoption with the help of practical examples, case studies, and best practices- Deploy market-ready solutions by implementing key components like pipelines, infrastructure, and release capabilities- Explore advanced design patterns, including integration with machine learning and generative AI- Purchase of the print or Kindle book includes a free PDF eBookBook Description: The fast-changing world of software development demands robust CI/CD solutions that go beyond traditional methods to address the complexities of modern pipelines. This practical guide presents proven design patterns to streamline your CI/CD processes, tackling pain points often overlooked by other resources. This book introduces continuous delivery design patterns to help practitioners and engineering teams design, adopt, and implement CI/CD. Drawing from decades of combined industry experience, the expert author team-including DevOps and cloud leader Garima Bajpai, industry expert Michel Schildmeijer, CI/CD framework creator Pawel Piwosz, and open source advocate Muktesh Mishra-provides invaluable insights from leading voices in the industry.The book lays a solid foundation by starting with the importance of CI/CD design patterns, components, and principles. You'll learn strategies for scaling CI/CD with a focus on performance, security, measurements, and pipeline auditability, along with infrastructure and release automation. The book also covers advanced design patterns that integrate machine learning, generative AI, and near real-time CI/CD processes.By the end of this book, you'll have a deep understanding of continuous delivery design patterns, a solid foundation for audits and controls, and be able to mitigate risks associated with the rapid integration of modern technology into the SDLC.What You Will Learn: - Use and manage CI/CD patterns effectively to design and implement CI/CD- Understand types of CI/CD design patterns and components- Discover relationships and interactions between tools within CI/CD- Implement well-tested development/design paradigms- Explore anti-patterns for CI/CD design pattern deployments- Master the taxonomy of assessment and audits for CI/CD design patterns- Get to grips with automation techniques for seamless CI/CD workflows- Gain insights into scaling CI/CD processes for large projectsWho this book is for: This design patterns book is for senior software developers, software architects, SRE architects, DevOps architects, cloud architects and platform engineering teams looking to speed up the development process by adopting well-tested, proven development/design paradigms for continuous delivery and its adoption. You are expected to have a basic understanding of CI/CD concepts and be familiar with the cloud ecosystem, DevOps principles, and CI/CD pipelines. Table of Contents- Foundations of CI/CD Design Patterns- Understanding Types of CI/CD Design Patterns and Their Components- Advancing on CI/CD Design Patterns - from Testing to Deployment- Business Outcome Alignment with CI/CD Design Patterns- Exploring Structural CI/CD Design Patterns- Deployment Strategies for Structural Design Patterns for CI/CD- Understanding Behavioral Design Patterns for CI/CD- Domain-Driven Design Patterns for Regulated Sectors- Applying Creational CI/CD Design Patterns- Understanding Deployment Strategies - Creational CI/CD with Cloud Providers(N.B. Please use the Read Sample option to see further chapters)
Business 101 for the Data Professional
This new book from bestselling author Jordan Morrow empowers data professionals to work and operate more effectively in an organizational setting, equipping them with key business knowledge and skills. It is vital for data professionals to understand the business needs and outcomes of the organizations they work for and collaborate effectively with non-technical colleagues. Business 101 for the Data Professional is the definitive guide for data professionals looking to upskill their organizational effectiveness and enhance their career prospects. From business strategy to different business areas such as product, marketing, sales and operations to data monetization and value, the book explains how these contribute to the business, and, crucially, the role that data plays in supporting them. Business 101 for the Data Professional explores how to navigate key challenges and pitfalls of data in business, such as bias, misuse of data and the balancing of data and technical debt. It shows how to build networking, influencing and relationship building skills and outlines the key principles of strong communication and data storytelling, explaining how these can be used to engage effectively with internal and external stakeholders such as clients. It is supported by examples, summaries of key learnings, and exercises at the end of each chapter to help readers detail their progress and map out their goals.
Business 101 for the Data Professional
This new book from bestselling author Jordan Morrow empowers data professionals to work and operate more effectively in an organizational setting, equipping them with key business knowledge and skills. It is vital for data professionals to understand the business needs and outcomes of the organizations they work for and collaborate effectively with non-technical colleagues. Business 101 for the Data Professional is the definitive guide for data professionals looking to upskill their organizational effectiveness and enhance their career prospects. From business strategy to different business areas such as product, marketing, sales and operations to data monetization and value, the book explains how these contribute to the business, and, crucially, the role that data plays in supporting them. Business 101 for the Data Professional explores how to navigate key challenges and pitfalls of data in business, such as bias, misuse of data and the balancing of data and technical debt. It shows how to build networking, influencing and relationship building skills and outlines the key principles of strong communication and data storytelling, explaining how these can be used to engage effectively with internal and external stakeholders such as clients. It is supported by examples, summaries of key learnings, and exercises at the end of each chapter to help readers detail their progress and map out their goals.
Managing Data as a Product
Learn everything you need to know to manage data as a product and shift toward a more modular and decentralized socio-technical data architecture to deliver business value in an incremental, measurable, and sustainable wayKey Features: - Leverage data-as-product to unlock the modular platform potential and fix flaws in traditional monolithic architectures- Learn how to identify, implement, and operate data products throughout their life cycle- Design and execute a forward-thinking strategy to turn your data products into organizational assets- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Traditional monolithic data platforms struggle with scalability and burden central data teams with excessive cognitive load, leading to challenges in managing technological debt. As maintenance costs escalate, these platforms lose their ability to provide sustained value over time. With two decades of hands-on experience implementing data solutions and his pioneering work in the Open Data Mesh Initiative, Andrea Gioia brings practical insights and proven strategies for transforming how organizations manage their data assets.Managing Data as a Product introduces a modular and distributed approach to data platform development, centered on the concept of data products. In this book, you'll explore the rationale behind this shift, understand the core features and structure of data products, and learn how to identify, develop, and operate them in a production environment. The book guides you through designing and implementing an incremental, value-driven strategy for adopting data product-centered architectures, including strategies for securing buy-in from stakeholders. Additionally, it explores data modeling in distributed environments, emphasizing its crucial role in fully leveraging modern generative AI solutions.By the end of this book, you'll have gained a comprehensive understanding of product-centric data architecture and the essential steps needed to adopt this modern approach to data management.What You Will Learn: - Overcome the challenges in scaling monolithic data platforms, including cognitive load, tech debt, and maintenance costs- Discover the benefits of adopting a data-as-a-product approach for scalability and sustainability- Navigate the complete data product lifecycle, from inception to decommissioning- Automate data product lifecycle management using a self-serve platform- Implement an incremental, value-driven strategy for transitioning to data-product-centric architectures- Optimize data modeling in distributed environments to enhance GenAI-based use casesWho this book is for: If you're an experienced data engineer, data leader, architect, or practitioner committed to reimagining your data architecture and designing one that enables your organization to get the most value from your data in a sustainable and scalable way, this book is for you. Whether you're a staff engineer, product manager, or a software engineering leader or executive, you'll find this book useful. Familiarity with basic data engineering principles and practices is assumed.Table of Contents- From Data as a Byproduct to Data as a Product- Data Products- Data Product-Centered Architectures- Identifying Data Products and Prioritizing Developments- Designing and Implementing Data Products- Operating Data Products in Production- Automating Data Product Lifecycle Management(N.B. Please use the Read Sample option to see further chapters)
Intelligent Data Engineering and Automated Learning - Ideal 2024
This two-volume set, LNCS 15346 and LNCS 15347, constitutes the proceedings of the 25th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024, held in Valencia, Spain, during November 20-22, 2024. The 86 full papers and 6 short papers presented in this book were carefully reviewed and selected from 130 submissions. IDEAL 2024 is focusing on Big Data Analytics and Privacy, Machine Learning & Deep Learning for Real-World Applications, Data Mining and Pattern Recognition, Information Retrieval and Management, Bio and Neuro-Informatics, and Hybrid Intelligent Systems and Agents.
Intelligent Data Engineering and Automated Learning - Ideal 2024
This two-volume set, LNCS 15346 and LNCS 15347, constitutes the proceedings of the 25th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024, held in Valencia, Spain, during November 20-22, 2024. The 86 full papers and 6 short papers presented in this book were carefully reviewed and selected from 130 submissions. IDEAL 2024 is focusing on Big Data Analytics and Privacy, Machine Learning & Deep Learning for Real-World Applications, Data Mining and Pattern Recognition, Information Retrieval and Management, Bio and Neuro-Informatics, and Hybrid Intelligent Systems and Agents.
The Data Science Handbook
Practical, accessible guide to becoming a data scientist, updated to include the latest advances in data science and related fields. Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The focus of The Data Science Handbook is on practical applications and the ability to solve real problems, rather than theoretical formalisms that are rarely needed in practice. Among its key points are: Readers of the third edition of Construction Graphics will also find: An emphasis on software engineering and coding skills, which play a significant role in most real data science problems. Extensive sample code, detailed discussions of important libraries, and a solid grounding in core concepts from computer science (computer architecture, runtime complexity, programming paradigms, etc.) A broad overview of important mathematical tools, including classical techniques in statistics, stochastic modeling, regression, numerical optimization, and more. Extensive tips about the practical realities of working as a data scientist, including understanding related jobs functions, project life cycles, and the varying roles of data science in an organization. Exactly the right amount of theory. A solid conceptual foundation is required for fitting the right model to a business problem, understanding a tool's limitations, and reasoning about discoveries. Data science is a quickly evolving field, and the 2nd edition has been updated to reflect the latest developments, including the revolution in AI that has come from Large Language Models and the growth of ML Engineering as its own discipline. Much of data science has become a skillset that anybody can have, making this book not only for aspiring data scientists, but also for professionals in other fields who want to use analytics as a force multiplier in their organization.
New Trends in Database and Information Systems
This book constitutes short papers, Doctoral Consortium and Workshops papers which were held in conjunction with the 28th European Conference on New Trends in Databases and Information Systems, ADBIS 2024, which took place in Bayonne, France, during August 28-31, 2024. The total of 28 full papers and 7 short papers presented in this book were carefully reviewed and selected from 103 submissions. They were organized in the following topical sections: Doctoral Consortium; 5th Workshop on Intelligent Data - From Data to Knowledge (DOING 2024); 3rd Workshop on Knowledge Graphs Analysis on a Large Scale (K-GALS 2024); 6th Workshop on Modern Approaches in Data Engineering and Information System Design (MADEISD 2024); 3rd Workshop on Personalization and Recommender Systems (PERS 2024); Access methods and query processing; discovery and data analysis; Machine Learning; large language models; and tutorials.
Knowledge and Systems Sciences
This book constitutes the refereed proceedings of the 23rd International Symposium on Knowledge and Systems Sciences, KSS 2024, held in Hobart, Tasmania, Australia, during November 16-17, 2024. The 23 full papers presented in this book were carefully reviewed and selected from 50 submissions. They are organized in the following topical sections: Complex networks and modeling; Opinion dynamics; Knowledge technologies and systems engineering; Knowledge management.
Big Data - Bigdata 2024
This book constitutes the refereed proceedings of the 13th International Conference on Big Data, BigData 2024, held as part of the Services Conference Federation, SCF 2024, in Bangkok, Thailand, during November 16-19, 2024. The 8 full papers and 1 short paper included in this book were carefully reviewed and selected from 21 submissions. They focus on various topics within the field of Data-based services such as Big Data Architecture, Big Data Modeling, Big Data As A Service, Big Data for Vertical Industries (Government, Healthcare, etc.), Big Data Analytics, Big Data Toolkits, Big Data Open Platforms, Economic Analysis, Big Data for Enterprise Transformation, Big Data in Business Performance Management, Big Data for Business Model Innovations and Analytics, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, and Big Data in Smart Planet Solutions. The papers have been organized under the following topical sections: Research track; Application track; and Short paper track.
Data Management
This guide illuminates the intricate relationship between data management, computer architecture, and system software. It traces the evolution of computing to today's data-centric focus and underscores the importance of hardware-software co-design in achieving efficient data processing systems with high throughput and low latency. The thorough coverage includes topics such as logical data formats, memory architecture, GPU programming, and the innovative use of ray tracing in computational tasks. Special emphasis is placed on minimizing data movement within memory hierarchies and optimizing data storage and retrieval. Tailored for professionals and students in computer science, this book combines theoretical foundations with practical applications, making it an indispensable resource for anyone wanting to master the synergies between data management and computing infrastructure.
Applying Color Theory to Digital Media and Visualization
Applying Color Theory to Digital Media and Visualization provides an overview of the application of color theory concepts to digital media and visualization. It highlights specific color concepts such as color harmony and data color schemes.
Data Science
This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached maturity.
Applying Color Theory to Digital Media and Visualization
Applying Color Theory to Digital Media and Visualization provides an overview of the application of color theory concepts to digital media and visualization. It highlights specific color concepts such as color harmony and data color schemes.
Data Science
This three-volume set CCIS 2213-2215 constitutes the refereed proceedings of the 10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024, held in Macau, China, during September 27-30, 2024. The 74 full papers and 3 short papers presented in these three volumes were carefully reviewed and selected from 249 submissions. The papers are organized in the following topical sections: Part I: Novel methods or tools used in big data and its applications; applications of data science. Part II: Education research, methods and materials for data science and engine; data security and privacy; big data mining and knowledge management. Part III: Infrastructure for data science; social media and recommendation system; multimedia data management and analysis.
