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Data Mesh
English | 2024 | ISBN: 2940180613165 | Pages: 100 | EPUB (True) | 506.02 KB
In a world where data reigns supreme, organizations are grappling with the challenges posed by traditional data management approaches. "Unleashing the Power of Data Mesh" is your definitive guide to navigating this evolving landscape and harnessing the true potential of your data assets.
This comprehensive book takes you on a transformative journey through the revolutionary paradigm of Data Mesh, a decentralized approach that reshapes how we think about, manage, and leverage data. From its foundational principles to real-world implementation strategies, this guide equips you with the knowledge and tools needed to embrace the future of data architecture.
Key Features:
1.
Evolution of Data:
Trace the historical evolution of data management and understand why traditional approaches fall short in today's data-driven environment.
2.
Data Mesh Principles:
Delve into the core principles of Data Mesh - domain-oriented decentralized data ownership, treating data as a product, self-serve data infrastructure, and federated computational governance.
3.
Practical Implementation:
Gain practical insights into identifying and defining data domains, adopting a product-centric mindset, and building a self-serve data infrastructure that empowers domain teams.
4.
Governance and Collaboration:
Explore the delicate balance between autonomy and collaboration through federated computational governance, ensuring data quality, security, and compliance.
5.
Technical Landscape:
Navigate the technical aspects of Data Mesh, including patterns like Domain Data Mesh and Federated Query Mesh, and understand how it integrates with modern technologies like microservices and cloud platforms.
6.
Real-world Case Studies:
Learn from the experiences of organizations that have successfully implemented Data Mesh, understanding their challenges, best practices, and the impact on their data capabilities.
7.
Step-by-Step Implementation:
Receive a step-by-step guide on how to implement Data Mesh within your organization, addressing common challenges and paving the way for a successful transition.
8.
Future of Data:
Look beyond the horizon and explore the future of data, discovering emerging trends and understanding how Data Mesh continues to evolve as a catalyst for data-driven innovation.
"Unleashing the Power of Data Mesh" is not just a book; it's your roadmap to a data-driven future. Whether you're a seasoned data professional, a business leader, or someone passionate about the transformative power of data, this guide empowers you to revolutionize your approach to data architecture and propel your organization into a new era of data excellence. Embrace the Data Mesh revolution and unleash the full potential of your data assets!
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Data Modeling and Design for Beginners
English | 2024 | ISBN: 2940180795595 | Pages: 125 | EPUB (True) | 326.15 KB
Are you curious about the world of data and how it shapes the digital landscape around us? Dive into the foundations of data modeling with our comprehensive guide, "Data Modeling and Design for Beginners" Tailored for beginners, this book demystifies the intricate world of data modeling and design, providing a clear and accessible roadmap for anyone eager to understand and harness the potential of structured information.
Key Features:
1.
Foundations of Data Modeling:
Grasp the essential concepts of entities, attributes, relationships, and keys that form the backbone of data models. Lay the groundwork for your journey into the world of information systems.
2.
Types of Data Models:
Explore conceptual, logical, and physical data models, understanding how they work together to create a comprehensive view of your data. Get hands-on experience with popular tools like ERwin and Microsoft Visio.
3.
Tools and Techniques:
Learn about the tools and techniques used in the data modeling process, with practical insights into best practices. Gain the confidence to apply your knowledge in real-world scenarios.
4.
Normalization and Denormalization:
Navigate the delicate balance between normalization and denormalization. Understand how to structure your data for efficiency while maintaining integrity.
5.
Advanced Concepts:
Delve into advanced topics such as subtypes and supertypes, aggregation, and metadata, enhancing your ability to design robust and flexible data models.
6.
Real-world Applications:
Explore case studies of successful data modeling implementations across various industries. Adapt your skills to meet the dynamic challenges of the business landscape.
7.
Future Trends:
Gain insights into the future of data modeling, including its role in big data, artificial intelligence, and machine learning. Stay ahead of the curve with evolving standards and technologies.
8.
Practical Exercises:
Reinforce your learning with practical exercises and hands-on projects. Build a simple data model from scratch, applying the concepts you've acquired throughout the book.
"Data Modeling and Design for Beginners" is your gateway to understanding and harnessing the incredible potential of information systems. Equip yourself with the knowledge and skills needed to navigate the ever-expanding landscape of data modeling and design. Embark on a journey to unlock the power within your data!
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Data Pipelines with Apache Airflow, Second Edition (MEAP V12) by Julian de Ruiter, Ismael Cabral, Kris Geusebroek, Daniel van der Ende, Bas Harenslak
English | 2025 | ISBN: 9781633436374 | 645 pages | EPUB, PDF | 51.6 MB
Simplify, streamline, and scale your data operations with data pipelines built on Apache Airflow.
Apache Airflow provides a batteries-included platform for designing, implementing, and monitoring data pipelines. Building pipelines on Airflow eliminates the need for patchwork stacks and homegrown processes, adding security and consistency to the process. Now in its second edition, Data Pipelines with Apache Airflow teaches you to harness this powerful platform to simplify and automate your data pipelines, reduce operational overhead, and seamlessly integrate all the technologies in your stack.
In Data Pipelines with Apache Airflow, Second Edition you'll learn how to
Master the core concepts of Airflow architecture and workflow design
Schedule data pipelines using the Dataset API and time tables, including complex irregular schedules
Develop custom Airflow components for your specific needs
Implement comprehensive testing strategies for your pipelines
Apply industry best practices for building and maintaining Airflow workflows
Deploy and operate Airflow in production environments
Orchestrate workflows in container-native environments
Build and deploy Machine Learning and Generative AI models using Airflow
Data Pipelines with Apache Airflow has empowered thousands of data engineers to build more successful data platforms. This new second edition has been fully revised to cover the latest features of Apache Airflow, including the Taskflow API, deferrable operators, and Large Language Model integration. Filled with real-world scenarios and examples, you'll be carefully guided from Airflow novice to expert.
about the bookData Pipelines with Apache Airflow, Second Edition teaches you how to build and maintain effective data pipelines. You'll master every aspect of directed acyclic graphs (DAGs)-the power behind Airflow-and learn to customize them for your pipeline's specific needs. Part reference and part tutorial, each technique is illustrated with engaging hands-on examples, from training machine learning models for generative AI to optimizing delivery routes. You'll explore common Airflow usage patterns, including aggregating multiple data sources and connecting to data lakes, while discovering exciting new features such as dynamic scheduling, the Taskflow API, and Kubernetes deployments.
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Data pre-processing for machine learning in Python (Your Data Teacher Books Book 2)
English | 2022 | ASIN: B0B5961KTR | 86 pages | PDF | 2.94 MB
In this book, the author shows the practical use of Python programming language to perform pre-processing tasks in machine learning projects. Pre-processing is the set of transformations to be applied to a dataset before it can be used to train a machine learning model. It's a very important phase of a data science pipeline because a wrong pre-processing will give a very poor performance of the model, while a good pre-processing is able to make the model learn properly.
The pre-processing transformations shown in this book are:
Data cleaning
Encoding of the categorical variables (one-hot encoding and ordinal encoding)
Principal Component Analysis
Scaling (normalization, standardization, robust scaling)
Binarizing
Binning
Power transformations
Filter-based feature selection
Oversampling using SMOTE
All the transformations are described both in theory and in practice using Python programming language and its powerful scikit-learn library.
About the author
Gianluca Malato was born in 1986 and he is an Italian data scientist, teacher and author. In 2010, he received his Master's Degree cum laude in Theoretical Physics of disordered systems at "La Sapienza" University of Rome (thesis advisors: Giorgio Parisi and Tommaso Rizzo). He has been working for years as a data architect, project manager, data analyst and data scientist for a large Italian company.
He is the founder of yourdatateacher.com, an online school where he teaches Data Science, Machine Learning, R, Python and SQL language using online courses and individual online training programs.
He has published several articles about Data Science on his blog yourdatateacher.com and on Towards Data Science online publication (towardsdatascience.com). He received the "Top Writer" mention on Medium.com in the "Artificial Intelligence" category for his articles.
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Data Preparation for AI and Analytics (MEAP V03)
English | 2025 | ISBN: 9781633435742 | 327 pages | PDF,EPUB | 14.53 MB
Excellent data quality makes for excellent data insights!
The Data Preparation for AI and Analytics is a practical guide to cleaning and organizing the messy, tangled data you'll encounter in the real world. You'll learn techniques you can use to get your data ready for almost any task-from reports and analysis, to building machine learning models.
In Data Preparation for AI and Analytics you'll:
Understand the importance of data quality
Use AI to clean and prepare data
Take advantage of Python and visual tools like Alteryx
Apply the right data preparation technique for the right outcome
Data Preparation for AI and Analytics is for anyone who works with data, from seasoned data architects to marketing pros and business analysts. It presents data preparation methods with clear language and concrete examples. You'll explore tried-and-true approaches along with emerging generative AI techniques. You'll especially appreciate the insights into automation and data governance.
about the book
Data Preparation for AI and Analytics teaches you to tackle the challenges you'll face as you work with data. You'll master popular data wrangling tools like Python and Alteryx. Complex data prep concepts are broken down into clear, manageable steps and fully illustrated with engaging data sets-including data on the Titanic disaster, rating video games, sentiment analysis of Los Angeles restaurant recommendations, and more. The book is packed with vital advice for complex tasks, including merging multiple data sets, alerting systems for data quality, and scaling data preparation into large cloud-based pipelines. Learn universal techniques for data enrichment and transformation, and specialized approaches optimized for machine learning, analytics, and creating AI.
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Data: Principles To Practice - Volume 2: Exploring Big Data, Data Science, Machine Learning, Data Analysis, Visualization, Security, and Ethical Insights for Organizational Success
English | 2024 | ASIN: B0DP4MCWMK | 334 pages | Epub | 6.24 MB
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Data Quality Assurance
English | 2024 | ISBN: 2940180742926 | Pages: 120 | EPUB (True) | 424.24 KB
In the digital era, where data is as valuable as currency, maintaining impeccable data quality is not just an option-it's a necessity. "Data Quality Assurance: Strategies, Tools, and Insights for Exceptional Data Quality " serves as an indispensable resource for professionals who understand the critical role data plays in their organization's success and are seeking to implement or enhance their data quality initiatives.
Across industries, the quality of data directly influences decision-making, operational efficiency, customer satisfaction, and ultimately, profitability. However, ensuring high data quality is a complex challenge that involves more than just technology. It requires a deep understanding of data quality dimensions, a robust framework for data governance, and a culture that values data accuracy and integrity.
This book offers a holistic view of Data Quality Assurance (DQA), covering everything from the fundamental principles of data quality, through to advanced techniques for managing data in the era of Big Data and AI. Readers will learn to identify and address common data quality issues, select and implement the right tools and technologies, and establish metrics for ongoing monitoring and improvement.
Structured to provide a clear and comprehensive path through the intricacies of DQA, the guide includes:
Practical Strategies
: Step-by-step instructions for developing and executing an effective data quality assurance plan.
Tools and Technologies
: An unbiased overview of the leading tools and technologies, along with guidance for integration and optimization.
Real-World Case Studies
: Insightful analyses of successful DQA implementations, highlighting the challenges faced and lessons learned.
Future Trends
: A look ahead at the evolving landscape of data management, including the impact of artificial intelligence and machine learning on data quality.
Whether you are a data management professional aiming to refine your organization's data quality practices, a business leader seeking to understand the impact of data quality on your bottom line, or a student of information technology or business analytics, "Data Quality Assurance: Strategies, Tools, and Insights for Exceptional Data Quality " is your go-to resource for mastering Data Quality Assurance in today's data-driven world.
Unlock the full potential of your data. Start ensuring excellence today.
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Data Science and Applications: Proceedings of ICDSA 2024, Volume 4
English | 2025 | ISBN: 9789819626472 | 581 pages | True PDF,EPUB | 116.46 MB
This book gathers outstanding papers presented at the 5th International Conference on Data Science and Applications (ICDSA 2024), organized by Soft Computing Research Society (SCRS) and Malaviya National Institute of Technology Jaipur, India, from July 17 to 19, 2024. The book is divided into four volumes, and it covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
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Data Science and Applications: Proceedings of ICDSA 2024, Volume 2
English | 2025 | ISBN: 981962178X | 883 Pages | PDF EPUB (True) | 90 MB
This book gathers outstanding papers presented at the 5th International Conference on Data Science and Applications (ICDSA 2024), organized by Soft Computing Research Society (SCRS) and Malaviya National Institute of Technology Jaipur, India, from 17 to 19 July 2024. The book is divided into four volumes, and it covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
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Data Science for Agricultural Innovation and Productivity
English | 2024 | ISBN: B0CVN75313 | Pages: 337 | EPUB (True) | 4.14 MB
Data Science for Agricultural Innovation and Productivity explores the transformation of agriculture through data-driven practices. This comprehensive book delves into the intersection of data science and farming, offering insights into the potential of big data analytics, machine learning, and IoT integration.
Readers will find a wide range of topics covered in 10 chapters, including smart farming, AI applications, hydroponics, and robotics. Expert contributors, including researchers, practitioners, and academics in the fields of data science and agriculture, share their knowledge to provide readers with up-to-date insights and practical applications. The interdisciplinary emphasis of the book gives a well-rounded view of the subject.
With real-world examples and case studies, this book demonstrates how data science is being successfully applied in agriculture, inspiring readers to explore new possibilities and contribute to the ongoing transformation of the agricultural sector. Sustainability and future outlook are the key themes, as the book explores how data science can promote environmentally conscious agricultural practices while addressing global food security concerns.
Key Features:
? Focus on data-driven agricultural practices
? Comprehensive coverage of modern farming topics with an interdisciplinary perspective
?? Expert insights
? Sustainability and future outlook
? Highlights practical applications
Data Science for Agricultural Innovation and Productivity is an essential resource for researchers, data scientists, farmers, agricultural technologists, students, educators, and anyone with an interest in the future of farming through data-driven agriculture.
Readership
Researchers, data scientists, farmers, agricultural technologists, students, educators, and general readers.
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Data Science for Beginners. Book 1
English | 2024 | ISBN: 2940180901170 | Pages: 150 | EPUB (True) | 404.27 KB
This book is an accessible and comprehensive guide that demystifies the world of data science and empowers readers to embark on their journey of understanding and utilizing the power of data. Whether you're a complete novice or have a basic understanding of the subject, this book will equip you with the foundational knowledge and practical skills needed to navigate the ever-evolving landscape of data science.
As you delve deeper into the book, you'll discover the essential components of the data science process, including data collection, data cleaning, data exploration, and data visualization. You'll learn how to extract valuable insights from data using statistical analysis and machine learning algorithms. The author provides step-by-step instructions and practical exercises that reinforce your understanding of these concepts, allowing you to apply them to real-world problems.
What sets this book apart is its emphasis on hands-on learning. Through engaging projects and case studies, you'll have the opportunity to tackle real-world datasets and develop your data science skills in a practical context. You'll learn how to clean messy data, perform exploratory data analysis, build predictive models, and effectively communicate your findings to non-technical stakeholders.
Whether you're a student, a professional transitioning into data science, or simply curious about the field, tis book equips you with the knowledge and tools necessary to kick-start your data science journey. By the end of this book, you'll have a solid foundation in data science and be well-prepared to take on more advanced topics in the field.
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Data Science for Neuroimaging: An Introduction
English | 2023 | ISBN: B0CJ68X5DD | Pages: 393 | PDF (True) | 12.69 MB
Data science methods and tools-including programming, data management, visualization, and machine learning-and their application to neuroimaging research
As neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage, and analyze datasets at unprecedented scales. Concerns about reproducibility and increased rigor in reporting of scientific results also demand higher standards of computational practice. This book offers neuroimaging researchers an introduction to data science, presenting methods, tools, and approaches that facilitate automated, reproducible, and scalable analysis and understanding of data. Through guided, hands-on explorations of openly available neuroimaging datasets, the book explains such elements of data science as programming, data management, visualization, and machine learning, and describes their application to neuroimaging. Readers will come away with broadly relevant data science skills that they can easily translate to their own questions.
* Fills the need for an authoritative resource on data science for neuroimaging researchers
* Strong emphasis on programming
* Provides extensive code examples written in the Python programming language
* Draws on openly available neuroimaging datasets for examples
* Written entirely in the Jupyter notebook format, so the code examples can be executed, modified, and re-executed as part of the learning process
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Data Science for Water Utilities: Data as a Source of Value (Chapman & Hall/CRC Data Science Series)
English | 2023 | ASIN: B0C36DL9ZN | 504 Pages | Epub | 7.89 MB
This addition to the Data Science Series introduces the principles of data science and the R language to the singular needs of water professionals. The book provides unique data and examples relevant to managing water utility and is sourced from the author's extensive experience.
Data Science for Water Utilities: Data as a Source of Value is an applied, practical guide that shows water professionals how to use data science to solve urban water management problems. Content develops through four case studies. The first looks at analysing water quality to ensure public health. The second considers customer feedback. The third case study introduces smart meter data. The guide flows easily from basic principles through code that, with each case study, increases in complexity. The last case study analyses data using basic machine learning.
Readers will be familiar with analysing data but do not need coding experience to use this book. The title will be essential reading for anyone seeking a practical introduction to data science and creating value with R.
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Data Science for Web3: A comprehensive guide to decoding blockchain data with data analysis basics and machine learning cases
English | 2023 | ISBN: B0C6QQBLZ8 | Pages: 555 | EPUB (True) | 7.72 MB
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.
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Data Science: Foundations and Hands-on Experience:
Handling Economic, Spatial, and Multidimensional Data with R
English | 2025 | ISBN: 9819646820 | 650 Pages | PDF EPUB (True) | 67 MB
This book will take readers from foundational concepts to practical applications, enabling them to transform raw data into meaningful insights. It covers key skills such as data collection, cleaning, organization, exploration, analysis, and impactful presentation-core competencies for navigating today's data-rich landscape.
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Data Science in 30 Days: The Complete Beginner's Guide
English | 2025 | ISBN: B0DXBD67RD | Pages: 179 | Epub | 4.01 MB
Data Science in 30 Days: The Complete Beginner's Guide is the ultimate hands-on roadmap for anyone looking to master data science for dummies and transition into the world of data-driven decision-making. Designed to take you from an absolute beginner to a confident data practitioner in just 30 days, this book provides a structured, easy-to-follow approach to learning data science for beginners efficiently.
With step-by-step lessons, real-world examples, and hands-on exercises, this book is perfect for those looking for a Python data science book that balances theory with practical applications. Whether you are a student, professional, or hobbyist, you'll find everything you need to get started with data science projects with Python and apply it in various domains such as business analytics, finance, artificial intelligence, and big data processing.
What You'll Learn in 30 Days:
✔ The fundamentals of data science, including data collection, cleaning, and preprocessing
✔ How to use Python, NumPy, Pandas, and MatDescriptionlib for Python for data science tasks
✔ Exploratory data analysis (EDA) techniques to uncover insights from data
✔ Introduction to statistics, probability, and hypothesis testing for data science for business applications
✔ Supervised and unsupervised machine learning models, including regression, classification, and clustering
✔ Working with databases, SQL queries, and big data tools like Apache Spark
✔ Advanced topics such as data science for finance, deep learning, NLP, time series analysis, and model deployment
Why This Book Stands Out:
✔ A structured day-by-day learning plan to keep you on track
✔ Practical exercises and coding challenges to reinforce each concept
✔ Covers both beginner and intermediate topics, making it ideal for continuous learning
✔ A bonus section with cheat sheets, common errors, and interview questions
If you're looking for the best Python data science book that breaks down complex concepts into digestible lessons, this book is for you. Whether you're interested in data science projects with Python, preparing for a data science bootcamp, or simply want to explore data science for business in an easy-to-understand manner, this book provides a complete, practical guide.
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Data Science in Healthcare
English | 2024 | ISBN: 2940180745613 | Pages: 165 | EPUB (True) | 377.30 KB
In an era where data is the lifeblood of progress, healthcare is not untouched by the wave of transformation brought by data science. "Data Science in Healthcare: Transforming the Future of Medicine" delves deep into this revolution, illuminating the profound impact data science is having on the healthcare landscape.
Healthcare, a field built on the foundations of compassion and expertise, is embracing the power of data and artificial intelligence to enhance patient care, streamline operations, and push the boundaries of medical research. This book is your comprehensive guide to understanding and navigating the ever-expanding world of data science in healthcare.
From the fundamental principles of data collection, preprocessing, and exploratory data analysis, to advanced techniques in machine learning, deep learning, and natural language processing, this book equips you with the knowledge and tools needed to harness the potential of healthcare data. You will explore the integration of data science with electronic health records, medical imaging, and telemedicine, and gain insights into the ethical and legal considerations that govern the use of healthcare data.
With real-world case studies and success stories, you will witness the practical applications of data science in disease diagnosis, personalized medicine, drug discovery, and beyond. Whether you are a healthcare professional seeking to optimize patient outcomes or a data scientist eager to make a difference in the field of medicine, this book provides the essential knowledge to bridge the gap.
"Data Science in Healthcare" doesn't just dwell on the present; it looks to the future of healthcare, from AI-driven innovations to the challenges that lie ahead. It offers a roadmap to navigate the dynamic landscape, ensuring you are prepared for the exciting possibilities and the ethical responsibilities that come with them.
As healthcare evolves into a data-driven industry, this book is your essential companion, providing the insights and wisdom to leverage the power of data science for the well-being of individuals and society. Discover how data science is transforming healthcare and get ready to be a part of the journey to a healthier, more connected world.
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Data Science Unveiled: Navigating the Future with Analytical Precision
English | 2025 | ASIN: B0DX7HRKVW | 296 Pages | Epub | 521.84 KB
"Data Science Unveiled: Navigating the Future with Analytical Precision" is a comprehensive guide that explores the fundamentals and advanced concepts of data science, offering practical insights for both beginners and experienced practitioners. This book covers 20 detailed chapters that progress from basic principles to cutting-edge developments in the field.
Readers will learn about:
Core concepts and methodologies in data science
Essential tools and technologies, including Python, R, and SQL
Advanced topics like machine learning, deep learning, and natural language processing
Big data analytics and cloud computing solutions
Data visualization techniques and best practices
Ethical considerations and governance in data science
Building a professional portfolio and career development
The book combines theoretical knowledge with practical applications, featuring real-world examples and detailed explanations of complex concepts. It addresses current trends and future developments, including the role of AI, quantum computing, and automation in shaping the future of data science.
Whether you're a student, professional, or business leader, this book provides valuable insights into harnessing the power of data science for better decision-making and innovation. Written in an accessible style while maintaining technical rigor, it serves as both a learning resource and a reference guide for navigating the evolving landscape of data science.
Ideal for:
Data science students and practitioners
Business analysts and decision-makers
IT professionals transitioning to data science
Anyone interested in understanding the impact of data science on modern business and society
This comprehensive guide empowers readers to understand, implement, and leverage data science techniques in their professional endeavors, preparing them for a future driven by data-based insights and decisions.
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Data Science with Julia (The Language of Data Science)
English | 2025 | ISBN: B0F2Z7D3CB | Pages: 623 | PDF | 673.29 KB
Reactive Publishing
Data Science with Julia
By Hayden Van Der Post
Experience data science reimagined-faster, leaner, and more elegant-with the Julia programming language.
While Python and R have dominated the data science conversation for years, Julia is the breakthrough language designed from the ground up for high-performance numerical computing. Data Science with Julia is your hands-on guide to mastering the full data science pipeline using this modern, lightning-fast tool.
From data wrangling to modeling to visualization and deployment, this book equips you to:
Harness Julia's speed for massive datasets and real-time analytics
Perform advanced statistical analysis with native packages
Build machine learning models using Flux and MLJ.jl
Visualize data interactively with Makie and Descriptions.jl
Deploy Julia-based data apps and scripts in production workflows
Written for analysts, researchers, and developers tired of clunky workarounds and sluggish runtime, this is a pragmatic, performance-first approach to doing data science in the 2020s.
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Data Science with Python for Beginners
English | 2024 | ISBN: 2940180774347 | Pages: 120 | EPUB (True) | 405.98 KB
In this accessible and engaging book, you'll traverse the fundamentals of Python programming, seamlessly transitioning into the realm of data science. From setting up your Python environment to mastering essential libraries like NumPy, Pandas, and MatDescriptionlib, each chapter builds upon the last, providing a scaffolded approach to learning.
Explore the art of data wrangling with Pandas, gaining proficiency in cleaning, preprocessing, and merging datasets. Demystify the complexities of exploratory data analysis (EDA) and statistical concepts, empowering you to make informed decisions and draw valuable insights from your data.
Dive into the heart of machine learning, where you'll grasp the basics of model training, evaluation, and hyperparameter tuning using the powerful Scikit-Learn library. Discover the significance of feature engineering, delve into time series analysis, and unravel the potential of big data with Apache Spark and Dask.
As you progress, the book navigates through advanced tools for data visualization, such as Descriptionly and Dash, allowing you to create stunning and interactive visualizations that bring your data to life. Uncover the ethical considerations in data science, addressing bias, privacy, and security, and cap off your journey with real-world projects and case studies, cementing your newfound skills.
"Data Science with Python for Beginners" is not just a book; it's your companion in the evolving landscape of data science. Packed with practical examples, exercises, and hands-on projects, this guide instills confidence in beginners and ignites a passion for data exploration and analysis. Whether you aspire to make data-driven decisions or embark on a career in data science, this book is your key to unlocking the doors of insight and innovation. Let Python be your guide as you unravel the vast possibilities of data science!
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