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From fundamental concepts to advanced analytics and machine learning techniques, you'll learn how to leverage interconnected data to drive actionable insights. Beyond theory, this book focuses on practical application, providing you with the hands-on skills needed to tackle real-world challenges. \n\nYou'll explore cutting-edge integrations with Large Language Models (LLMs) like ChatGPT to build advanced recommendation systems. With intuitive frameworks and interconnected data strategies, you'll elevate your analytical prowess. \n\nThis book offers a straightforward approach to mastering graph data science. With detailed explanations, real-world examples, and a dedicated GitHub repository filled with code examples, this book is an indispensable resource for anyone seeking to enhance their data practices with graph technology. Join us on this transformative journey across various industries, and unlock new, actionable insights from your data. \nWhat you will learn \n● Set up and utilize Python and Neo4j environments effectively for graph analysis. \n● Import and manipulate data within the Neo4j graph database using Cypher Query Language. \n● Visualize complex graph networks to gain insights into data relationships and patterns. \n● Enhance data analysis by integrating ChatGPT for context-rich data enrichment. \n● Explore advanced topics including Neo4j vector indexing and Retrieval-Augmented Generation (RAG). \n● Develop recommendation engines leveraging graph embeddings for personalized suggestions. \n● Build and deploy recommendation systems and fraud detection models using graph techniques. \n● Gain insights into the future trends and advancements shaping the field of graph data science. \nTable of Contents \n1. Introduction to Graph Data Science \n2. Getting Started with Python and Neo4j \n3. Import Data into the Neo4j Graph Database \n4. Cypher Query Language \n5. Visualizing Graph Networks \n6. Enriching Neo4j Data with ChatGPT \n7. Neo4j Vector Index and Retrieval-Augmented Generation (RAG) \n8. Graph Algorithms in Neo4j \n9. Recommendation Engines Using Embeddings \n10. Fraud Detection \nCLOSING SUMMARY \nThe Future of Graph Data Science \nIndex \nAbout the Author \nTimothy (Tim) Eastridge is an A.I. consultant known for his innovation and thought leadership in integrating knowledge graphs with Generative AI and Large Language Models (LLMs). His expertise in extracting actionable insights from complex datasets positions him as a leader in transforming data into understandable formats. Tim’s innovative solutions have resulted in billions of dollars of suspicious activity reports (SARs) for a major bank related to the Paycheck Protection Program (PPP). He continues this work as a consultant to the Pandemic Response Accountability Committee (PRAC), leading the team in the identification, prioritization, and indictment of fraudsters using a combination of unsupervised machine learning and recommendation systems.\u003cp\u003e\u003cstrong\u003eNote:\u003c\/strong\u003e This is a digital eBook. You will receive instant access after purchase. 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The Deitels cover the most current topics and applications to prepare you for your subsequent courses and career. Interesting, real-world problems use real-world datasets, with a focus on software engineering and program clarity. Jupyter Notebooks supplements, downloadable Python source code, and hundreds of hands-on examples, exercises and projects provide opportunities for you to test and apply your skills as you learn.\u003c\/p\u003e \u003cp\u003eRevel® empowers you to actively participate in learning. More than a digital textbook, Revel delivers an engaging blend of author content, media, and assessment. With Revel, you can read and practice in one continuous experience anytime, anywhere, on any device.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eNOTE:\u003c\/strong\u003e Revel is a fully digital delivery of Pearson content. This ISBN is for the Revel access code only. In addition to this access code, you will need a course invite link provided by your instructor to register for and use Revel.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eNote:\u003c\/strong\u003e This is a digital eBook. You will receive instant access after purchase. No physical book will be shipped.\u003c\/p\u003e","brand":"VitalSource","offers":[{"title":"Default Title","offer_id":50596267458846,"sku":"9780138221331R365","price":139.64,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_fdaf6d88-579f-45da-bb85-f511d6a01f3a.jpg?v=1770652900"},{"product_id":"practical-data-science-environments-with-python-and-r-9789349887558","title":"Practical Data Science Environments with Python and R","description":"From Beginner to Practitioner: A Practical Path to Learning Data Science \n\nKey Features \n● Build production-ready data science environments from scratch. \n● Learn Python and R through complete, real-world workflows for cleaning, visualizing, and modeling data. \n● Learn real-world and practical workflows used by modern data organizations. \n\nBook Description \nData science often fails beginners not because of complex algorithms, but because setting up the right tools, environments, and workflows is confusing and poorly explained. Practical Data Science Environments with Python and R fills that gap by focusing on the practical foundations required to work effectively in real data science settings. \n\nYou begin by developing a clear understanding of the data science landscape, including how different programming languages, tools, and platforms are used across analytics and machine learning workflows. As you advance, you learn how to import structured and unstructured data, apply systematic cleaning and transformation techniques, and perform exploratory analysis to understand data behavior. \n\nYou will implement and evaluate foundational models while learning how to organize code, manage versions with Git, and follow workflows used in professional data teams. The final chapters connect these skills to industry use cases, advanced topics, and next steps, preparing you to continue growing beyond the basics. \n\nWhat you will learn \n● Build complete, reproducible data science environments from scratch. \n● Prepare raw data through structured cleaning and transformation processes. \n● Apply Python and R workflows for end-to-end data analysis tasks. \n● Visualize data to identify patterns and communicate analytical insights \n. ● Implement and evaluate foundational machine learning models. \n● Manage data science projects using industry-standard version control workflows. \n\nTable of Contents \n1. An Overview of Data Science \n2. Comparing Programming Languages and Various Environments \n3. Setting Up Data Science Environment \n4. Importing and Cleaning Data in Python and R \n5. Data Wrangling and Manipulation in Python and R \n6. Data Visualization in Python and R \n7. Introduction to Data Science Algorithms \n8. Implementing Machine Learning Models \n9. Version Control with Git \n10. Data Science and Analytics in Industry \n11. Advanced Topics and Next Steps \nIndex \n\nAbout the Author \nAstha Puri is a senior data science leader at a Fortune 10 healthcare organization, where she builds large-scale recommendation systems that shape millions of user journeys each day. Over the past nine years, she has honed her expertise in machine learning, personalization, and applied AI through roles at Oracle, Twilio, and CVS Health. \n\nRohan Mathur Rohan is a Senior Software Engineer in Nvidia’s autonomous vehicles division, specializing in large-scale data analytics, machine learning, and fleet data strategies. His work helps shape the data systems that power perception and decision-making for next-generation autonomous driving.\u003cp\u003e\u003cstrong\u003eNote:\u003c\/strong\u003e This is a digital eBook. You will receive instant access after purchase. 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The book covers basic concepts like data types, operators, and statements that enable the reader to solve simple problems. As functions are the core of any programming, a detailed illustration of defining \u0026amp; invoking functions and recursive functions is covered.\u003c\/p\u003e\n\u003cp\u003eBuilt-in data structures of Python, such as strings, lists, tuples, sets, and dictionary structures, are discussed in detail with examples and exercise problems. Files are an integrated part of programming when dealing with large data. File handling operations are illustrated with examples and a case study at the end of the chapter.\u003c\/p\u003e\n\u003cp\u003eWidely used Python packages for data science, such as Pandas, Data Visualization libraries, and regular expressions, are discussed with examples and case studies at the end of the chapters. The book also contains a chapter on SQLite3, a small relational database management system of Python, to understand how to create and manage databases.\u003c\/p\u003e\n\u003cp\u003eAs AI applications are becoming popular for developing intelligent solutions to various problems, the book includes chapters on Machine Learning and Deep Learning. They cover the basic concepts, example applications, and case studies using popular frameworks such as SKLearn and Keras on public datasets\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eNote:\u003c\/strong\u003e This is a digital eBook. You will receive instant access after purchase. No physical book will be shipped.\u003c\/p\u003e","brand":"VitalSource","offers":[{"title":"120 Day Option","offer_id":50596273455390,"sku":"9783031524738R120","price":42.0,"currency_code":"USD","in_stock":true},{"title":"180 Day Option","offer_id":50596273488158,"sku":"9783031524738R180","price":45.5,"currency_code":"USD","in_stock":true},{"title":"30 Day Option","offer_id":50596273357086,"sku":"9783031524738R30","price":21.0,"currency_code":"USD","in_stock":true},{"title":"60 Day Option","offer_id":50596273389854,"sku":"9783031524738R60","price":28.0,"currency_code":"USD","in_stock":true},{"title":"90 Day Option","offer_id":50596273422622,"sku":"9783031524738R90","price":35.0,"currency_code":"USD","in_stock":true},{"title":"Perpetual","offer_id":50596273324318,"sku":"9783031524738","price":69.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_2ef6c473-fed9-4ac9-abdb-f8ee4db7f1f2.jpg?v=1770652913"},{"product_id":"financial-data-science-with-python-9781637428269","title":"Financial Data Science with Python","description":"\u003cp\u003e\u003cb\u003eIn today’s finance industry, data-driven decision-making is essential. \u003ci\u003eFinancial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning\u003c\/i\u003e bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThis book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges.\u003c\/p\u003e\u003cp\u003eBeginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context.\u003c\/p\u003e\u003cp\u003eA key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity.\u003c\/p\u003e\u003cp\u003eWhether you’re a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eNote:\u003c\/strong\u003e This is a digital eBook. You will receive instant access after purchase. 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It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality. This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science. Key Features: Applies a modern, computational approach to working with data Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues Teaches the fundamentals of some of the most important tools in the Python data-science stack Provides a basic, but rigorous, introduction to Probability and its application to Statistics Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material\u003cp\u003e\u003cstrong\u003eNote:\u003c\/strong\u003e This is a digital eBook. You will receive instant access after purchase. 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This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI). This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts. Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.\u003cp\u003e\u003cstrong\u003eNote:\u003c\/strong\u003e This is a digital eBook. You will receive instant access after purchase. No physical book will be shipped.\u003c\/p\u003e","brand":"VitalSource","offers":[{"title":"180 Day Option","offer_id":50596278829342,"sku":"9781040275153R180","price":37.95,"currency_code":"USD","in_stock":true},{"title":"365 Day Option","offer_id":50596278796574,"sku":"9781040275153R365","price":44.85,"currency_code":"USD","in_stock":true},{"title":"Perpetual","offer_id":50596278763806,"sku":"9781040275153","price":68.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_bf3ab079-9af3-433e-905d-3ef52a034164.jpg?v=1770652928"},{"product_id":"ultimate-parallel-and-distributed-computing-with-julia-for-data-science-9789391246860","title":"Ultimate Parallel and Distributed Computing with Julia For Data Science","description":"Unleash Julia’s power: Code Your Data Stories, Shape Machine Intelligence!\n\nKey Features\n● Comprehensive Learning Journey from fundamentals of Julia ML to advanced techniques.\n● Immersive practical approach with real-world examples, exercises, and scenarios, ensuring immediate application of acquired knowledge.\n● Delve into the unique features of Julia and unlock its true potential to excel in modern ML applications.\n\nBook Description\nThis book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results.\n\nThe handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.\n\nWhat you will learn\n● Master Julia ML Basics to gain a deep understanding of Julia's syntax, variables, and functions.\n● Efficient Data Handling with Julia arrays and DataFrames for streamlined and insightful analysis.\n● Develop expertise in both basic and advanced statistical models for informed decision-making through Statistical Modeling.\n● Achieve Machine Learning Proficiency by confidently implementing ML algorithms using MLJ.jl and MLBase.jl.\n● Apply Bayesian Inference Skills with Turing.jl for advanced modeling techniques.\n● Optimize workflows using Julia's Parallel Processing Capabilities and Distributed Computing for efficient and scalable data processing.\n\nTable of Contents\n1. Julia In Data Science Arena\n2. Getting Started with Julia\n3. Features Assisting Scaling ML Projects\n4. Data Structures in Julia\n5. Working With Datasets In Julia\n6. Basics of Statistics\n7. Probability Data Distributions\n8. Framing Data in Julia\n9. Working on Data in DataFrames\n10. Visualizing Data in Julia\n11. Introducing Machine Learning in Julia\n12. Data and Models\n13. Bayesian Statistics and Modeling\n14. Parallel Computation in Julia\n15. Distributed Computation in Julia\nIndex\n\nAbout the Author\nNabanita Dash, a results-oriented Research Engineer, holds a BTech in Computer Science and Engineering from IIIT, India. A former Head of the Programming Club, she blends technology passion with leadership. With a foundation in Mathematics, Physics, Chemistry, and English, she excels in her role as a Research Engineer at Antimodular Research in Montreal. Specializing in deep learning artworks and 2D\/3D data analysis, Nabanita previously volunteered for MLPack and OpenMined, contributing to C++ data management and privacy research. 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This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You’ll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data.   You’ll see how to use Mathematica, where data management and mathematical computations are needed. 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The book includes a case study analyzing the data required to predict the results of presidential elections in the state of Maine for 2016 and 2020. In addition to the presidential elections, the book provides real-life examples, including analyses of stock, oil, and gold prices, crime, marketing, and healthcare. You will see data science in action and how easily it can be performed using complicated tasks and visualizations in SAS Studio. You will learn, step by step, how to perform visualizations, including creating maps. In most cases, you will not need a line of code as you work with the SAS Studio graphical user interface. The book includes explanations of the code that SAS Studio generates automatically. You will learn how to edit this code to perform more complicated advanced tasks. What You Will Learn   Become familiar with the SAS Studio IDE. How to create essential visualizations. Know the fundamental statistical analysis required in most data science and analytics reports. Clean the most common dataset problems Learn linear and logistic regression for data prediction and analysis. Write programs in SAS. How to analyze data and get insights from it for decision-making. Learn character, numeric, date, time, and datetime functions and typecasting. Who This Book Is For A general audience of people who are new to data science, students, and data analysts and scientists who are new to SAS. No prior programming or statistical knowledge is required.\u003cp\u003e\u003cstrong\u003eNote:\u003c\/strong\u003e This is a digital eBook. You will receive instant access after purchase. 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