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Python for Data Analytics: A Beginner's Guide for Excel Users to Clean, Analyze, and Visualize Business Data with Python, Pandas, and Real-World Projects (Data Analytics and Python Learning)
English | October 12, 2026 | ASIN: B0HMRD539V | 526 pages | Epub | 4.17 MB
Stop wrestling with spreadsheets. Start analyzing with Python.Do you spend hours copying, pasting, filtering, and rebuilding the same Excel reports every week? Python for Data Analytics shows Excel users how to do the same work faster, repeatably, and at any scale. It uses Python and pandas, and it assumes no programming experience.You already think in rows, columns, and formulas. This step-by-step beginner's guide builds on that knowledge. It takes you from the spreadsheet you know to the code that automates it. You will import, clean, combine, summarize, and visualize real business data. Then you will turn the reports you rebuild by hand into scripts you can re-run whenever the data changes.INSIDE, YOU WILL LEARN HOW TO:Translate everyday Excel tasks into pandas. Recreate VLOOKUP-style lookups, filters, sorting, and pivot tables in Python.Clean messy data with confidence. Remove duplicates, handle missing values, and fix inconsistent text and dates.Merge, group, and summarize data. Combine multiple files and turn raw tables into clear business answers.Build clear charts with matDescriptionlib. Visualize sales, profit, and trends, then export your results back to Excel.Automate repetitive reporting. Replace manual, error-prone spreadsheet work with reusable Python scripts.Finish with real-world projects and a capstone. Practice on business datasets provided in the appendices, so you can apply every skill right away.WHO THIS BOOK IS FORExcel users who want to move beyond formulas, macros, and manual reportingBusiness analysts, financial analysts, and operations professionalsAccountants, marketers, sales teams, and managers who work with data every dayStudents and career changers starting a path in data analyticsAnyone who wants a practical, beginner-friendly introduction to Python for data analysisWHY THIS BOOK IS DIFFERENTMost Python books assume you are a programmer. This one assumes you are an Excel user. Each concept is introduced through spreadsheet tasks you already know, such as lookups, pivot tables, and filters. You learn Python by solving the problems you face at work, instead of memorizing abstract syntax.Whether you want to save hours on weekly reports, make your analysis more accurate, or add Python and pandas to your resume, this guide gives you a clear path from Excel to data analytics.Ready to automate your spreadsheets and analyze data like a pro? Buy your copy today and start turning Excel work into Python scripts you can re-run.

Python: Step by Step: CSCI 100 - Learn Python Through Challenges, Experimentation, and AI-Assisted Problem Solving
English | October 5, 2026 | ASIN: B0HLYCG6WY | 1584 pages | Epub | 716.97 KB
Most programming books show you completed code. This book asks you to complete the thinking.Python: Step by Step - Volume 1 is a challenge-based introduction to Python for readers who want to understand how programs are investigated, built, tested, and improved-not simply how finished code is copied.Across 31 structured chapters and 434 practical exercises, you will progress from your first Python expression to variables, decisions, loops, collections, functions, exception handling, and persistent text files.Inside This Volume:* 31 progressive chapters* 434 practical exercises* A consistent five-part challenge system* Partial guidance covering approximately 30-70% of the solution path* Incomplete code that must be analyzed, completed, and repaired* Testing, debugging, and improvement activities* Guided use of AI Agents for research, hints, error analysis, and reviewEvery challenge follows the Half-Solution Strategy. You receive part of the explanation, key concepts, incomplete code, rules, and testing requirements. Your responsibility is to investigate the remaining steps, experiment with possible approaches, test the results, and explain why the final solution works.An AI Agent can assist your research and reasoning, but it does not replace your responsibility to think, test, and verify.This volume covers the Python interpreter and REPL, output, expressions, variables, numbers, strings, Boolean logic, user input, formatting, conditions, pattern matching, loops, lists, tuples, sets, dictionaries, slicing, mutability, comprehensions, functions, scope, flexible arguments, documentation, exceptions, and text files.This book is designed for complete beginners, independent learners, professionals exploring automation, researchers who need reliable scripts, and readers preparing for advanced work in software development, data analysis, cybersecurity, finance, or artificial intelligence.The goal is not merely to memorize Python syntax. It is to develop the ability to approach unfamiliar problems, learn from failure, evaluate evidence, and build reliable solutions independently.Begin with the first challenge-and learn Python by solving it.

Python Projects with uv and Ruff: A Step-by-Step Guide to Modern Python Packaging, Dependency Management, Testing, Typing, pyproject.toml, and CI/CD
English | 5 Oct. 2026 | ASIN: B0HLYQ99QT | 225 pages | PDF | 11.36 MB
Writing Python code is one thing. Turning that code into a clean, organized, testable, maintainable project can feel like an entirely different challenge.Virtual environments, dependencies, pyproject.toml, linting, testing, type checking, packaging, and CI/CD can quickly become overwhelming—especially when every tool seems to come with its own rules and commands. Python Projects with uv and Ruff brings those pieces together into one clear, practical workflow. Python_Projects_with_uv_and_RuffYou do not need previous experience with uv, Ruff, modern Python packaging, static type checking, or CI/CD. The book introduces these skills gradually through a continuous hands-on project. A basic understanding of Python syntax, functions, modules, and terminal commands is helpful, making this ideal for readers who know the fundamentals of Python but are new to professional development tooling. Python_Projects_with_uv_and_RuffInstead of overwhelming you with theory, each chapter builds on the same working project so you can understand not just what to do, but why each step matters.Key FeaturesStep-by-step guidance using a continuous real-world Python projectPractical commands and configuration introduced only when neededModern workflows built around uv, Ruff, pytest, mypy, and pyproject.tomlClear explanations of dependency management and reproducible environmentsHands-on packaging, testing, CI/CD, and release automationPractical migration guidance for older pip, venv, requirements, and Poetry workflowsTroubleshooting checklists and verification steps throughoutA learning approach that treats failed commands, broken tests, and configuration mistakes as normal parts of development—not reasons to give up Python_Projects_with_uv_and_RuffWhat You Will LearnYou will learn how to:Create and organize modern Python projects with uvManage Python versions, dependencies, environments, and lockfilesStructure and maintain pyproject.tomlImprove code quality with Ruff linting and formattingBuild reliable test suites with pytestAdd practical static typing with mypyBuild wheels and source distributionsPrepare and publish Python packages safelyAutomate testing and releases with GitHub ActionsMigrate legacy Python projects to modern workflowsTroubleshoot dependency, testing, packaging, and CI/CD problemsMaintain a professional, reproducible development workflow over time Python_Projects_with_uv_and_RuffWho This Book Is ForThis book is for Python learners, self-taught programmers, students, junior developers, and working developers who already understand basic Python but want to move beyond simple scripts and learn how real projects are structured, tested, packaged, automated, and maintained.Table of ContentsChapter 1: Modern Python Project FoundationsChapter 2: Dependencies, Environments, and ReproducibilityChapter 3: Structuring Projects with pyproject.tomlChapter 4: Code Quality with RuffChapter 5: Testing Python Projects with pytestChapter 6: Practical Static Typing with mypyChapter 7: Building and Distributing Python PackagesChapter 8: CI/CD with GitHub ActionsChapter 9: Migration, Troubleshooting, and Long-Term Maintenance Python_Projects_with_uv_and_RuffIf you are ready to move from simply writing Python code to building projects that are cleaner, more dependable, and easier to maintain, this book gives you a practical path forward—one manageable step at a time.

Python Programming on Raspberry Pi 5: A Beginner's Guide to Writing Code and Building Useful Applications
English | October 10, 2026 | ASIN: B0HMK4MQ96 | 506 pages | Epub | 575.23 KB
Have you ever wondered how a small computer can become a powerful tool for learning Python, automating everyday tasks, controlling electronic devices, and building practical applications? Have you wanted to write your own programs but felt uncertain about where to begin, which tools to install, or how to turn your ideas into working projects? What if you could move beyond simply using a computer and start creating software that interacts with the physical world?Python Programming on Raspberry Pi 5: A Beginner's Guide to Writing Code and Building Useful Applications by Jamie T. Guzman is designed to help you take those first steps with greater confidence. Whether you are completely new to programming or have experimented with Python and want to apply your knowledge to real projects, this guide offers a practical introduction to programming with the Raspberry Pi 5.Where should you begin? How do you prepare your computer, configure your development environment, and write your first Python program? How can you understand variables, functions, conditions, loops, and data structures without feeling overwhelmed by unfamiliar terminology? This book helps you develop those essential skills while showing you how they fit into useful applications.You do not need to be an experienced programmer to start learning how to build meaningful projects. With a structured approach and consistent practice, you can develop the knowledge needed to write readable code, solve problems, and gradually take on more challenging tasks.What happens when you want your programs to do more than display messages on a screen? Imagine collecting readings from sensors, controlling compatible electronic components, recording information in databases, communicating across a network, or automating repetitive activities. The Raspberry Pi 5 makes these possibilities accessible, while Python provides a flexible language for bringing your ideas to life.Inside, you will explore essential Python programming concepts, Linux terminal skills, project organisation, file handling, databases, networking, debugging, and testing. You will also discover how GPIO programming, electronic interfaces, cameras, and other hardware features can support practical applications. As your understanding grows, you can explore automation systems, environmental monitoring, web dashboards, and projects that combine software with physical devices.Are you concerned about making mistakes while learning? Every programmer encounters errors, unexpected results, and confusing behaviour. Learning how to interpret error messages, test assumptions, inspect program output, and correct faulty logic is an important part of becoming capable. This guide emphasises the practical skills that help you approach problems methodically rather than giving up when your code does not work as expected.The goal is not merely to teach you Python syntax, but to help you understand how to use programming to create useful solutions. You will build a foundation for independent experimentation, more organised development, and projects that can grow alongside your skills.Whether your interests include electronics, home automation, robotics, data collection, networking, or general software development, the knowledge you gain can provide a starting point for further exploration.Why keep postponing the projects you have imagined? Why remain a passive technology user when you can begin learning to create your own solutions?Take the first step toward practical programming today. Get your copy of Python Programming on Raspberry Pi 5 by Jamie T. Guzman, prepare your workspace, open your editor, and start turning your curiosity into working code. Your next useful application could begin with a single line of Python.

Python Data Structures and Algorithms: A Clear, Step-by-Step Guide to Core Concepts and Problem Solving
English | October 5, 2026 | ASIN: B0HLYPML3S | 369 pages | Epub | 4.87 MB
Python Data Structures and Algorithms is a practical, step-by-step guide to understanding how algorithms work, how data structures support them, and how to turn problem statements into reliable Python solutions.Rather than simply presenting a catalog of algorithms, this book focuses on the reasoning behind good implementations. You will learn how to define clear contracts, choose appropriate data structures, analyze time and space requirements, test edge cases, and recognize when an algorithm's assumptions no longer hold.The book begins with the foundations of algorithmic problem solving and gradually progresses to more advanced topics. Along the way, you will explore:How to move from a problem statement to a well-defined algorithmPython objects, references, mutation, copying, and reliable function contractsTime and space complexity, including practical ways to reason about growthSequences, arrays, indexing, slicing, and efficient access patternsHash maps and sets, including collision behavior and practical tradeoffsStacks, queues, linked structures, and their common applicationsRecursion, backtracking, and techniques for controlling recursive stateLinear and binary search and how ordering changes what is possibleSorting algorithms and how to select a method based on the problemBinary search trees, ordered maps, heaps, and priority queuesGraph representations, breadth-first search, and depth-first searchShortest-path algorithms, including Dijkstra's algorithm and Bellman–FordDisjoint-set structures and their role in connectivity problemsGreedy algorithms, their assumptions, and situations where greedy choices failDynamic programming, including state design, transitions, reconstruction, and optimizationCommon problem-solving patterns that help you recognize reusable algorithmic structuresText processing, tries, string searching, normalization, and matching techniquesHow to combine multiple data structures and algorithms into a complete working system-Throughout the book, concepts are explained with concrete examples before introducing more formal notation. Code is accompanied by discussions of correctness, edge cases, assumptions, and complexity so that you can understand not only what an implementation does, but why it works and when it should be used.You will also encounter practical exercises designed to encourage independent reasoning and implementation. Worked examples, visual explanations, complexity analysis, and an integrated task-planning project help connect individual techniques to larger programming problems.A companion package provides additional example modules, tests, synthetic data, expected outputs, and practical material for working with the book's examples. The book itself remains useful without the download, with the principal examples explained directly in its pages.This book is designed for readers who already have basic Python knowledge and want to develop stronger data-structures and algorithms skills. You should be comfortable with functions, conditionals, loops, and basic lists. No previous algorithms course or advanced mathematics is required.By the end, you should be better prepared to look at an unfamiliar programming problem, identify the important constraints, select an appropriate approach, reason about its correctness and efficiency, test it against meaningful edge cases, and recognize when a different solution would be more appropriate.If you want to move beyond memorizing algorithm names and develop a practical understanding of how data structures and algorithms support reliable Python problem solving, this book provides a clear path from fundamental concepts to complete algorithmic systems.

Python AI Agents for Beginners
English | October 11, 2026 | ASIN: B0HMN9139R | 450 pages | PDF | 8.14 MB
Python AI Agents for Beginners is a practical guide to understanding and building AI agents with Python.AI agents are changing how software applications can solve problems, use tools, work with information, maintain context, and complete multi-step tasks. This book takes a beginner-friendly approach and explains the concepts step by step, using practical Python examples, tables, diagrams, workflows, and hands-on explanations.Whether you are beginning your journey into AI development or already have some Python experience, this book provides a structured path from basic AI-agent concepts to complete agent-based applications.What You Will LearnUnderstand what AI agents are and how they workLearn the core components of an AI agentSet up a Python environment for AI developmentConnect Python applications to AI modelsWork with prompts, responses, and model interactionsBuild and use AI tools and function callingManage conversation state, context, and memoryConnect agents with web search and external informationUnderstand retrieval and knowledge-based workflowsBuild planning and multi-step agent systemsWork with files, data, and practical toolsUnderstand Retrieval-Augmented Generation (RAG)Build agents with persistent knowledge and memoryDesign multi-agent AI systemsCreate a complete real-world AI-agent workflowTest AI applications and handle errorsUnderstand security considerations for AI agentsPrepare agents for deploymentMonitor, evaluate, and improve AI applicationsLearn by Following Practical ExamplesInstead of focusing only on theory, this book uses Python code examples and visual explanations to make AI-agent concepts easier to understand.Throughout the book, you will find:Practical Python examplesStep-by-step explanationsArchitecture diagramsWorkflow diagramsComparison tablesTool-calling examplesMemory and RAG examplesMulti-agent workflowsTesting and security conceptsPractical exercises and development ideasThe examples gradually become more advanced, allowing you to understand how individual components can be combined into larger AI-agent systems.From Python Fundamentals to AI AgentsYou will begin by understanding the basic architecture of an AI agent and then progress through models, tools, memory, external data, planning, RAG, and multi-agent workflows.By the later chapters, you will see how these components can work together in a complete application:User → Agent → Planning → Tools → Knowledge → Memory → Processing → Final ResponseThe goal is not simply to provide code to copy. It is to help you understand why each component is used, how the pieces connect, and how you can adapt the ideas to your own projects.Who This Book Is ForThis book is designed for:Beginners interested in AI agentsPython programmers exploring AI developmentStudents learning practical AI programmingDevelopers interested in LLM applicationsReaders learning about RAG and AI memoryAnyone who wants to understand modern AI-agent architectureNo advanced AI background is required to begin. A basic familiarity with Python will make the practical examples easier to follow.Learn Python. Understand AI agents. Build practical AI-powered applications.

Python & SQL Quick-Start Guide: Learn Data Analysis from Zero in 30 Days, with Pandas, Joins, Projects and Practice Exercises
English | October 11, 2026 | ASIN: B0HMMJYH2Z | 89 pages | Epub | 626.51 KB
Want a data job but don't know where to start?This beginner-friendly guide teaches you Python and SQL, the two most in-demand data skills, in plain English. No coding experience needed.Each chapter explains the idea with a real-life example, walks through the code line by line, and ends with practice exercises. Answers are included.Inside you'll learn how to:Set up Python and write your first programUse lists, dictionaries and functionsClean and analyse data with PandasWrite SQL queries, joins, CTEs and window functionsCombine Python and SQL in one projectBuild a portfolio project for GitHub and your CVPrepare for data analyst interviewsIncludes a 30-day study plan, cheat sheets, a glossary and common interview questions with simple answers.

Prompt Intelligence
English | October 6, 2026 | ASIN: B0HM3GRYM3 | 304 pages | Epub | 910.81 KB
PROMPT INTELLIGENCEAn Advanced Manual of Systematic Engineering with ChatGPT to Automate Your BusinessStop just "trying things" with ChatGPT and start working with a system.Most people use ChatGPT like a search engine that talks: they type a sentence, get an acceptable answer, and start from scratch again the next day. Prompt Intelligence offers a different way of working—one where every instruction becomes a professional asset: designed, verified, and reusable, capable of saving you and your business real hours. This advanced manual, updated for 2026, takes you from setting up your environment all the way to orchestrating AI agents, with a clear method, practical examples, and templates you can start adapting today.What You'll LearnHow a language model works under the hood, what it can and can't do, and how to avoid its errors and hallucinations.How to build professional prompts using a proprietary framework, reasoning techniques, and advanced dialogue methodologies.How to audit and improve your prompts through reverse engineering.How to work with documents, data, images, and voice: text extraction, data analysis, and in-depth research using web search.How to create AI agents and multi-agent workflows to automate repetitive tasks.How to protect privacy and manage the risks of using AI in your organization.How to turn your prompts into business assets: libraries, standards, and teamwork.How to apply all of this to office work, technical and creative content, learning, and industry-specific cases (legal and advisory services, health and wellness, education, and personal finance), always with clear boundaries.Includes the Master Prompt Library: 30 ready-to-copy templates organized by work area—fundamentals and verification, office and productivity, business and data, communication, creativity, agents and governance, and learning.A Manual That Never Goes Out of Date: AI moves fast. That's why the final chapter, "The Living Manual," gives you protocols for checking with the tool itself which content still holds up, so you can keep what works for you even after the book itself has fallen behind.Who This Book Is For: Freelancers, entrepreneurs, executives, office teams, and professionals who already use ChatGPT and know they could be getting a lot more out of it.An independent work, not affiliated with, sponsored by, or endorsed by OpenAI.

Prompt Engineering: Image & Video: The Only Guide You'll Ever Need for AI Image and Video Generation, From First Prompt to Professional Output.
English | 22 Sept. 2026 | ASIN: B0HKQ2X2Z8 | 380 pages | Epub | 77.41 MB

Production MLOps for Quant Trading: Building and Monitoring Infrastructure on Kubernetes
English | September 14, 2026 | ASIN: B0HJSLGXK1 | 565 pages | Epub | 2.93 MB
Reactive PublishingBuild, deploy, and scale institutional-grade machine learning pipelines designed specifically for high-frequency and quantitative trading environments.In quantitative finance, model latency, feature staleness, and unmonitored drift directly translate to financial loss. Production MLOps for Quant Trading delivers a practical, hands-on blueprint for building resilient, continuous-delivery ML systems on Kubernetes-tailored to the zero-tolerance demands of live market execution.This book skips theoretical high-level overviews to focus on the concrete engineering challenges quant developers, MLOps engineers, and financial data scientists face daily:Automated CI/CD Pipelines: Design and execute robust testing, validation, and deployment automation for live algorithmic models.Low-Latency Feature Stores: Implement real-time and batch feature management to eliminate training-serving skew in dynamic markets.Model Drift & Anomaly Detection: Detect concept drift, covariate shift, and signal decay before bad trades execute.Kubernetes Infrastructure: Orchestrate distributed training, auto-scaling, and self-healing workloads on enterprise cloud environments.Packed with production-ready architectures, actionable code patterns, and real-world trade-offs, this guide provides the exact technical roadmap needed to bridge the gap between backtested alpha and live trading execution.