Mastering Python For Data Engineering
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Mastering Python For Data Engineering
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Author : Thompson Carter
language : en
Publisher: Independently Published
Release Date : 2025-01-09
Mastering Python For Data Engineering written by Thompson Carter and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-09 with Computers categories.
Mastering Python for Data Engineering: Transform and Manipulate Big Data with Python Unlock the true potential of Python for big data manipulation and engineering with Mastering Python for Data Engineering. This comprehensive guide is designed to help data engineers and aspiring professionals transform, process, and analyze massive datasets efficiently. By leveraging Python's powerful libraries and tools, you'll be equipped to build scalable data pipelines, integrate various data sources, and optimize data workflows for performance. From basic data wrangling to advanced engineering techniques, this book provides a practical, hands-on approach to mastering data engineering tasks with Python, making it the perfect companion for anyone aiming to work with big data. What You'll Learn: The fundamentals of Python for data engineering, including essential libraries like pandas, NumPy, and Dask. Building efficient data pipelines for ETL (Extract, Transform, Load) processes. Working with large datasets using parallel and distributed processing tools like Apache Spark and Dask. Integrating data from various sources, such as databases, APIs, and streaming data. Data transformation and cleaning techniques to prepare data for analysis. Optimizing performance and scaling data workflows with Python. With step-by-step guidance and practical examples, Mastering Python for Data Engineering will show you how to handle data at scale, integrate different data sources, and build automated data workflows that are crucial for modern data infrastructure. Dive into the world of data engineering with Python and learn how to transform raw data into actionable insights while building systems that can handle vast amounts of information.
Master Python Data Engineering With Virtual Ai Tutoring
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Author : Diego Rodrigues
language : en
Publisher: Diego Rodrigues
Release Date : 2024-11-19
Master Python Data Engineering With Virtual Ai Tutoring written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Business & Economics categories.
Imagine acquiring a book and, as a bonus, gaining access to a 24/7 AI-assisted Virtual Tutoring to personalize your learning journey, reinforce knowledge, and receive mentorship for developing and implementing real projects... ...Welcome to the Revolution of Personalized Learning with AI-Assisted Virtual Tutoring! Discover " MASTER PYTHON DATA ENGINEERING: From Fundamentals to Advanced Applications with Virtual AI Tutoring," the essential guide for professionals and enthusiasts who want to master data engineering with Python. This innovative manual, written by Diego Rodrigues, an author with over 140 titles published in six languages, combines high-quality content with the advanced technology of IAGO, a virtual tutor developed and hosted on the OpenAI platform. Innovative Features: Personalized Learning: IAGO adapts the content to your knowledge level, offering detailed explanations and personalized exercises. Immediate Feedback: Receive corrections and suggestions in real time, speeding up your learning process. Interactivity and Engagement: Interact with the tutor via text or voice, making learning more dynamic and motivating. Project Development Mentorship: Get practical guidance to develop and implement real projects, applying the knowledge gained. Total Flexibility: Access the tutor anywhere, anytime, whether on a desktop, notebook, or smartphone with web access. Take advantage of the Limited-Time Launch Promotional Price! Don't miss the opportunity to transform your learning journey with an innovative and effective method. This book has been carefully structured to meet your needs and exceed your expectations, ensuring you are prepared to face challenges and seize opportunities in the field of data engineering. Open the book sample and discover how to access the select club of cutting-edge technology professionals. Take advantage of this unique opportunity and achieve your goals! TAGS: data engineering automation science big Pandas NumPy Dask SQLAlchemy web scraping BeautifulSoup Scrapy APIs ETL DataOps Data Lakes Data Warehouses AWS Google Cloud Microsoft Azure Hadoop Spark machine learning artificial intelligence data pipelines data visualization Matplotlib Seaborn data analysis relational databases NoSQL MongoDB Apache Airflow Kafka real-time data governance data security compliance mentorship Diego Rodrigues Tableau Power BI Snowflake Informatica Alation Talend Apache Flink Jupyter Notebooks DevOps Databricks Cloudera Hortonworks Teradata IBM Cloud Oracle Cloud Salesforce SAP HANA ElasticSearch Redis Kubernetes Docker Jenkins GitHub GitLab Continuous Integration Continuous Deployment CI/CD digital transformation predictive analysis business intelligence IoT Internet of Things smart cities connected health Industry 4.0 fintechs retail education marketing competitive intelligence data science automated testing custom reports operational efficiency Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques skills cybersecurity industry global cybersecurity trends Kali Linux tools education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR
A Practical Guide To Data Engineering
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Author : Pedram Ariel Rostami
language : en
Publisher: Starseed AI
Release Date :
A Practical Guide To Data Engineering written by Pedram Ariel Rostami and has been published by Starseed AI this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.
"A Practical Guide to Machine Learning and AI: Part-I" is an essential resource for anyone looking to dive into the world of artificial intelligence and machine learning. Whether you're a complete beginner or have some experience in the field, this book will equip you with the fundamental knowledge and hands-on skills needed to harness the power of these transformative technologies. In this comprehensive guide, you'll embark on an engaging journey that starts with the basics of data engineering. You'll gain a solid understanding of big data, the key roles involved, and how to leverage the versatile Python programming language for data-centric tasks. From mastering Python data types and control structures to exploring powerful libraries like NumPy and Pandas, you'll build a strong foundation to tackle more advanced concepts. As you progress, the book delves into the realm of exploratory data analysis (EDA), where you'll learn techniques to clean, transform, and extract insights from your data. This sets the stage for the heart of the book - machine learning. You'll explore both supervised and unsupervised learning, diving deep into regression, classification, clustering, and dimensionality reduction algorithms. Along the way, you'll encounter real-world examples and hands-on exercises to reinforce your understanding and apply what you've learned. But this book goes beyond just the technical aspects. It also addresses the ethical considerations surrounding machine learning, ensuring you develop a well-rounded perspective on the responsible use of these powerful tools. Whether your goal is to jumpstart a career in data science, enhance your existing skills, or simply satisfy your curiosity about the latest advancements in AI, "A Practical Guide to Machine Learning and AI: Part-I" is your comprehensive companion. Prepare to embark on an enriching journey that will equip you with the knowledge and skills to navigate the exciting frontiers of artificial intelligence and machine learning.
Mastering Python Data Visualization
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Author : Kirthi Raman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-10-27
Mastering Python Data Visualization written by Kirthi Raman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-27 with Computers categories.
Generate effective results in a variety of visually appealing charts using the plotting packages in Python About This Book Explore various tools and their strengths while building meaningful representations that can make it easier to understand data Packed with computational methods and algorithms in diverse fields of science Written in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuse Who This Book Is For If you are a Python developer who performs data visualization and wants to develop existing knowledge about Python to build analytical results and produce some amazing visual display, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed. What You Will Learn Gather, cleanse, access, and map data to a visual framework Recognize which visualization method is applicable and learn best practices for data visualization Get acquainted with reader-driven narratives and author-driven narratives and the principles of perception Understand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with it Explore with various visualization choices how Python can be very useful in computation in the field of finance and statistics Get to know why Python is the second choice after Java, and is used frequently in the field of machine learning Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js Discover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environment In Detail Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences. This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical linear, and non-linear regression; clustering and classification, but also in helping you understand the aesthetics and best practices of data visualization. The book concludes with interesting examples such as social networks, directed graph examples in real-life, data structures appropriate for these problems, and network analysis. By the end of this book, you will be able to effectively solve a broad set of data analysis problems. Style and approach The approach of this book is not step by step, but rather categorical. The categories are based on fields such as bioinformatics, statistical and machine learning, financial computation, and linear algebra. This approach is beneficial for the community in many different fields of work and also helps you learn how one approach can make sense across many fields
Mastering Python For Bioinformatics
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Author : Ken Youens-Clark
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-05-05
Mastering Python For Bioinformatics written by Ken Youens-Clark and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-05 with Computers categories.
Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained, usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software. Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and tools including linters, formatters, type checkers, and tests to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming. Create command-line Python programs to document and validate parameters Write tests to verify refactor programs and confirm they're correct Address bioinformatics ideas using Python data structures and modules such as Biopython Create reproducible shortcuts and workflows using makefiles Parse essential bioinformatics file formats such as FASTA and FASTQ Find patterns of text using regular expressions Use higher-order functions in Python like filter(), map(), and reduce()
Mastering Data Engineering And Analytics With Databricks A Hands On Guide To Build Scalable Pipelines Using Databricks Delta Lake And Mlflow
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Author : Manoj Kumar
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-09-30
Mastering Data Engineering And Analytics With Databricks A Hands On Guide To Build Scalable Pipelines Using Databricks Delta Lake And Mlflow written by Manoj Kumar and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-30 with Computers categories.
Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges Key Features● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow.● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action.● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines.● Offers proven strategies to optimize workflows and avoid common pitfalls. Book DescriptionIn today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. What you will learn● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases.● Optimize query performance and efficiently manage cloud resources for cost-effective data processing.● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation.● Build and deploy real-time data processing solutions for timely and actionable insights.● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. Table of ContentsSECTION 11. Introducing Data Engineering with Databricks2. Setting Up a Databricks Environment for Data Engineering3. Working with Databricks Utilities and ClustersSECTION 24. Extracting and Loading Data Using Databricks5. Transforming Data with Databricks6. Handling Streaming Data with Databricks7. Creating Delta Live Tables8. Data Partitioning and Shuffling9. Performance Tuning and Best Practices10. Workflow Management11. Databricks SQL Warehouse12. Data Storage and Unity Catalog13. Monitoring Databricks Clusters and Jobs14. Production Deployment Strategies15. Maintaining Data Pipelines in Production16. Managing Data Security and Governance17. Real-World Data Engineering Use Cases with Databricks18. AI and ML Essentials19. Integrating Databricks with External Tools Index
Data Engineering With Python Sql 2025 Edition
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Author : Diego Rodrigues
language : en
Publisher: Diego Rodrigues
Release Date : 2025-01-01
Data Engineering With Python Sql 2025 Edition written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-01 with Business & Economics categories.
Welcome to "DATA ENGINEERING WITH PYTHON AND SQL: Build Scalable Data Pipelines - 2025 Edition," a comprehensive and essential guide for professionals and students who wish to master the art of data engineering in a data-driven world. This book, written by Diego Rodrigues, a best-selling author with over 180 titles published in six languages, combines theory and practice to empower you in building efficient and scalable pipelines. Python and SQL are indispensable tools for data engineers, enabling precise manipulation, integration, and optimization of data workflows. Throughout this book, you will be guided through fundamental and advanced topics, exploring everything from the basics of data engineering to sophisticated strategies for security, governance, and automation of pipelines in both on-premises and cloud environments. Each chapter has been carefully designed to provide practical and applied understanding. You will learn to design database schemas, implement robust ETLs, automate workflows with frameworks such as Apache Airflow, and optimize SQL queries for high performance. Moreover, the book covers emerging topics like DataOps, API integration, and the use of Big Data tools such as Hadoop and Spark. With practical examples, detailed scripts, and clear explanations, "DATA ENGINEERING WITH PYTHON AND SQL" is more than just a technical manual; it is a gateway to a transformative career in the data field. Get ready to stand out in a competitive market and propel your professional journey. Your transformation in data engineering begins now! TAGS: Python Java Linux Kali HTML ASP.NET Ada Assembly BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Regression Logistic Regression Decision Trees Random Forests AI ML K-Means Clustering Support Vector Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF AWS Google Cloud IBM Azure Databricks Nvidia Meta Power BI IoT CI/CD Hadoop Spark Dask SQLAlchemy Web Scraping MySQL Big Data Science OpenAI ChatGPT Handler RunOnUiThread() Qiskit Q# Cassandra Bigtable VIRUS MALWARE Information Pen Test Cybersecurity Linux Distributions Ethical Hacking Vulnerability Analysis System Exploration Wireless Attacks Web Application Security Malware Analysis Social Engineering Social Engineering Toolkit SET Computer Science IT Professionals Careers Expertise Library Training Operating Systems Security Testing Penetration Test Cycle Mobile Techniques Industry Global Trends Tools Framework Network Security Courses Tutorials Challenges Landscape Cloud Threats Compliance Research Technology Flutter Ionic Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Bitrise Actions Material Design Cupertino Fastlane Appium Selenium Jest Visual Studio AR VR sql mysql
Mastering Python Design Patterns
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Author : Kamon Ayeva
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-31
Mastering Python Design Patterns written by Kamon Ayeva and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.
Learn Python design patterns such as Observer, Proxy, Throttling, Dependency Injection, and Anti-Patterns to develop efficient, scalable applications. Key Features Master essential design principles to build robust software architecture with the latest features in Python 3.10 Leverage concurrency, async patterns, and testing strategies for optimal performance Apply SOLID principles and advanced patterns to real-world Python projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAs software systems become increasingly complex, maintaining code quality, scalability, and efficiency can be a daunting challenge. Mastering Python Design Patterns is an essential resource that equips you with the tools you need to overcome these hurdles and create robust, scalable applications. The book delves into design principles and patterns in Python, covering both classic and modern patterns, and apply them to solve daily challenges as a Python developer or architect. Co-authored by two Python experts with a combined experience of three decades, this new edition covers creational, structural, behavioral, and architectural patterns, including concurrency, asynchronous, and performance patterns. You'll find out how these patterns are relevant to various domains, such as event handling, concurrency, distributed systems, and testing. Whether you're working on user interfaces (UIs), web apps, APIs, data pipelines, or AI models, this book equips you with the knowledge to build robust and maintainable software. The book also presents Python anti-patterns, helping you avoid common pitfalls and ensuring your code remains clean and efficient. By the end of this book, you'll be able to confidently apply classic and modern Python design patterns to build robust, scalable applications.What you will learn Master fundamental design principles and SOLID concepts Become familiar with Gang of Four (GoF) patterns and apply them effectively in Python Explore architectural design patterns to architect robust systems Delve into concurrency and performance patterns for optimized code Discover distributed systems patterns for scalable applications Get up to speed with testing patterns to ensure code reliability and maintainability Develop modular, decoupled systems and manage dependencies efficiently Who this book is for With a focus on intermediate and advanced Python programmers, this book offers valuable insights into the best practices for software design, backed by real-world examples and decades of experience. The book is also an excellent resource for software architects and team leaders who want to improve code quality and maintainability across their projects. Prior Python proficiency, including syntax, data structures, and OOP will help you get the most out of this book.
Mastering Python 3 Programming
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Author : Subburaj Ramasamy
language : en
Publisher: BPB Publications
Release Date : 2024-05-14
Mastering Python 3 Programming written by Subburaj Ramasamy and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-14 with Computers categories.
Learn the nitty-gritty of Python 3 programming language by coding and executing programs seamlessly in a lucid manner KEY FEATURES ● Python 3 fundamentals, from data manipulation to control flow. ● Key concepts like data structures, algorithms, and Python applications, catering to a diverse audience. ● Beginner-friendly guide with step-by-step explanations and practical examples. DESCRIPTION Python 3's clear and concise syntax and extensive collection of built-in libraries and frameworks make it a powerful and versatile programming language. This comprehensive guide, "Mastering Python 3 Programming", is designed to take you from the ground up to proficiency, equipping you to create effective Python programs. This book provides an extensive overview of Python programming, covering a diverse range of topics essential for understanding Python 3. Each chapter explores key concepts like Unicode strings, functions and recursions, lists, tuples, sets, and dictionaries, along with advanced topics such as object-oriented programming, file handling, exception handling, and more. With detailed explanations and real-life examples, you will be able to build a strong understanding of Python 3. Throughout the book, you will find useful concepts and Python libraries explained clearly, along with case studies, executable programs, exercises, and easy-to-follow style. This book focuses on real-world Python applications, developing critical thinking and problem-solving skills. It prepares students for Python challenges, equipping them to contribute meaningfully in their fields. With a deep understanding of Python, students gain confidence to explore new opportunities and drive innovation. WHAT YOU WILL LEARN ● Set up IDLE for Python programming and execute programs. ● Adapt algorithm based problem-solving techniques. ● Utilize Python libraries for data visualization. ● Grasp data structures and common algorithms. ● Master decorators, file handling, exception handling, inheritance, polymorphism, and recursion in Python. WHO THIS BOOK IS FOR The target audience for this book includes undergraduate students from diverse academic backgrounds, including life sciences, mathematics, commerce, management, arts, and individuals who are new to computer science. TABLE OF CONTENTS 1. Introduction to Python 3 2. Algorithmic Problem Solving 3. Numeric Computations and Console Input 4. Unicode, Strings and Console Output 5. Selection and Loops 6. Functions and Recursion 7. Lists 8. Tuples, Sets, and Dictionaries 9. Introduction to Object-Oriented Programming 10. Inheritance and Polymorphism 11. File Handling 12. Exception Handling 13. Gems of Python 14. Data Structures and Algorithms using Python 15. Data Visualization 16. Python Applications and Libraries Appendix 1: Python Projects Appendix 2: List of Built-in Functions in Python Appendix 3: Answers to Review Questions
Mastering Python Algorithms
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2024-10-26
Mastering Python Algorithms written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Computers categories.
"Mastering Python Algorithms: Practical Solutions for Complex Problems" is an essential guide for anyone eager to delve into the world of algorithmic design and implementation using Python. Structured to cater to various levels of learners, this book meticulously covers foundational principles and advanced algorithmic techniques. Whether you're a student, a developer, or a data scientist, you'll find the blend of theoretical insights and hands-on Python applications both enriching and practical. Spanning key areas from sorting and searching algorithms to the intricacies of graph theory and dynamic programming, the book provides in-depth explanations paired with Python code examples. It also delves into contemporary machine learning approaches and optimization methods, all while introducing readers to the nuances of Python’s advanced features that can significantly enhance algorithmic efficiency. By combining clear narrative with expert exploration of Python's rich ecosystem, "Mastering Python Algorithms" ensures readers are well-equipped to tackle diverse computational challenges with confidence. The emphasis on both performance analysis and implementation strategies guarantees that upon completion, readers will not only grasp complex algorithmic concepts but also be able to apply them effectively in real-world situations.