Python

• A Course in Python: The Core of the Language

  Roozbeh Hazrat · Book

  A Course in Python: The Core of the Language by Roozbeh Hazrat is a comprehensive textbook that introduces readers to Python programming through clear examples and worked-out exercises. Originally developed for undergraduate students in mathematics, science, engineering, and finance, the book is also suitable for self-study, especially for researchers aiming to use Python as a computational tool. Key Highlights: Structured Learning: Each section begins with a new topic description, followed by basic examples and detailed exercises, facilitating a step-by-step learning process. Practical Application: Emphasizes programming and problem-solving, particularly in numerical problems that don't require advanced mathematics. Comprehensive Coverage: Includes chapters on data handling, calculus, equation solving, and graphics, covering fundamental Python topics. Author's Expertise: Roozbeh Hazrat, a Professor of Pure Mathematics at Western Sydney University, brings extensive teaching experience in computer programming across various institutions. This book serves as an ideal resource for those seeking a hands-on, example-driven introduction to Python, balancing theoretical concepts with practical coding skills.

  Also filed under Math, Computer Science

• Advanced Data Analytics Using Python

  Sayan Mukhopadhyay · Book

  Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this ...

  Also filed under Data Visualization, Machine Learning

• Algorithms Python

  Yang Hu · Notes

  Algorithms Python YANG HU Simple is the beginning of wisdom. From the essence of practice, this book to briefly explain the concept, and vividly cultivate programming interest , you will learn it easy fast and well.

  Also filed under Computer Science, Machine Learning

• An Introduction To Statistical Learning with Applications in Python

  Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor · Book

  "An Introduction to Statistical Learning with Applications in Python" is an essential guide to statistical and machine learning methods, providing a clear and accessible introduction to the field. Designed for students, researchers, and practitioners, this book offers a balance between theory and practical implementation, focusing on Python-based applications for data analysis and predictive modeling. Key Highlights: Comprehensive Coverage of Statistical Learning: The book explains core machine learning principles, including supervised and unsupervised learning, model evaluation, and statistical inference. Regression and Classification Techniques: Topics include linear regression, logistic regression, decision trees, and support vector machines (SVMs), providing practical guidance on applying these models effectively. Resampling Methods and Model Selection: Introduces cross-validation and bootstrap techniques, helping readers understand how to improve model performance and avoid overfitting. Tree-Based and Ensemble Methods: Covers random forests, gradient boosting, and bagging, which are widely used in modern machine learning applications. Unsupervised Learning Techniques: Introduces principal component analysis (PCA), k-means clustering, and hierarchical clustering, essential for working with high-dimensional data. Python-Based Implementation: The book provides hands-on coding examples using scikit-learn, NumPy, pandas, and Matplotlib, enabling readers to apply theoretical concepts in real-world scenarios. This book serves as a valuable resource for those looking to build a strong foundation in statistical learning, blending intuitive explanations, practical examples, and Python-based applications to make complex concepts accessible to a broad audience.

  Also filed under Machine Learning, Statistics

• Applied Data Science

  Ian Langmore & Daniel Krasner · Book

  26 5.2 Coefficient Estimation: Bayesian Formulation . 29 5.2.2 Ideal Gaussian World . 30 5.3 Coefficient Estimation: Optimization Formulation . 33 5.3.1 The least squares problem and the singular value de- composition .

  Also filed under Data Visualization, Computer Science

• Applied Machine Learning with Python

  Andrea Giussani · Book

  Via Salasco, 5 - 20136 Milano Tel.

  Also filed under Machine Learning, Math

• Artificial Intelligence with Python

  Tutorials Point · Book

  About the Tutorial Artificial intelligence is the intelligence demonstrated by machines, in contrast to the intelligence displayed by humans. This tutorial covers the basic concepts of various fields of artificial intelligence like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms etc., and its implementation in Python. Audience This tutorial will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The reader can be a beginner ...

  Also filed under AI, Machine Learning

• Building Machine Learning Systems with Python

  Book

  A practical introduction to machine learning using Python, covering NumPy, SciPy, and Matplotlib for data handling; classification (kNN, naive Bayes), clustering, and feature engineering; hands-on examples on the Iris and Seeds datasets; techniques for evaluation and model selection.

  Also filed under Machine Learning, Computer Science

• Clean Architectures in Python

  Leonardo Giordani · Book

  6 Prerequisites and structure of the book. 7 Why this book comes for free . 7 Submitting issues or patches. 8 Changes in the second edition.

  Also filed under Software Engineering, Data Visualization

• Data Science from Scratch

  Steven Cooper · Book

  "Data Science from Scratch" by Steven Cooper is an excellent introduction to the world of data science for beginners and intermediate learners alike. The book focuses on the foundational concepts of data science and offers a practical approach to understanding its key principles, including algorithms, statistical analysis, and machine learning. The author breaks down complex topics into digestible pieces, providing a hands-on, Python-based approach for readers to learn and apply data science concepts from scratch. Key Highlights: Introduction to Data Science: The book begins with an overview of data science, its applications, and its importance in various industries. Cooper introduces the core concepts and workflows in data science, including data collection, cleaning, analysis, visualization, and modeling. He emphasizes the iterative process of working with data and the need for solid foundational knowledge to excel in the field. Python Basics for Data Science: Since the book assumes no prior experience in data science or programming, it starts with a primer on Python. The author introduces basic Python concepts, such as variables, loops, conditionals, functions, and data structures (lists, dictionaries, and tuples). The use of Python as a tool for data science is emphasized, with practical examples to illustrate key programming concepts. Data Collection and Preparation: A large portion of the book focuses on the critical step of data collection and preparation. Cooper walks readers through methods of gathering data from various sources, including APIs and databases. He also discusses techniques for cleaning data, handling missing values, and transforming raw data into a format suitable for analysis. The importance of data quality is stressed, and readers learn how to manage and preprocess data to get meaningful results. Exploratory Data Analysis (EDA): The book introduces exploratory data analysis (EDA) as a vital part of the data science process. Cooper explains how to summarize and visualize data using various Python libraries such as pandas, NumPy, and Matplotlib. Readers learn how to analyze distributions, detect patterns, and identify outliers in their datasets through descriptive statistics and visualizations. Probability and Statistics: The author covers the essential statistical concepts needed for data science, including probability theory, distributions, hypothesis testing, and confidence intervals. The book introduces concepts like mean, median, variance, correlation, and regression analysis to help readers understand how to quantify uncertainty and make inferences about data. Machine Learning: One of the key aspects of data science is machine learning, and the book provides a comprehensive introduction to this area. Cooper discusses supervised and unsupervised learning, explaining key algorithms like linear regression, k-nearest neighbors (KNN), decision trees, and clustering. The author also covers model evaluation techniques, such as cross-validation and confusion matrices, helping readers understand how to assess the performance of machine learning models.

  Also filed under Computer Science, Statistics

• Data-Driven Innovation in the Creative Industries

  Melissa Terras, Vikki Jones, Nicola Osborne & Chris Speed · Book

  The exploration of innovation in this collection of essays champions the collaborative potential of creativity and technology and encourages the adoption of data-led creativity to shape the future of the creative economy. A must-read for those looking to drive meaningful change and innovation across the industry.” Lee Walters, CEO of Ffilm Cymru Wales. Formerly Programme Manager of Clwstwr, part of the Creative Industries Clusters Programme “This book brings to life real-world applications of data-led creativity and, importantly, addresses ethical considerations. But what do these mean in t...

  Also filed under Data Visualization, Computer Science

• Deep Learning for Computer Vision with Python

  Adrian Rosebrock · Book

  Deep Learning for Computer Vision with Python Practitioner Bundle Dr. Books like this are made possible by the time invested by the authors. First printing, September 2017 To my father, Joe; my wife, Trisha; and the family beagles, Josie and Jemma. Without their constant love and support, this book would not be possible.

  Also filed under Machine Learning, Computer Science

• High Performance Python

  Micha Gorelick & Ian Ozsvald · Book

  Understanding Performant Python. Profiling to Find Bottlenecks.

  Also filed under Data Visualization, Software Engineering

• Introduction to Scientific Programming with Python

  Joakim Sundnes · Book

  This book is an open access publication. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc.

  Also filed under Computer Science, Statistics

• Kubernetes Patterns: Reusable Elements for Designing Cloud Native Applications

  Bilgin Ibryam & Roland Huß · Book

  Kubernetes Patterns: Reusable Elements for Designing Cloud-Native Applications by Bilgin Ibryam and Roland Huß is a detailed guide that explores the design patterns essential for building scalable, reliable, and efficient cloud-native applications on Kubernetes. The book provides reusable solutions to common challenges faced during the design and implementation of Kubernetes-based systems, helping developers and architects optimize their workflows. Key Highlights: Introduction to Kubernetes and Cloud-Native Principles: The book starts with a solid foundation on Kubernetes and cloud-native application design. It introduces the core principles behind Kubernetes, including container orchestration, service management, and microservices architecture, which are fundamental to designing cloud-native applications. Understanding Design Patterns: The central theme of the book is design patterns—reusable solutions to recurring design problems. The authors demonstrate how design patterns are vital for building complex, cloud-native applications that need to scale, remain resilient, and be easily maintained over time. Observability and Monitoring Patterns: Kubernetes provides powerful tools for logging, monitoring, and tracing, and this book outlines patterns to implement observability in cloud-native applications. The authors explore how to monitor clusters, containers, and applications, ensuring that issues can be detected and resolved quickly. Scaling and Resilience Patterns: One of the key advantages of Kubernetes is its ability to scale applications efficiently. This section focuses on patterns to manage scaling horizontally and vertically, as well as ensuring that applications remain resilient to failures through automated self-healing processes, replica sets, and distributed systems. Security Patterns: Security is an essential part of designing cloud-native applications. The book outlines patterns for securing applications on Kubernetes, including role-based access control (RBAC), network policies, and managing sensitive data with Kubernetes secrets. Resource Management Patterns: Kubernetes allows for fine-grained control over resources, such as CPU and memory allocation. The authors explain patterns for managing resources effectively, ensuring that containers get the necessary resources while avoiding conflicts or bottlenecks. CI/CD and DevOps Integration: The book covers patterns for integrating Kubernetes with continuous integration and continuous deployment (CI/CD) pipelines. By automating testing and deployment workflows, developers can speed up release cycles and improve the quality and reliability of applications. Real-World Use Cases: Throughout the book, the authors provide practical case studies and real-world examples that demonstrate how the patterns discussed are implemented in live Kubernetes clusters. These case studies show how companies leverage Kubernetes to build resilient, scalable systems in production.

  Also filed under Software Engineering, Computer Science

• Learn More Python 3 the Hard Way

  Zed A. Shaw · Book

  "Learn More Python 3 the Hard Way" by Zed A. Shaw is a continuation of his instructional series, designed for individuals who have a foundational understanding of Python and wish to deepen their programming skills. This book emphasizes hands-on learning through a series of 52 meticulously crafted exercises, each aimed at building practical capabilities in Python programming. Key Features: Project-Based Learning: The book adopts a project-centric approach, guiding readers through exercises that involve analyzing problems, designing solutions, and implementing them in Python. This methodology fosters a deeper understanding of programming concepts and enhances problem-solving skills. Emphasis on Process and Quality: Shaw underscores the importance of adhering to a structured process, nurturing creativity, and striving for quality in coding practices. These principles are woven throughout the exercises to cultivate disciplined and proficient programmers. Supplementary Video Content: The book is complemented by over 12 hours of online video tutorials, where Shaw demonstrates how to break, fix, and debug code. This visual aid reinforces the lessons from the exercises and provides deeper insights into effective coding practices. "Learn More Python 3 the Hard Way" is ideal for readers who have completed introductory Python courses and are seeking to advance their skills through practical application. By engaging with real-world projects and challenges, readers can transition from basic understanding to proficient coding, preparing them for more complex programming endeavors.

  Also filed under Math, Computer Science

• Learn Python Programming Quickly

  Chris Ford · Book

  Data Types in Python What Are Data Types? Understanding Data Types Type Casting 5. Lists and Tuples What Is a List Data Type?

  Also filed under Data Visualization, Machine Learning

• Learning Pandas

  Stack Overflow Documentation · Book

  Learning Pandas (Stack Overflow Documentation) is a practical and comprehensive guide to the Pandas library, offering a structured approach to data manipulation and analysis in Python. Designed for data analysts, scientists, and developers, this book compiles expert knowledge from Stack Overflow, providing clear explanations and hands-on examples to enhance data processing skills. Key Highlights: Foundational Understanding of Pandas : Introduces Pandas' core data structures, Series and DataFrames, demonstrating their fundamental role in data analysis. Data Importing and Exporting: Covers techniques for reading and writing data in various formats, including CSV, Excel, JSON, and SQL, ensuring seamless integration with different data sources. Data Cleaning and Transformation: Guides readers through handling missing values, filtering, indexing, and data type conversions, essential for preparing datasets for analysis. Efficient Data Manipulation: Explains powerful Pandas functions such as groupby(), merge(), pivot(), and apply(), enabling effective aggregation, reshaping, and computation on datasets. Exploratory Data Analysis (EDA): Discusses summary statistics, value distributions, and data visualization using Pandas, Matplotlib, and Seaborn, aiding in uncovering key insights. Advanced Techniques and Performance Optimization: Introduces time series analysis, vectorization, and efficient memory usage, helping users work with large datasets effectively. Real-World Applications: Provides practical examples and workflows for finance, machine learning preprocessing, and business analytics, making Pandas a valuable tool across industries. With a practical, example-driven approach, this book equips readers with the knowledge to harness the full power of Pandas for data analysis, making complex tasks more manageable and efficient. Whether you’re a beginner or an experienced user, this guide serves as an essential reference for mastering Pandas in Python.

  Also filed under Data Visualization, Machine Learning

• Learning Professional Python

  Usharani Bhimavarapu & Jude D. Hemanth · Book

  Learning Professional Python by Usharani Bhimavarapu and Jude D. Hemanth is a two-volume series designed to provide a comprehensive understanding of Python programming, catering to both beginners and experienced programmers. The series emphasizes object-oriented programming concepts and practical applications, aiming to equip readers with the skills necessary for professional Python development. Volume 1: The Basics Overview: This volume introduces readers to the fundamentals of Python programming, focusing on object-oriented principles and essential programming constructs. It's suitable for individuals new to programming or those transitioning from other languages. Key Highlights: Python Fundamentals: Covers the basics of Python syntax, data types, and control structures, providing a solid foundation for further learning. Object-Oriented Programming (OOP): Explores core OOP concepts such as classes, objects, inheritance, dynamic dispatch, interfaces, and packages, enabling readers to write modular and reusable code. Advanced Features: Delves into Python's generics and collections, exception handling, multithreaded applications, and designing graphical user interface (GUI) applications, preparing readers for complex programming tasks. Volume 2: Advanced Overview: Building upon the foundational knowledge from Volume 1, this volume delves into advanced topics, providing readers with the expertise needed for complex application development. Key Highlights: Advanced OOP Concepts: Further explores object-oriented principles, enhancing the reader's ability to design sophisticated systems. Exception Handling and Multithreading: Provides in-depth coverage of robust error handling and concurrent programming, essential for developing reliable and efficient applications. GUI Application Development: Guides readers through designing and implementing user-friendly GUI applications, expanding the scope of Python projects. Case Studies: Presents real-world projects such as WhatsApp Analyzer, Breast Cancer Prediction, Stock Price Prediction, and more, offering practical insights into applying Python in various domains.

  Also filed under Math, Computer Science

• Let Us Python Solutions

  Yashavant Kanetkar & Aditya Kanetkar · Book

  No part of this publication can be stored in a retrieval system or reproduced in any form or by any means without the prior written permission of the publishers. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The Author and Publisher of this book have tried their best to ensure that the programmes, procedures and functions described in the book are correct. However, the author and the publishers make no warranty of any kind, expressed or implied, with regard to these programmes or the documentation contained in the book. The author and publisher shall not be liable in any event of any damag...

  Also filed under Data Visualization, Math

• Machine Learning in Python For Everyone

  Jonathan Wayna Korn · Book

  An introduction to implementing machine learning algorithms in Python using libraries such as scikit-learn. It covers key concepts including data preprocessing, model training, evaluation metrics, and common supervised and unsupervised learning techniques.

  Also filed under Machine Learning, Data Visualization

• Machine Learning with Python

  Tutorials Point · Book

  A comprehensive tutorial from Tutorials Point covering the fundamentals of machine learning using Python. It introduces key ML concepts, the Python ecosystem for data science, methods and tasks suited for machine learning, data loading and preprocessing, and practical implementation of algorithms using NumPy, Scikit-learn, SciPy, and Matplotlib.

  Also filed under Machine Learning, Computer Science

• Mastering Pandas: Advanced Pandas For Finance

  Hayden Van Der Post · Book

  "Mastering Pandas: Advanced Pandas For Finance" by Hayden Van Der Post delves into sophisticated techniques for using the Pandas library in financial data analysis. It covers advanced data manipulation, time series analysis, and financial modeling, providing practical examples and strategies to enhance data-driven decision-making in finance.

  Also filed under Quant Finance, Finance

• Pricing Commodity Options Using Monte Carlo Simulation with Python

  Nikita Lavrentyev · Notes

  Introduction to Option Pricing in Commodity Markets As oil prices fluctuate amid uncertainty surrounding the Middle East and U.S. elections, traders are reminded of how unpredictable the energy markets can be. Commodity markets, such as oil, are particularly susceptible to sudden price swings due to a range of external factors, including geopolitical tensions, supply chain disruptions, and shifts in global demand. In such a volatile environment, accurately pricing options is crucial for managing risk and protecting against sudden price shifts.

  Also filed under Quant Finance, Risk Management

• Python 200 Things Every Beginner Should Know

  Author Unknown · Book

  Purpose Chapter 2 for beginners 1. Python uses indentation for code blocks 2. Variables are dynamically typed 3. Using snake_case Naming Conventions 4.

  Also filed under Computer Science, Math

• Python 500 Practice Exams Questions & Answers With Explanation

  Author Unknown · Guide

  Basics of Python Programming Chapter 2. Python Control Structures Chapter 3. Python Functions and Modules Chapter 4. Python Data Structures Chapter 5.

  Also filed under Software Engineering, Data Visualization

• Python Automation Cookbook

  Jaime Buelta · Book

  "Python Automation Cookbook" by Jaime Buelta is a practical guide that demonstrates how to automate various tasks using Python, aiming to enhance efficiency and productivity. The book employs a problem-solution approach, providing recipes to automate repetitive tasks across different domains. Key Features: Web Scraping and Data Extraction: Learn techniques to scrape websites, detect changes, and extract valuable information for analysis. File and Data Management: Discover methods to search, process, and aggregate raw data files into structured formats like spreadsheets. Report Generation: Explore ways to extract data from Excel spreadsheets and generate comprehensive reports with graphs using libraries such as Matplotlib. Marketing Automation: Understand how to automatically generate marketing campaigns and communicate with recipients over different channels. Debugging Techniques: Gain insights into identifying and implementing precise solutions to common automation challenges. The second edition of the book includes additional chapters focusing on automated code testing, machine learning projects, and handling unstructured data, reflecting Python's growth in data science and AI automation. This cookbook is suitable for developers, data enthusiasts, or anyone interested in automating monotonous manual tasks related to business processes such as finance, sales, and HR. It serves both as a step-by-step guide for beginners and a reference for experienced Python users seeking to streamline their workflows.

  Also filed under Machine Learning, Data Visualization

• Python By Example

  Nichola Lacey · Book

  "Python By Example" by Nichola Lacey provides a practical approach to learning Python programming through hands-on examples. It guides readers from basic concepts to more advanced topics, using real-world scenarios to illustrate how Python can be applied effectively. The book is designed to help beginners understand and implement Python code by working through a series of projects and exercises.

  Also filed under Data Visualization, Machine Learning

• Python Essentials 1

  The OpenEDG Python Institute · Book

  About this eBook While ePUB is an open standard widely employed for the publication of electronic books, support and features may vary from one device to another. Every effort has been made to ensure that this book will display faithfully on all devices, but it may be necessary to adjust the settings of your particular device for optimum readability. There are many examples of code employed throughout this book. Due to the flowing nature of text in the ePUB format, this code, which is written line by line, may not always display correctly.

  Also filed under Data Visualization, Math

• Python Fast/Deep/Simple

  Behnam Khani · Book

  About the author Hello My name is Behnam Khani. I’m a software engineer with 10 years of experience in the industry. I have a passion for technology, education, and software development and enjoy combining the three. This book and my website dejavucode.com are places to share my knowledge and experiences!

  Also filed under Data Visualization, Machine Learning

• Python for Excel

  Felix Zumstein · Book

  "Python for Excel" by Felix Zumstein is a comprehensive guide that demonstrates how to integrate Python into Excel workflows, enhancing data analysis and automation capabilities for experienced Excel users. As the creator of xlwings, a popular open-source package for automating Excel with Python, Zumstein provides practical insights and tools to bridge the gap between these two platforms. Key Features: Introduction to Python in Excel: The book begins by introducing Python as a powerful tool for Excel users, emphasizing its advantages over traditional VBA scripting. It guides readers through setting up modern development environments, including Jupyter notebooks and Visual Studio Code, to streamline their workflow. Data Analysis with pandas: Readers learn to utilize the pandas library to acquire, clean, and analyze data, effectively replacing typical Excel calculations with more robust Python operations. This section covers data manipulation techniques essential for efficient analysis. Automation of Excel Tasks: The book provides strategies to automate repetitive tasks such as consolidating workbooks and generating reports. By leveraging Python scripts, users can significantly reduce manual effort and increase productivity. Building Interactive Tools with xlwings: Zumstein delves into using xlwings to create interactive Excel tools that harness Python as a calculation engine. This enables the development of sophisticated applications within the familiar Excel interface. Database Integration and Web Data Retrieval: The guide covers connecting Excel to external databases and importing data from the web using Python, expanding the scope of data sources available for analysis. Unified Tool for Automation and Analysis: By combining Python with Excel, users can replace multiple tools such as VBA, Power Query, and Power Pivot, creating a cohesive and versatile environment for data tasks. For hands-on practice, the author provides a companion repository containing all relevant Jupyter notebooks and code samples, facilitating practical application of the concepts discussed.

  Also filed under Data Visualization, SQL

• Python for Science and Engineering

  Hans-Petter Halvorsen · Book

  Preface Python is a popular programming language, and it is one of the most used pro- gramming languages today. Python works on all the main platforms and operating systems used today, such Windows, macOS, and Linux. Python is a multi-purpose programming language, which can be use for simu- lation, creating web pages, communicate with database systems, etc. Here you can download the software, download code examples, etc.

  Also filed under Math, Computer Science

• Python In easy steps

  Mike McGrath · Book

  No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without prior written permission from the publisher. Notice of Liability Every effort has been made to ensure that this book contains accurate and current information. However, In Easy Steps Limited and the author shall not be liable for any loss or damage suffered by readers as a result of any information contained herein. Trademarks All trademarks are acknowledged as belonging to their respective ...

  Also filed under Data Visualization, Machine Learning

• Python in Excel

  Hayden Van Der Post · Book

  Excel, a stalwart of data manipulation, visualization, and analysis, is ubiquitous in business environments. Python, on the other hand, brings unparalleled versatility and efficiency to data handling tasks. Integrating these two can significantly enhance your data processing capabilities, streamline workflows, and open up new possibilities for advanced analytics. The Foundation: Why Integrate Python with Excel?

  Also filed under Data Visualization, SQL

• Python Machine Learning Workbook for Begginers

  AI Publishing · Guide

  Introduction and Environment Set Up Data science libraries exist in various programming languages. However, you will be using Python programming language for data science and machine learning since Python is flexible, easy to learn, and offers the most advanced data science and machine learning libraries. Furthermore, Python has a huge data science community from where you can take help whenever you want. In this chapter, you will see how to set up the Python environment needed to run various data science and machine learning libraries.

  Also filed under Machine Learning, Data Visualization

• Python Machine Learning: A Beginner's Guide to Scikit-Learn

  Rajender Kumar · Book

  No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright owner. This book is sold subject to the condition that it shall not, by way of trade or otherwise, be lent, resold, hired out, or otherwise circulated without the publisher's prior consent in any form of binding or cover other than that in which it is published and without a similar condition including this condition being imposed on the subsequent purchaser. Trad...

  Also filed under Machine Learning, Statistics

• Python makes programming fun!

  Mike McGrath · Book

  "Python Makes Programming Fun!" by Mike McGrath is an engaging introduction to Python, designed to make learning to program an enjoyable and accessible experience for beginners. The book provides a step-by-step approach to Python programming, ensuring that readers build a solid foundation in coding while having fun with practical examples and projects. Key Highlights: Beginner-Friendly Approach: The book is designed for those with little or no prior programming experience, making it an excellent resource for students, hobbyists, and aspiring developers who want to explore Python in a simple and engaging way. Core Python Concepts Explained Clearly: Readers are introduced to fundamental programming concepts such as variables, data types, loops, conditional statements, functions, and error handling. Each topic is explained in an easy-to-follow manner with practical examples. Hands-On Coding with Fun Projects: The book encourages hands-on learning by guiding readers through small, enjoyable coding projects. These include creating simple games, automating tasks, and building interactive programs that make learning Python an exciting experience. Graphical and Interactive Programming: In addition to traditional programming exercises, the book covers how to use Python libraries such as Tkinter for GUI development and turtle graphics to create visual projects, making programming more engaging for beginners. Logical Thinking and Problem-Solving Skills: By working through various challenges and exercises, readers develop logical thinking and problem-solving skills that are essential for programming in any language. Python’s Versatility and Real-World Applications: The book highlights Python's broad applications, from web development to data science, showing how learning Python can open doors to various career opportunities. Simple, Practical, and Fun Learning Experience: The author emphasizes a fun and enjoyable learning journey, making programming less intimidating and more rewarding. The use of humor, interactive examples, and step-by-step instructions ensures that readers stay engaged.

  Also filed under Math, Data Visualization

• Python Mastery

  Chloe Annable · Book

  "Python Mastery" by Chloe Annable is a comprehensive guide designed to elevate readers from beginner to expert in Python programming. It covers fundamental concepts, advanced techniques, and practical applications, providing hands-on exercises and real-world examples to ensure mastery of the language.

  Also filed under Math, Data Visualization

• Python Pandas Tutorial For Beginners : The Ultimate Guide For Beginners

  Kavi & Shila · Book

  A beginner-friendly tutorial on the Pandas library in Python for data manipulation and analysis. It covers fundamental operations such as creating DataFrames, reading CSV files, filtering data, and performing basic statistical summaries.

  Also filed under Data Visualization, Machine Learning

• Python Programming

  Dylan Penny · Book

  "Python Programming" by Dylan Penny is a comprehensive guide that introduces readers to the fundamentals of Python, covering essential concepts such as data types, control structures, functions, and modules. The book progresses to more advanced topics like object-oriented programming, file handling, and libraries, providing practical examples and exercises to reinforce learning. It is designed for both beginners and those looking to enhance their Python skills for real-world applications.

  Also filed under Computer Science, Math

• Python Programming Essentials

  Ankit Pandey · Book

  Emphasis will also be placed on verifying the installation and resolving potential issues that may arise during the process. Windows Installation To install Python on a Windows operating system, the official Python distribution is recommended. Select the Downloads section and choose the appropria

  Also filed under Data Visualization, Machine Learning

• Python Tkinter

  Vaishali B. Bhagat · Book

  BHAGAT Copyright © 2024 by Vaishali B. No part of this book may be used or reproduced in any form whatsoever without written permission except in the case of brief quotations in critical articles or reviews.

  Also filed under Data Visualization, Software Engineering

• Python Tutorial (Codes)

  Mustafa Germec · Book

  "Python Tutorial (Codes)" by Mustafa Germec, PhD, is a comprehensive guide aimed at both beginners and intermediate learners seeking to enhance their Python programming skills. The tutorial is structured into 21 chapters, each focusing on a specific aspect of Python, providing detailed explanations and practical code examples. Key Features: Foundational Concepts: The initial chapters introduce the basics of Python, including data types, strings, lists, tuples, sets, and dictionaries. These sections lay the groundwork for understanding how data is stored and manipulated in Python. Control Structures: Subsequent chapters delve into conditions and loops, explaining how to control the flow of a Python program using conditional statements and iterative processes. Functions and Exception Handling: The tutorial covers the creation and utilization of functions for modular programming and discusses exception handling to manage errors gracefully. Object-Oriented Programming (OOP): An in-depth exploration of classes and objects is provided, illustrating the principles of OOP and how they are implemented in Python. File Operations: Chapters on reading and writing files equip readers with the skills to handle file input and output operations, essential for data processing tasks. Advanced Topics: The latter sections introduce advanced concepts such as decorators, generators, lambda functions, list comprehensions, and the use of the math module. These topics are crucial for writing efficient and Pythonic code. Each chapter is designed to build upon the previous ones, ensuring a cohesive learning experience. The inclusion of practical code snippets throughout the tutorial allows readers to apply concepts immediately, reinforcing their understanding through hands-on practice. This tutorial is particularly beneficial for individuals aiming to solidify their Python programming abilities, offering a structured approach to both fundamental and advanced topics. By following this guide, readers can develop a robust foundation in Python, preparing them for more complex programming challenges.

  Also filed under Computer Science, Math

• Storytelling with data

  Cole Nussbaumer Knaflic · Book

  A guide based on Cole Nussbaumer Knaflic's influential book on effective data communication. It covers how to choose appropriate visuals, eliminate clutter, and use narrative structure to transform data into compelling stories for business audiences.

  Also filed under Data Visualization, Statistics

• The Python Handbook

  Flavio Copes · Book

  "The Python Handbook" by Flavio Copes is a comprehensive guide for beginners and experienced programmers alike, covering Python programming fundamentals, data structures, functions, modules, and libraries. It provides practical examples and exercises to help readers understand and apply Python concepts effectively.

  Also filed under Computer Science, Math

• Think Python

  Allen B. Downey · Book

  Downey Think Python by Allen B. Downey Copyright © 2024 Allen Downey. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.

  Also filed under Data Visualization, Math

• Think Stats

  Allen B. Downey · Book

  "Think Stats" by Allen B. Downey is a practical guide to exploring and analyzing real-world data using Python. It introduces statistical concepts and techniques through hands-on exercises, focusing on understanding data distributions, probability, and statistical inference. The book emphasizes computational thinking and encourages readers to apply statistical methods to solve problems and make data-driven decisions.

  Also filed under Statistics, Data Visualization

• Time Series Analysis

  Andrew Lesniewski · Slides

  We have seen that a stationary time series in the ARMA(p, q) family can be written in the moving average (MA) form: Xt = µ + εt + γ1εt−1 + γ2εt−2 . = µ + γ(L)εt, (1) where L is the lag operator, and where P∞ j=1 |γj| < ∞. Stationary series are rather unusual in finance, and hence the need for developing models that capture the non-stationary nature of financial time series. There are various approaches to model non-stationarity.

  Also filed under Data Visualization, Risk Management

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