Название: Software Engineering for Data Scientists: From Notebooks to Scalable Systems (Final)
Автор: Саthеrinе Nеlsоn
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 400
Язык: английский
Формат: pdf, epub (true)
Размер: 11.2 MB

Data Science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to Data Science. All the code examples in this book are written in Python, and many of the chapters describe Python-specific tools. In recent years, Python has become the most popular programming language for Data Science. Python has an extremely solid set of open source libraries for data science, with good backing and a healthy community of maintainers. Large trend-setting companies have chosen Python for their main ML frameworks, including TensorFlow (Google) and PyTorch (Meta). Because of this, Python appears to be especially popular among data scientists working on production Machine Learning code, where good coding skills are particularly important. In my experience, the Python community has been friendly and welcoming, with many excellent events that have helped me improve my skills. It’s my preferred programming language, so it was an easy choice for this book. This book is aimed at data scientists, but people working in closely related fields such as data analysts, Machine Learning (ML) engineers, and data engineers will also find it useful.

Название: A Simple Introduction to Python
Автор: Stерhеn Lуnсh
Издательство: CRC Press
Серия: The Python Series
Год: 2024
Страниц: 113
Язык: английский
Формат: pdf (true)
Размер: 21.4 MB

A Simple Introduction to Python is aimed at pre-university students and complete novices to programming. The whole book has been created using Jupyter notebooks. After introducing Python as a powerful calculator, simple programming constructs are covered, and the NumPy, MatPlotLib and SymPy modules (libraries) are introduced. Python is then used for Mathematics, Cryptography, Artificial Intelligence, Data Science and Object Oriented Programming. The reader is shown how to program using the integrated development environments: Python IDLE, Spyder, Jupyter notebooks, and through cloud computing with Google Colab. Currently, Python is the most popular programming language in the world. It is open source and completely free to the user. It is also fun to program with and extremely powerful at solving real-world problems. This book is for pre-university students or complete novices to programming. Each chapter provides both examples and exercises. In order to understand programming, the reader must attempt these exercises. Learning to program is like learning to ride a bike – you have to fall off many times before mastering the art. Expect to make many mistakes, learn from those mistakes and then move on to the next chapter. Full working solutions to all of the exercises and other resources will be provided via GitHub, where readers will be able to download all files for free. There will also be a web page with full-worked solutions, where readers can simply copy and paste code and run the programs in a Python environment. Readers should note that Python programs can easily be generated with ChatGPT, developed by OpenAI, and other alternatives such as Microsoft Bing, Perplexity AI and Google Bard AI, for example.

Название: Outlier Detection in Python (MEAP v1)
Автор: Вrеtt Кеnnеdу
Издательство: Manning Publications
Год: 2024
Страниц: 283
Язык: английский
Формат: pdf, epub
Размер: 20.4 MB

Learn how to find the unusual, interesting, extreme, or inaccurate parts of your data. Outliers can be the most informative parts of your data, revealing hidden insights, novel patterns, and potential problems. For a business, this can mean finding new products, expanding markets, and flagging fraud or other suspicious activity. Outlier Detection in Python introduces the tools and techniques you’ll need to uncover the parts of a dataset that don’t look like the rest, even when they’re the more hidden or intertwined among the expected bits. Outlier Detection in Python is a comprehensive guide to the statistical methods, Machine Learning, and Deep Learning approaches you can use to detect outliers in different types of data. Throughout the book, you’ll find real-world examples taken from author Brett Kennedy’s extensive experience developing outlier detection tools for financial auditors and social media analysis. Plus, the book’s emphasis on interpretability ensures you can identify why your outliers are unusual and make informed decisions from your detection results. Each key concept and technique is illustrated with clear Python examples. All you’ll need to get started is a basic understanding of statistics and the Python data ecosystem. For Python programmers familiar with tools like pandas and NumPy, and the basics of statistics.

Название: Ultimate Python Libraries for Data Analysis and Visualization: Leverage Pandas, NumPy, Matplotlib, Seaborn, Julius AI and No-Code Tools for Data Acquisition, Visualization, and Statistical Analysis
Автор: Аbhinаbа Ваnеrjее
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 507
Язык: английский
Формат: pdf, epub
Размер: 14.9 MB

Test your Data Analysis skills to its fullest using Python and other no-code toolsBook DescriptionUltimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets.Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more.By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax.

Название: Mastering Time Series Analysis and Forecasting with Python: Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time Series Analysis in Python
Автор: Sulеkhа Аlооrrаvi
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 310
Язык: английский
Формат: epub (true)
Размер: 10.1 MB

Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate. "Mastering Time Series Analysis and Forecasting with Python" is an essential handbook tailored for those seeking to harness the power of time series data in their work. The book begins with foundational concepts and seamlessly guides readers through Python libraries such as Pandas, NumPy, and Plotly for effective data manipulation, visualization, and exploration. Offering pragmatic insights, it enables adept visualization, pattern recognition, and anomaly detection. Advanced discussions cover feature engineering and a spectrum of forecasting methodologies, including Machine Learning and Deep Learning techniques such as ARIMA, LSTM, and CNN. Additionally, the book covers multivariate and multiple time series forecasting, providing readers with a comprehensive understanding of advanced modeling techniques and their applications across diverse domains.

Название: Data Universe: Organizational Insights with Python: Embracing Data Driven Decision Making
Автор: Науdеn Vаn Dеr Роst
Издательство: Reactive Publishing
Год: 2024
Страниц: 416
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Unlock the Power of Python to Transform Your Organization with "Data Universe: Organizational Insights with Python". In the rapidly evolving digital landscape, data is the new currency. Mastering it means unlocking endless opportunities for growth, innovation, and efficiency. "Data Universe: Organizational Insights with Python" by Hayden Van Der Post is your comprehensive guide to harnessing the full potential of Python for data-driven decision-making and organizational transformation. Python, with its simplicity and powerful libraries, has become synonymous with the data-driven transformation. Libraries such as Pandas for data manipulation, NumPy for numerical data processing, and Matplotlib for data visualization, among others, have equipped data scientists and analysts with the tools to perform complex analyses with relative ease. Python’s role in this paradigm shift cannot be overstated; it's the lingua franca of Data Science, enabling the translation of data into a narrative that informs decision-making processes. Python, with its extensive ecosystem of data analytics libraries such as Pandas, Scikit-learn, and TensorFlow, serves as a powerful tool in the DDDM process. These libraries simplify the tasks of data manipulation, analysis, and Machine Learning, making Python an indispensable asset in translating complex datasets into meaningful insights.

Название: Programming Machine Learning: Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning
Автор: Каvishаnkаr Раnсhtilаk
Издательство: Kavis Web Designer
Год: 2024
Страниц: 558
Язык: английский
Формат: pdf
Размер: 37.0 MB

Machine Learning (ML) is basically that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of Artificial Intelligence (AI) that extract patterns out of raw data by using an algorithm or method. The key focus of ML is to allow computer systems to learn from experience without being explicitly programmed or human intervention. This book will be useful for graduates, postgraduates, 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 or an advanced learner. This tutorial has been prepared for the students as well as professionals to ramp up quickly. This tutorial is a stepping stone to your Machine Learning journey. The reader must have basic knowledge of Artificial Intelligence. He/she should also be aware of Python, NumPy, Scikit-learn, Scipy, Matplotlib.

Название: Python for Geometric Modeling: Quick Introduction and Step-by-Step Guide to CAD Applications
Автор: Rоnаld Маrtinоd, Dаniеl Соrrеа, Раоlа Vаllеjо
Издательство: Practical Books
Год: 2024
Страниц: 346
Язык: английский
Формат: pdf
Размер: 30.2 MB

The book gives a quick introduction to the fundamentals of geometric modeling through Python programming. It covers making parametric curves and surfaces. through, avoiding any overwhelming concepts. The book is filled with easy-to-understand code snippets, diagrams, exercises, and quick projects, making learning practical and accessible. By going through the content, you'll quickly get the hang of Python and be all set to use it for geometric modeling. After finishing the book, you'll have grasped the basics of geometric modeling. This knowledge helps you dive into more advanced projects, making it easier to pick up extra skills and insights along the way. What you will learn: Utilizing Colab effectively; Using Numpy, Matplotlib, and Plotly to plot geometric models; Modeling two-dimensional curves; Modeling three-dimensional curves, etc. This book is designed for Python developers at any level of experience with Python programming who want to learn geometric computer modeling, whether they are young, adults, professionals, or teachers. In addition, it is a book designed for beginners or novices in mathematical concepts.

Название: Mathematical Methods using Python: Applications in Physics and Engineering
Автор: Vаsilis Раgоnis, Сhristорhеr W. Кulр
Издательство: CRC Press
Год: 2024
Страниц: 505
Язык: английский
Формат: pdf (true)
Размер: 13.6 MB

This advanced undergraduate textbook presents a new approach to teaching mathematical methods for scientists and engineers. It provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses. Both analytical and computational examples are integrated from its start. Each chapter concludes with a set of problems designed to help students hone their skills in mathematical techniques, computer programming, and numerical analysis. The book places less emphasis on mathematical proofs, and more emphasis on how to use computers for both symbolic and numerical calculations. It contains 182 extensively documented coding examples, based on topics that students will encounter in their advanced courses in Mechanics, Electronics, Optics, Electromagnetism, Quantum Mechanics etc. An introductory chapter gives students a crash course in Python programming and the most often used libraries (SymPy, NumPy, SciPy, Matplotlib). This is followed by chapters dedicated to differentiation, integration, vectors and multiple integration techniques. The next group of chapters covers complex numbers, matrices, vector analysis and vector spaces.

Название: Google Gemini for Python: Coding with Bard
Автор: Оswаld Саmреsаtо
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 203
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.1 MB

This book provides a bridge between the worlds of Python 3 programming and Generative AI, aiming to equip readers with the skills to navigate both domains with confidence. It begins with an introduction to fundamental aspects of Python programming, which include various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. In addition, you will learn about loops, functions, data structures, NumPy, Pandas, conditional logic, and reserved words in Python. Further chapters show how to handle user input, manage exceptions, and work with command-line arguments. The text then transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including Bard (now called “Gemini”) and its competitors, are presented to give readers an understanding of the current AI landscape. The book discusses the capabilities of Bard, its strengths, weaknesses, and potential applications. Finally, you will learn how to generate a variety of Python 3 code samples via Bard. This book is intended primarily for people who want to learn both Python and how to use Bard with Python.