Analiza danych
Analiza danych jest ekscytującą dyscypliną, która umożliwia zrozumienie pewnych zjawisk, uzyskanie wglądu i wiedzy na podstawie surowych danych. Pojęcie to oznacza dokładnie przetwarzanie danych za pomocą technik matematycznych i statystycznych w celu uzyskania cennych wniosków, podjęcia ważnych decyzji i opracowania przydatnych produktów. Termin ten wywodzi się od angielskiego data science, często traktowanego jako synonim takich terminów, jak analityka biznesowa, badania operacyjne, business intelligence, wywiad konkurencyjny, analiza i modelowanie danych, a także pozyskiwanie wiedzy. Dzięki takim technologiom, jak języki Python czy R, platformy Hadoop i Spark masz szansę wyciągnąć maksimum wniosków, dostrzec szanse na rozwój swojej organizacji albo przewidzieć i zapobiec zagrożeniom.
Feature Store for Machine Learning. Curate, discover, share and serve ML features at scale
Jayanth Kumar M J
Feature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started.Feature Store for Machine Learning is for data scientists who want to learn how to use feature stores to share and reuse each other's work and expertise. You’ll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model. While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time.By the end of this book, you’ll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud.
MrExcel's Holy Macro! Books, Liam Bastick, Oscar...
This book is a practical guide for mastering financial modeling in project finance, providing a clear journey from foundational concepts to advanced techniques. It begins by introducing project finance, its significance, and how it differs from other finance structures. Readers will learn key Excel functions, data validation, and layout strategies essential for creating accurate and dynamic models.As the journey progresses, the book emphasizes best practices for building transparent, flexible, and robust models. It covers linked financial statements, cash flow waterfalls, debt structuring, and valuation techniques. A comprehensive case study walks readers through the construction of a full project finance model, separating construction and operational phases while integrating advanced concepts like scenario planning, sensitivity analysis, and ratio metrics.Designed with a logical flow, this book equips readers with practical skills to tackle real-world financial challenges. From Excel tips to project valuation and funding strategies, it provides actionable insights for analysts, finance professionals, and project managers seeking to excel in project finance modeling.
Financial Modelling in Power BI. Master Subtotals, Functions, and Advanced Excel Tricks in Minutes!
MrExcel's Holy Macro! Books, Jonathan Liau, Liam...
This book introduces readers to the fundamentals of financial modeling using Power BI, starting with an overview of the tool and best practices for creating robust, transparent, and flexible models. Early chapters lay the groundwork by explaining financial statement theory and control accounts, essential concepts for any financial analyst. Readers are guided step-by-step through creating parameters and calculating sales, ensuring a solid foundation in Power BI's core functionalities.As the book progresses, readers delve into more advanced topics such as inventory calculations, operating and capital expenditures, and tax computations. Practical examples and hands-on exercises make complex concepts like DAX functions, FIFO inventory modeling, and control account measures accessible to users of all experience levels. Detailed sections on cash flow statements, income statements, and balance sheets tie the lessons together, showing how these elements integrate into a comprehensive financial model.The final chapters explore advanced features like interest and debt modeling, recursion aversion, and equity calculations, culminating in the creation of fully dynamic and optimized models. Readers also learn to design compelling visualizations to present financial insights effectively. By the end of the journey, users will have the tools and confidence to apply their knowledge to real-world scenarios, mastering financial modeling with Power BI.
Financial Modelling using Dynamic Arrays. Let Lambdas Extend Your Range
MrExcel's Holy Macro! Books, Liam Bastick
Dive into the transformative power of Excel's dynamic arrays in financial modelling. Learn to optimize formulas with LET, create reusable LAMBDA functions, and craft sophisticated models. The book provides a comprehensive introduction to Excel’s dynamic arrays, comparing legacy methodologies with modern capabilities while integrating practical tips and best practices.Through real-world examples and step-by-step tutorials, you’ll uncover the full potential of functions like SORT, FILTER, SEQUENCE, and LAMBDA. Discover how dynamic arrays reduce errors, boost efficiency, and enable innovative approaches to financial modelling. The book also highlights advanced features like eta lambdas and helper functions, offering a deep dive into the cutting-edge tools now available in Excel 365.Whether you’re building complex financial models or just looking to refine your techniques, this guide equips you with the knowledge to transform your processes. Excel enthusiasts and professionals alike will appreciate the clarity and depth this book provides, helping you elevate your modelling game to a whole new level.
Frank Kane
Frank Kane’s Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you’ll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python.Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease.
Fundamentals of Analytics Engineering. An introduction to building end-to-end analytics solutions
Dumky De Wilde, Fanny Kassapian, Jovan Gligorevic,...
Written by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer.After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance.By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.
Kailash Ahirwar
Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain.Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation.By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.
Generative Deep Learning with Python. Unleashing the Creative Power of AI by Mastering AI and Python
Cuantum Technologies LLC
Generative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation.The course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field.Throughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.