Scala is now an established programming language developed by Martin Oderskey and his team at the EPFL. The name Scala is derived from Sca(lable) La(nguage). Scala is a multi-paradigm language, incorporating object oriented approaches with functional programming. Although some familiarity with standard computing concepts is assumed (such as the idea of compiling a program and executing this compiled from etc.) and with basic procedural language concepts (such as variables and allocation of values to these variables) the early chapters of the book do not assume any familiarity with object orientation nor with functional programming These chapters also step through other concepts with which the reader may not be familiar (such as list processing). From this background, the book provides a practical introduction to both object and functional approaches using Scala. These concepts are introduced through practical experience taking the reader beyond the level of the language syntax to the philosophy and practice of object oriented development and functional programming. Students and those actively involved in the software industry will find this comprehensive introduction to Scala invaluable.

This book gives an introduction to the programming language Scala. It presents it from a functional programming perspective. The book explains with detail functional programming and recursivity, and includes chapters on lazy and eager evaluation, streams, higher-order functions (including map, fold, reduce, and aggregate), and algebraic data types. The book also describes the object-oriented aspects of Scala, as they are a fundamental part of the language. In addition, the book includes a chapter on parallelism in Scala, giving an overview of the actor model.

This textbook is aimed at readers who have little or no knowledge of computer programming but want to learn to program in Python. It starts from the very basics including how to install your Python environment, how to write a very simple program and run it, what a variable is, what an if statement is, how iteration works using for and while loops as well as important key concepts such as functions, classes and modules. Each subject area is prefaced with an introductory chapter, before continuing with how these ideas work in Python. The second edition has been completely updated for the latest versions of Python including Python 3.11 and Python 3.12. New chapters have been added such as those that consider where and how Python is used, the use of Frozensets, how data can be sorted, enumerated types in Python, structural pattern matching and how (and why) Python Virtual Environments are configured. A new chapter ‘The Python Bites back’ is introduced to present the fourteen most common / biggest gotchas for someone new to Python. Other sections have been updated with new features such as Exception Groups, string operations and dictionary operations. A Beginners Guide to Python 3 Programming second Edition provides all you need to know about Python, with numerous examples provided throughout including several larger worked case studies illustrating the ideas presented in the previous chapters.

Follows a hands-on approach and offers practical lessons and tutorials related to Scala. Includes detailed tutorials meant for beginners to Scala. Discusses Scala in-depth to help build robust knowledge.

Get up to speed on Scala--the JVM, JavaScript, and natively compiled language that offers all the benefits of functional programming, a modern object model, and an advanced type system. Packed with code examples, this comprehensive book shows you how to be productive with the language and ecosystem right away. You'll learn why Scala is ideal for building today's highly scalable, data-centric applications while maximizing developer productivity. While Java remains popular and Kotlin has become popular, Scala hasn't been sitting still. This third edition covers the new features in Scala 3 with updates throughout the book. Programming Scala is ideal for beginning to advanced developers who want a complete understanding of Scala's design philosophy and features with a thoroughly practical focus. Program faster with Scala's succinct and flexible syntax Dive into basic and advanced functional programming techniques Build killer big data and distributed apps using Scala's functional combinators and tools like Spark and Akka Create concise solutions to challenging design problems with the sophisticated type system, mixin composition with traits, pattern matching, and more

Why learn Scala? You don’t need to be a data scientist or distributed computing expert to appreciate this object-oriented functional programming language. This practical book provides a comprehensive yet approachable introduction to the language, complete with syntax diagrams, examples, and exercises. You’ll start with Scala's core types and syntax before diving into higher-order functions and immutable data structures. Author Jason Swartz demonstrates why Scala’s concise and expressive syntax make it an ideal language for Ruby or Python developers who want to improve their craft, while its type safety and performance ensures that it’s stable and fast enough for any application. Learn about the core data types, literals, values, and variables Discover how to think and write in expressions, the foundation for Scala's syntax Write higher-order functions that accept or return other functions Become familiar with immutable data structures and easily transform them with type-safe and declarative operations Create custom infix operators to simplify existing operations or even to start your own domain-specific language Build classes that compose one or more traits for full reusability, or create new functionality by mixing them in at instantiation

Build powerful software solutions and develop proficiency in Haskell, from understanding the foundational principles through to mastering advanced functional programming concepts Key Features Learn from an expert lecturer and researcher who knows all the ins and outs of Haskell Develop a clear understanding of Haskell, from the basics through to advanced concepts Get to grips with all the key functional programming techniques Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWith software systems reaching new levels of complexity and programmers aiming for the highest productivity levels, software developers and language designers are turning toward functional programming because of its powerful and mature abstraction mechanisms. This book will help you tap into this approach with Haskell, the programming language that has been leading the way in pure functional programming for over three decades. The book begins by helping you get to grips with basic functions and algebraic datatypes, and gradually adds abstraction mechanisms and other powerful language features. Next, you’ll explore recursion, formulate higher-order functions as reusable templates, and get the job done with laziness. As you advance, you’ll learn how Haskell reconciliates its purity with the practical need for side effects and comes out stronger with a rich hierarchy of abstractions, such as functors, applicative functors, and monads. Finally, you’ll understand how all these elements are combined in the design and implementation of custom domain-specific languages for tackling practical problems such as parsing, as well as the revolutionary functional technique of property-based testing. By the end of this book, you’ll have mastered the key concepts of functional programming and be able to develop idiomatic Haskell solutions.What you will learn Write pure functions in all their forms – that is basic, recursive, and higher-order functions Model your data using algebraic datatypes Master Haskell’s powerful type-class mechanism for ad hoc overloading Find out how Haskell’s laziness gets the job done Reconcile Haskell’s functional purity with side effects Familiarize yourself with the functor, applicative functor, monad hierarchy Discover how to solve problems with domain-specific languages Find more bugs with Haskell’s property-based testing approach Who this book is for If you are a programmer looking to gain knowledge of Haskell who’s never been properly introduced to functional programming, this book is for you. Basic experience with programming in a non-functional language is a prerequisite. This book also serves as an excellent guide for programmers with limited exposure to Haskell who want to deepen their understanding and foray further into the language.

This textbook assumes very little knowledge of programming so whether you have dabbled with a little JavaScript, played with a bit of Python, written Java or have virtually no programming experience at all you will find that it is for you. The first part of the book introduces Kotlin program structures as well as conditional flow of control features such as if and when expressions as well as iteration loops such as for, while and do-while. Subsequent chapters explain how functions are implemented in Kotlin and introduce concepts from functional programming such as higher order functions and curried functions. The second part focusses on object oriented programming techniques, these include classes, inheritance, abstraction and interfaces. The third part presents container data types such as Arrays, and collections including Lists, Sets and Maps and the fourth part considers concurrency and parallelism using Kotlin coroutines. The book concludes with an introduction to Android mobile application development using Kotlin. Clear steps are provided explaining how to set up your environment and get started writing your own Kotlin programs. An important aspect of the book is teaching by example and there are many examples presented throughout the chapters. These examples are supported by a public GitHub repository that provides complete working code as well as sample solutions to the chapter exercises. This helps illustrate how to write well structured, clear, idiomatic Kotlin to build real applications.

Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert. Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You’ll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, like Hive, Avro, Kafka and so on. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language. There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost—possibly a big boost—to your career.

Supervised and unsupervised machine learning made easy in Scala with this quick-start guide. Key FeaturesConstruct and deploy machine learning systems that learn from your data and give accurate predictionsUnleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j libraryBook Description Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala. What you will learnGet acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4jLearn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured dataUnderstand supervised and unsupervised learning techniques with best practices and pitfallsLearn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques Learn effective ways of clustering analysis with dimensionality reduction techniquesLearn recommender systems with collaborative filtering approachDelve into deep learning and neural network architecturesWho this book is for This book is for machine learning developers looking to train machine learning models in Scala without spending too much time and effort. Some fundamental knowledge of Scala programming and some basics of statistics and linear algebra is all you need to get started with this book.