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Scala for data science : leverage the power of Scala to build scalable, robust data science applications /
Leverage the power of Scala with different tools to build scalable, robust data science applications About This Book A complete guide for scalable data science solutions, from data ingestion to data visualization Deploy horizontally scalable data processing pipelines and take advantage of web framew...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2016.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Scala for data science : |b leverage the power of Scala to build scalable, robust data science applications / |c Pascal Bugnion. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2016. | |
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| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Scala and Data Science; Data science; Programming in data science; Why Scala?; Static typing and type inference; Scala encourages immutability; Scala and functional programs; Null pointer uncertainty; Easier parallelism; Interoperability with Java; When not to use Scala; Summary; References; Chapter 2: Manipulating Data with Breeze; Code examples; Installing Breeze; Getting help on Breeze; Basic Breeze data types; Vectors; Dense and sparse vectors and the vector trait | |
| 505 | 8 | |a MatricesBuilding vectors and matrices; Advanced indexing and slicing; Mutating vectors and matrices; Matrix multiplication, transposition, and the orientation of vectors; Data preprocessing and feature engineering; Breeze -- function optimization; Numerical derivatives; Regularization; An example -- logistic regression; Towards re-usable code; Alternatives to Breeze; Summary; References; Chapter 3: Plotting with breeze-viz; Diving into Breeze; Customizing plots; Customizing the line type; More advanced scatter plots; Multi-plot example -- scatterplot matrix plots; Managing without documentation | |
| 505 | 8 | |a Breeze-viz referenceData visualization beyond breeze-viz; Summary; Chapter 4: Parallel Collections and Futures; Parallel collections; Limitations of parallel collections; Error handling; Setting the parallelism level; An example -- cross-validation with parallel collections; Futures; Future composition -- using a future's result; Blocking until completion; Controlling parallel execution with execution contexts; Futures example -- stock price fetcher; Summary; References; Chapter 5: Scala and SQL through JDBC; Interacting with JDBC; First steps with JDBC; Connecting to a database server | |
| 505 | 8 | |a Creating tablesInserting data; Reading data; JDBC summary; Functional wrappers for JDBC; Safer JDBC connections with the loan pattern; Enriching JDBC statements with the ""pimp my library"" pattern; Wrapping result sets in a stream; Looser coupling with type classes; Type classes; Coding against type classes; When to use type classes; Benefits of type classes; Creating a data access layer; Summary; References; Chapter 6: Slick -- A Functional Interface for SQL; FEC data; Importing Slick; Defining the schema; Connecting to the database; Creating tables; Inserting data; Querying data; Invokers | |
| 505 | 8 | |a Operations on columnsAggregations with ""Group by""; Accessing database metadata; Slick versus JDBC; Summary; References; Chapter 7: Web APIs; A whirlwind tour of JSON; Querying web APIs; JSON in Scala -- an exercise in pattern matching; JSON4S types; Extracting fields using XPath; Extraction using case classes; Concurrency and exception handling with futures; Authentication -- adding HTTP headers; HTTP -- a whirlwind overview; Adding headers to HTTP requests in Scala; Summary; References; Chapter 8: Scala and MongoDB; MongoDB; Connecting to MongoDB with Casbah; Connecting with authentication | |
| 520 | |a Leverage the power of Scala with different tools to build scalable, robust data science applications About This Book A complete guide for scalable data science solutions, from data ingestion to data visualization Deploy horizontally scalable data processing pipelines and take advantage of web frameworks to build engaging visualizations Build functional, type-safe routines to interact with relational and NoSQL databases with the help of tutorials and examples provided Who This Book Is For If you are a Scala developer or data scientist, or if you want to enter the field of data science, then this book will give you all the tools you need to implement data science solutions. What You Will Learn Transform and filter tabular data to extract features for machine learning Implement your own algorithms or take advantage of MLLib's extensive suite of models to build distributed machine learning pipelines Read, transform, and write data to both SQL and NoSQL databases in a functional manner Write robust routines to query web APIs Read data from web APIs such as the GitHub or Twitter API Use Scala to interact with MongoDB, which offers high performance and helps to store large data sets with uncertain query requirements Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive In Detail Scala is a multi-paradigm programming language (it supports both object-oriented and functional programming) and scripting language used to build applications for the JVM. Languages such as R, Python, Java, and so on are mostly used for data science. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists. Data scientists might be aware that building applications that are truly scalable is hard. Scala, with its powerful functional libraries for interacting with databases and building scalable frameworks will give you the tools to construct robust data pipelines. This book will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Packed with real-world examples and interesting data sets, this book will teach you to ingest data from flat files and web APIs and store it in a SQL or NoSQL database. It will show you how to design scalable architectu... | ||
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