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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2016.
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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... |
| Beschreibung: | 1 online resource (1 volume) : illustrations. |
| ISBN: | 9781785289385 1785289381 1785281372 9781785281372 |
Internformat
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | ZDB-4-EBA-ocn939394719 | ||
| 003 | OCoLC | ||
| 005 | 20241004212047.0 | ||
| 006 | m o d | ||
| 007 | cr unu|||||||| | ||
| 008 | 160215s2016 enka o 000 0 eng d | ||
| 040 | |a UMI |b eng |e rda |e pn |c UMI |d OCLCF |d TEFOD |d N$T |d DEBBG |d DEBSZ |d CEF |d NLE |d UKMGB |d AGLDB |d IGB |d UKAHL |d RDF |d QGK |d OCLCO |d OCLCQ |d OCLCO |d OCLCL |d OCLCQ | ||
| 015 | |a GBB6G3474 |2 bnb | ||
| 016 | 7 | |a 018010566 |2 Uk | |
| 019 | |a 1259102211 | ||
| 020 | |a 9781785289385 |q (electronic bk.) | ||
| 020 | |a 1785289381 |q (electronic bk.) | ||
| 020 | |z 9781785281372 | ||
| 020 | |a 1785281372 | ||
| 020 | |a 9781785281372 | ||
| 035 | |a (OCoLC)939394719 |z (OCoLC)1259102211 | ||
| 037 | |a CL0500000715 |b Safari Books Online | ||
| 037 | |a F1DD2F33-4126-4793-8399-380D24B525DC |b OverDrive, Inc. |n http://www.overdrive.com | ||
| 050 | 4 | |a QA76.73.S28 | |
| 072 | 7 | |a COM |x 051010 |2 bisacsh | |
| 072 | 7 | |a COM |x 051230 |2 bisacsh | |
| 082 | 7 | |a 005.133 |2 23 | |
| 049 | |a MAIN | ||
| 100 | 1 | |a Bugnion, Pascal, |e author. | |
| 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. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | |a Description based on online resource; title from cover page (Safari, viewed February 12, 2016). | ||
| 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... | ||
| 650 | 0 | |a Scala (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2010013203 | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 6 | |a Scala (Langage de programmation) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 7 | |a COMPUTERS / Programming Languages / General |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Software Development & Engineering / General |2 bisacsh | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Scala (Computer program language) |2 fast | |
| 776 | |z 1-78528-137-2 | ||
| 830 | 0 | |a Community experience distilled. |0 http://id.loc.gov/authorities/names/no2011030603 | |
| 856 | 4 | 0 | |l FWS01 |p ZDB-4-EBA |q FWS_PDA_EBA |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1163381 |3 Volltext |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH30053555 | ||
| 938 | |a EBSCOhost |b EBSC |n 1163381 | ||
| 994 | |a 92 |b GEBAY | ||
| 912 | |a ZDB-4-EBA | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-ocn939394719 |
|---|---|
| _version_ | 1816882339725180928 |
| adam_text | |
| any_adam_object | |
| author | Bugnion, Pascal |
| author_facet | Bugnion, Pascal |
| author_role | aut |
| author_sort | Bugnion, Pascal |
| author_variant | p b pb |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.73.S28 |
| callnumber-search | QA76.73.S28 |
| callnumber-sort | QA 276.73 S28 |
| callnumber-subject | QA - Mathematics |
| collection | ZDB-4-EBA |
| contents | 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 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 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 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 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 |
| ctrlnum | (OCoLC)939394719 |
| dewey-full | 005.133 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.133 |
| dewey-search | 005.133 |
| dewey-sort | 15.133 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>08061cam a2200661 i 4500</leader><controlfield tag="001">ZDB-4-EBA-ocn939394719</controlfield><controlfield tag="003">OCoLC</controlfield><controlfield tag="005">20241004212047.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr unu||||||||</controlfield><controlfield tag="008">160215s2016 enka o 000 0 eng d</controlfield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">UMI</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="e">pn</subfield><subfield code="c">UMI</subfield><subfield code="d">OCLCF</subfield><subfield code="d">TEFOD</subfield><subfield code="d">N$T</subfield><subfield code="d">DEBBG</subfield><subfield code="d">DEBSZ</subfield><subfield code="d">CEF</subfield><subfield code="d">NLE</subfield><subfield code="d">UKMGB</subfield><subfield code="d">AGLDB</subfield><subfield code="d">IGB</subfield><subfield code="d">UKAHL</subfield><subfield code="d">RDF</subfield><subfield code="d">QGK</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCL</subfield><subfield code="d">OCLCQ</subfield></datafield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">GBB6G3474</subfield><subfield code="2">bnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">018010566</subfield><subfield code="2">Uk</subfield></datafield><datafield tag="019" ind1=" " ind2=" "><subfield code="a">1259102211</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781785289385</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1785289381</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9781785281372</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1785281372</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781785281372</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)939394719</subfield><subfield code="z">(OCoLC)1259102211</subfield></datafield><datafield tag="037" ind1=" " ind2=" "><subfield code="a">CL0500000715</subfield><subfield code="b">Safari Books Online</subfield></datafield><datafield tag="037" ind1=" " ind2=" "><subfield code="a">F1DD2F33-4126-4793-8399-380D24B525DC</subfield><subfield code="b">OverDrive, Inc.</subfield><subfield code="n">http://www.overdrive.com</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">QA76.73.S28</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="x">051010</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="x">051230</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="7" ind2=" "><subfield code="a">005.133</subfield><subfield code="2">23</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">MAIN</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bugnion, Pascal,</subfield><subfield code="e">author.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Scala for data science :</subfield><subfield code="b">leverage the power of Scala to build scalable, robust data science applications /</subfield><subfield code="c">Pascal Bugnion.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, UK :</subfield><subfield code="b">Packt Publishing,</subfield><subfield code="c">2016.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (1 volume) :</subfield><subfield code="b">illustrations.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">text file</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Community experience distilled</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on online resource; title from cover page (Safari, viewed February 12, 2016).</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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...</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Scala (Computer program language)</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh2010013203</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Data mining.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh97002073</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Scala (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Exploration de données (Informatique)</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Programming Languages / General</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Software Development & Engineering / General</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Data mining</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Scala (Computer program language)</subfield><subfield code="2">fast</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">1-78528-137-2</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Community experience distilled.</subfield><subfield code="0">http://id.loc.gov/authorities/names/no2011030603</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1163381</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Askews and Holts Library Services</subfield><subfield code="b">ASKH</subfield><subfield code="n">AH30053555</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">1163381</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBA</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-4-EBA-ocn939394719 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:27:03Z |
| institution | BVB |
| isbn | 9781785289385 1785289381 1785281372 9781785281372 |
| language | English |
| oclc_num | 939394719 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (1 volume) : illustrations. |
| psigel | ZDB-4-EBA |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Packt Publishing, |
| record_format | marc |
| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Bugnion, Pascal, author. Scala for data science : leverage the power of Scala to build scalable, robust data science applications / Pascal Bugnion. Birmingham, UK : Packt Publishing, 2016. 1 online resource (1 volume) : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Community experience distilled Description based on online resource; title from cover page (Safari, viewed February 12, 2016). 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 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 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 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 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 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... Scala (Computer program language) http://id.loc.gov/authorities/subjects/sh2010013203 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Scala (Langage de programmation) Exploration de données (Informatique) COMPUTERS / Programming Languages / General bisacsh COMPUTERS / Software Development & Engineering / General bisacsh Data mining fast Scala (Computer program language) fast 1-78528-137-2 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1163381 Volltext |
| spellingShingle | Bugnion, Pascal Scala for data science : leverage the power of Scala to build scalable, robust data science applications / Community experience distilled. 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 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 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 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 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 Scala (Computer program language) http://id.loc.gov/authorities/subjects/sh2010013203 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Scala (Langage de programmation) Exploration de données (Informatique) COMPUTERS / Programming Languages / General bisacsh COMPUTERS / Software Development & Engineering / General bisacsh Data mining fast Scala (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2010013203 http://id.loc.gov/authorities/subjects/sh97002073 |
| title | Scala for data science : leverage the power of Scala to build scalable, robust data science applications / |
| title_auth | Scala for data science : leverage the power of Scala to build scalable, robust data science applications / |
| title_exact_search | Scala for data science : leverage the power of Scala to build scalable, robust data science applications / |
| title_full | Scala for data science : leverage the power of Scala to build scalable, robust data science applications / Pascal Bugnion. |
| title_fullStr | Scala for data science : leverage the power of Scala to build scalable, robust data science applications / Pascal Bugnion. |
| title_full_unstemmed | Scala for data science : leverage the power of Scala to build scalable, robust data science applications / Pascal Bugnion. |
| title_short | Scala for data science : |
| title_sort | scala for data science leverage the power of scala to build scalable robust data science applications |
| title_sub | leverage the power of Scala to build scalable, robust data science applications / |
| topic | Scala (Computer program language) http://id.loc.gov/authorities/subjects/sh2010013203 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Scala (Langage de programmation) Exploration de données (Informatique) COMPUTERS / Programming Languages / General bisacsh COMPUTERS / Software Development & Engineering / General bisacsh Data mining fast Scala (Computer program language) fast |
| topic_facet | Scala (Computer program language) Data mining. Scala (Langage de programmation) Exploration de données (Informatique) COMPUTERS / Programming Languages / General COMPUTERS / Software Development & Engineering / General Data mining |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1163381 |
| work_keys_str_mv | AT bugnionpascal scalafordatascienceleveragethepowerofscalatobuildscalablerobustdatascienceapplications |