Scala: Guide for Data Science Professionals oleh Patrick R. Nicolas

Key FeaturesBuild data science and data engineering solutions with easeAn in-depth look at each stage of the data analysis process — from reading and collecting data to distributed analyticsExplore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulations, and source codeBook DescriptionScala is especially good for analyzing large sets of data as the scale of the task doesnt have any significant impact on performance. Scalas powerful functional libraries can interact with databases and build scalable frameworks — resulting in the creation of robust data pipelines.The first module introduces you to Scala libraries to ingest, store, manipulate, process, and visualize data. Using real world examples, you will learn how to design scalable architecture to process and model data — starting from simple concurrency constructs and progressing to actor systems and Apache Spark. After this, you will also learn how to build interactive visualizations with web frameworks.Once you have become familiar with all the tasks involved in data science, you will explore data analytics with Scala in the second module. Youll see how Scala can be used to make sense of data through easy to follow recipes. You will learn about Bokeh bindings for exploratory data analysis and quintessential machine learning with algorithms with Spark ML library. Youll get a sufficient understanding of Spark streaming, machine learning for streaming data, and Spark graphX.Armed with a firm understanding of data analysis, you will be ready to explore the most cutting-edge aspect of data science — machine learning. The final module teaches you the A to Z of machine learning with Scala. Youll explore Scala for dependency injections and implicits, which are used to write machine learning algorithms. Youll also explore machine learning topics such as clustering, dimentionality reduction, Naive Bayes, Regression models, SVMs, neural networks, and more.This learning path combines some of the best that Packt has to offer into one complete, curated package. It includes content from the following Packt products:Scala for Data Science, Pascal BugnionScala Data Analysis Cookbook, Arun ManivannanScala for Machine Learning, Patrick R. NicolasWhat you will learnTransfer and filter tabular data to extract features for machine learningRead, clean, transform, and write data to both SQL and NoSQL databasesCreate Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizationsLoad data from HDFS and HIVE with easeRun streaming and graph analytics in Spark for exploratory analysisBundle and scale up Spark jobs by deploying them into a variety of cluster managersBuild dynamic workflows for scientific computingLeverage open source libraries to extract patterns from time seriesMaster probabilistic models for sequential dataAbout the AuthorPascal Bugnion is a data engineer at the ASI, a consultancy offering bespoke data science services. Previously, he was the head of data engineering at SCL Elections. He holds a PhD in computational physics from Cambridge University.Besides Scala, Pascal is a keen Python developer. He has contributed to NumPy, matplotlib and IPython. He also maintains scikit-monaco, an open source library for Monte Carlo integration. He currently lives in London, UK.Arun Manivannan has been an engineer in various multinational companies, tier-1 financial institutions, and start-ups, primarily focusing on developing distributed applications that manage and mine data. His languages of choice are Scala and Java, but he also meddles around with various others for kicks. He blogs at http://rerun.me.Arun holds a masters degree in software engineering from the National University of Singapore.He also holds degrees in commerce, computer applications, and HR management. His interests and education could probably be a good dataset for clustering.Patrick R. Nicolas is a lead R&D engineer at Dell in Santa Clara, California. He has 25 years of experience in software engineering and building large-scale applications in C++, Java, and Scala, and has held several managerial positions. His interests include real-time analytics, modeling, and optimization.Table of ContentsScala and Data ScienceManipulating Data with BreezePlotting with breeze-vizParallel Collections and FuturesScala and SQL through JDBCSlick – A Functional Interface for SQLWeb APIsScala and MongoDBConcurrency with AkkaDistributed Batch Processing with SparkSpark SQL and DataFramesDistributed Machine Learning with MLlibWeb APIs with PlayVisualization with D3 and the Play FrameworkPattern Matching and ExtractorsGetting Started with BreezeGetting Started with Apache Spark DataFramesLoading and Preparing Data – DataFrameData VisualizationLearning from DataScaling UpGoing FurtherGetting StartedHello World!Data PreprocessingUnsupervised LearningNaive Bayes ClassifiersRegression and RegularizationSequential Data ModelsKernel Models and Support Vector MachinesArtificial Neural NetworksGenetic AlgorithmsReinforcement LearningScalable FrameworksBasic ConceptsBibliography