Advanced analytics spark patterns von ryza sandy (28 Ergebnisse)

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Paperback. Zustand: Very Good. In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.…

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Zustand: Very Good. Zustand des Schutzumschlags: 27557749. 1777024877. 4/24/2026 10:01:17 AM.

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Zustand: New. In English.

Paperback. Zustand: Brand New. 2nd edition. 330 pages. 9.00x7.00x0.50 inches. In Stock.

Paperback. Zustand: Brand New. 275 pages. 9.19x7.00x0.50 inches. In Stock.

Zustand: New. In English.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware - In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.…

Taschenbuch. Zustand: Neu. Neuware - The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming. Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection, to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing. If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis. - Familiarize yourself with Spark's programming model and ecosystem - Learn general approaches in data science - Examine complete implementations that analyze large public datasets - Discover which machine learning tools make sense for particular problems - Explore code that can be adapted to many uses. …