Link to Content:

Advanced Analytics with Spark

Created/Published/Taught by:

Sandy Ryza

Uri Laserson

Sean Owen

Josh Wills

O'Reilly

Content Found Via:

Ayman Abuelela

Free? No

Cost Range:

$19.38 - $28.99

Tags: java / python / scala / Spark

Difficulty Rating:

From Amazon:

“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 Audioscrobber 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-occurence 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”

Recommended Prerequisites: none specified

Go to Content: Advanced Analytics with Spark: Patterns for Learning from Data at Scale

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