Cloudera Developer Blog · Data Science Posts

Estimating Financial Risk with Apache Spark

Learn how Spark facilitates the calculation of computationally-intensive statistics such as VaR via the Monte Carlo method.

Under reasonable circumstances, how much money can you expect to lose? The financial statistic value at risk (VaR) seeks to answer this question. Since its development on Wall Street soon after the stock market crash of 1987, VaR has been widely adopted across the financial services industry. Some organizations report the statistic to satisfy regulations, some use it to better understand the risk characteristics of large portfolios, and others compute it before executing trades to help make informed and immediate decisions.

Meet the Data Scientist: Sandy Ryza

Meet Sandy Ryza (@SandySifting), the newest member of Cloudera’s data science team. See Sandy present at Spark Summit 2014 (June 30-July 1 in San Francisco; register here for a 20% discount).

What is your definition of a “data scientist”?

Meet the Data Scientist: Alan Paulsen

Meet Alan Paulsen, among the first to earn the CCP: Data Scientist distinction.

Big Data success requires professionals who can prove their mastery with the tools and techniques of the Apache Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the Big Data talent gap with the Cloudera Certified Professional (CCP) program.

A New Python Client for Impala

The new Python client for Impala will bring smiles to Pythonistas!

As a data scientist, I love using the Python data stack. I also love using Impala to work with very large data sets. But things that take me out of my Python workflow are generally considered hassles; so it’s annoying that my main options for working with Impala are to write shell scripts, use the Impala shell, and/or transfer query results by reading/writing local files to disk.

Meet the Data Scientist: Stuart Horsman

Meet Stuart Horsman, among the first to earn the CCP: Data Scientist distinction.

Big Data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the Big Data talent gap with the Cloudera Certified Professional (CCP) program.

Meet the Data Scientist: David F. McCoy

Meet David F. McCoy, one of the first to have earned the title “CCP: Data Scientist” from Cloudera University.

Big Data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the Big Data talent gap with the Cloudera Certified Professional (CCP) program.

Why Apache Spark is a Crossover Hit for Data Scientists

Spark is a compelling multi-purpose platform for use cases that span investigative, as well as operational, analytics.

Data science is a broad church. I am a data scientist — or so I’ve been told — but what I do is actually quite different from what other “data scientists” do. For example, there are those practicing “investigative analytics” and those implementing “operational analytics.” (I’m in the second camp.)

How-to: Do Statistical Analysis with Impala and R

The new RImpala package brings the speed and interactivity of Impala to queries from R.

Our thanks to Austin Chungath, Sachin Sudarshana, and Vikas Raguttahalli of Mu Sigma, a Decision Sciences and Big Data analytics company, for the guest post below.

How-to: Use Cascading Pattern with R and CDH

Our thanks to Concurrent Inc. for the how-to below about using Cascading Pattern with CDH. Cloudera recently tested CDH 4.4 with the Cascading Compatibility Test Suite verifying compatibility with Cascading 2.2.

Cascading Pattern is a machine-learning project within the Cascading development framework used to build enterprise data workflows. Cascading provides an abstraction layer on top of Apache Hadoop and other computing topologies that allows enterprises to leverage existing skills and resources to build data processing applications on Hadoop, without the need for specialized Hadoop skills.

How-to: Use MADlib Pre-built Analytic Functions with Impala

Thanks to Victor Bittorf, a visiting graduate computer science student at Stanford University, for the guest post below about how to use the new prebuilt analytic functions for Cloudera Impala.

Cloudera Impala is an exciting project that unlocks interactive queries and SQL analytics on big data. Over the past few months I have been working with the Impala team to extend Impala’s analytic capabilities. Today I am happy to announce the availability of pre-built mathematical and statistical algorithms for the Impala community under a free open-source license. These pre-built algorithms combine recent theoretical techniques for shared nothing parallelization for analytics and the new user-defined aggregations (UDA) framework in Impala 1.2 in order to achieve big data scalability. This initial release has support for logistic regression, support vector machines (SVMs), and linear regression.

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