• Partnership for Advanced Computing in Europe (PRACE)

Managing Big Data with R and Hadoop

Learn how to manage and analyse big data using the R programming language and Hadoop programming framework.

13,629 enrolled on this course

Management of Massive Data Managing Big Data with R and Hadoop

You will experience how to use RHadoop tool to manage and analyse big data.

This course will give you access to a virtual environment with installations of Hadoop, R and Rstudio to get hands-on experience with big data management. Several unique examples from statistical learning and related R code for map-reduce operations will be available for testing and learning.

Those with basic knowledge in statistical learning and R will better understand the methods behind and how to run them in parallel using map-reduce functions and Hadoop data storage. At the end of the course you will get access to RHadoop on a supercomputer at University of Ljubljana.

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Skip to 0 minutes and 25 seconds Nearly every historical period may be said to have had sources of data that were considered big for that time. Books, documents, drawings, maps and paintings are examples of such data. Yet it is only today that we have to deal with really big data. Luckily, more and more data is digital, but expressed in different formats. Large-scale scientific instruments, social network platforms, cloud solutions, digital cultural heritage are only a few examples of sources of huge amount of text, photo, video and audio materials which are considered big data.

Skip to 0 minutes and 55 seconds But questions related to data have not changed much: how to store and maintain it, how to understand and how to learn from the data for an improved response in the future. These issues necessarily involve the use of high performance computers. Distributed storage and parallel computing need be considered to avoid loss of data and to make computations efficient.

Skip to 1 minute and 16 seconds Join us and cope with big data using R and RHadoop.

What topics will you cover?

  • Welcome to BIG DATA
  • Working with Hadoop
  • First steps in R and RHadoop
  • Statistical learning with RHadoop: clustering
  • Statistical learning with RHadoop: regression and classification

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

  • Available now

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Explore basic functionality of Apache Hadoop and of RHadoop
  • Experiment how to achieve performance of modern supercomputing
  • Experiment regression, clustering and classification with RHadoop
  • Investigate basic functionality of Bash terminal window
  • Knowledge about statistical learning to instances of data provided by edcators
  • How to do big data management with RHadoop on real supercomputer provided by University of Ljubljana

Who is the course for?

This course is designed for people interested in data science, computational statistics and machine learning and have basic experiences with them. It will be also useful for advanced undergraduate students and first year PhD students in data analysis, statistics or bioinformatics, who wish to understand how to manage big data with Hadoop using R programming language.

We expect that the learners will also have basic experiences with linux and bash and working experiences with R and matrix operations. They should be also capable to download and run virtual machine.

What software or tools do you need?

All software needed to actively participate the course is provided within the virtual machine that the followers are supposed to download and run on the local machine. No extra software is needed. You will need a modest local machine with 15GB free disk space and 2GB RAM.

Who will you learn with?

I am an active researcher in mathematical optimization, which has many applications in data science and where HPC is an inevitable tool.

Biljsna Mileva Boshkoska is an assistant professor in computer science. Her interests include decision support systems, data mining and working with big data.

Leon Kos is a 25+ years
veteran of using Linux desktop on a daily basis to build digital
relationships for research, teaching, and getting the job done by programming.

Who developed the course?

Partnership for Advanced Computing in Europe (PRACE)

The Partnership for Advanced Computing in Europe (PRACE) is an international non-profit association with its seat in Brussels.

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The University of Ljubljana logo
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  • Digital certificate when you're eligible

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$84

One-off payment

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