Course Material

Slides:

• Day1 - morning 9-12: Algorithm Design in MapReduce [pdf]
• Day1 - afternoon 14-17: Using Hadoop MapReduce, the basics and "Hello World"

• Day2 - morning 9-12: Hadoop Internals [pdf]
• Day2 - afternoon 14-17:Using Hadoop MapReduce, design patterns and algorithms

• Day3 - morning 9-12: Relational Algebra and Pig [pdf]
• Day3 - afternoon 14-17: Using Hadoop Pig, with applications to network data

LAB material:

• The material is provided on github
  • git clone https://github.com/bigfootproject/datascience.git
  • the repository also contains the exercices description
• The exercices and the homeworks can be done using a preconfigured virtual machine.

Reference material

• Jimmy Lin and Chris Dyer, “Data-Intensive Text Processing with MapReduce,” Morgan & Claypool Publishers, 2010. 
• Tom White, “Hadoop, The Definitive Guide,” O’Reilly / Yahoo Press, 2012
• Anand Rajaraman, Jeffrey D. Ullman, Jure Leskovec, “Mining of Massive Datasets”, Cambridge University Press, 2013

Additional material

• Prof. Martin Odersky (EPFL), Functional Programming Principles in Scala
• Prof. Bill Howe (U. Washington), Introduction to Data Science