• Graduate Program
    • Why study Business Data Science?
    • Program outline
    • Courses
    • Course registration
    • Admissions
    • Facilities
      • Student Offices
      • Location
      • Housing
      • Student Council
  • Research
  • News
  • Events
    • Events Calendar
    • Events archive
    • Summer School
      • Behavioral Decision Making
      • Deep Learning
      • Econometrics and Data Science Methods for Business and Economics and Finance
      • Foundations of Data Analysis and Machine Learning in Python
      • Introduction to Genome-Wide Data Analysis
      • Reinforcement Learning
      • Tinbergen Institute Summer School Program
  • Summer School
  • Alumni
Home | Courses | Parallel Computing and Big data

Parallel Computing and Big data

  • Teacher(s)
    Jeroen Engelberts
  • Research field
    Data Science
  • Dates
    Period 2 - Oct 25, 2021 to Dec 17, 2021
  • Course type
  • Program year
  • Credits

Course description

Nowadays, even mobile phones and tablets have multiple core central processing units (CPUs), as do have the simplest laptop and desktop PCs. Using their combined compute power, however, is not trivial. This is as true for the small systems, as well as (worlds) largest compute systems. In data science, making use efficiently of all compute power is a required skill that needs to be learned. In this course you will be taught how to have all cores take part in a single task, or to have each core working on its own share of the total task. Modern day researchers quite often have to rely on larger systems than their own. In the Netherlands many use the national supercomputer clusters, Lisa and Cartesius, at SURFsara. Like most other large shared computer systems in research, these systems have UNIX, or Linux, running as operating system. On top of that, many of them make use of a batch system to give multiple users a fair share of the total resources. During the course, students will get hands-on experience with UNIX and batch systems. After the practicing with the batch system, the different types of parallel programming will be taught with Python as programming language. Although C and Fortran are very common in high-performance computing (HPC), it is also possible to use parallelism in Python, the language of choice for many researchers in the data science field. The contents of this course comprise a BASH (Unix shell) course, a Python recap, an introduction to Jupyter Notebooks and a programming course to learn how to work with different parallel modules and packages in Python. For the latter, the “Python Parallel Programming Cookbook” is used. Although referred to as a cookbook, it has a decent amount of theory to build a foundation for deeper understanding of parallel paradigms.


Programming Basics, Mathematics, Statistics

Course literature

The following list of mandatory readings (presented in alphabetical order) are considered essential for your learning experience. These articles are also part of the exam material. Changes in the reading list will be communicated on CANVAS.


Zaccone (2019) – Python Parallel Programming Cookbook, 2nd Edition, Packt Publishing, ISBN-13: 978-1-78953-373-6 (https://learning.oreilly.com/library/view/python-parallel-programming/9781789533736/)