Valid from: Autumn 2015
Decided by: FN1/Anders Gustafsson
Date of establishment: 2015-09-08
Division: Computer Science (LTH)
Course type: Third-cycle course
Teaching language: English
This course will teach the doctoral students how to analyze and design programs for big data. It will provide knowledge on big data architectures, languages, and ecosystems with a focus on Spark. The techniques presented in the course are expected to have high impacts in a variety of fields such as data analysis, customer recommendation, trend prediction, pattern recognition, etc.
Knowledge and Understanding
For a passing grade the doctoral student must
Competences and Skills
For a passing grade the doctoral student must show her/his capability to operate big data architectures and design and write programs using Spark.
Judgement and Approach
For a passing grade the doctoral student must show the ability to select and assess architectures and algorithms for big data problems.
The course consists of four full-day sessions that will address: 1/ Cloud architectures, Spark concepts, and Spark programming. 2/ Intermediate and advanced Spark. 3/ Supervised machine-learning with Spark: MLlib and MLlib programming. 4/ Unsupervised machine learning.
Types of instruction: Lectures, laboratory exercises, exercises, project
Examination format: Written assignments.
The assessment will consist of programs and reports to hand in
Grading scale: Failed, pass
Examiner: Professor Pierre Nugues
Assumed prior knowledge: Good programming skills in Java, Scala, or Python. Knowledge of statistics
Minimum number of participants: 20
Start date: 2015-09-07.
Start date is approximate.
End date: 2015-10-30
Course coordinator: Pierre Nugues <firstname.lastname@example.org>