Large Scale Data Analysis (Spring 2023)

Abstract

Turning the unprecedented amounts of data being collected today into useful information is well beyond the computing power of a single general purpose CPU core. It is, therefore, crucial to know and understand the methods and tools that are able to parallelize various data analysis tasks in an efficient way on multicore CPUs and on a cluster of machines.

With this goal in mind, this course first gives an overview of the popular parallel data processing platforms. Then, it dives into parallelizing various machine learning tasks.

Description

Turning the unprecedented amounts of data being collected today into useful information is well beyond the computing power of a single general purpose CPU core. It is, therefore, crucial to know and understand the methods and tools that are able to parallelize various data analysis tasks in an efficient way on multicore CPUs and on a cluster of machines. 
With this goal in mind, this course first gives an overview of the popular parallel data processing platforms. Then, it dives into parallelizing various machine learning tasks.

Formal prerequisites

The course is mandatory for BSc in Data Science fourth semester.
The course assumes that the students have taken an introductory course on data management or database systems.

Intended learning outcomes

After the course, the student should be able to:

• Select the right distributed data processing platform and the right subset of functionalities from such platforms for a given task
• Apply machine learning and data mining in a parallel setting
• Effectively combine different types of data analysis tasks (machine learning, traditional SQL, …) in a query
• Reason about the performance of data processing systems in a parallel setting

Learning activities

There will be 2 hour lectures a week covering the weekly topic, and 2 hour exercises a week covering coding exercises and activities that would guide students in their assignments and exam.

Three assignments will be given. Detailed description of the assignments and deadlines will be announced on the course page of LearnIT. Note that feedback will only be given on the assignments, assuming that they are handed over before the associated deadline. If the assignments are submitted late, no feedback is given.

Course literature

Large-Scale Machine Learning with Python. B.Sjardin, L.Massaron, A.Boschetti. Packt

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Lectures: 20%
• Exercises: 20%
• Assignments: 40%
• Exam with preparation: 20%

Ordinary exam

Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C11: Submission of written work

reexam

Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C11: Submission of written work

Time and date