Large Scale Data Analysis (Spring 2020)
Official course description:
Course info
Programme
Staff
Course semester
Exam
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.
Mandatory activities
Three out of the four assignments are mandatory activities. Students choose which assignments are mandatory. Feedback is given on the assignments, assuming that they are handed over before the associated deadline. If the assignments are submitted late, no feedback is given. Detailed description of the assignments and deadlines will be announced on the course page of LearnIT. Be aware: The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.
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, internal (7-trinsskala)
Exam variation:
C: Submission of written work
The exam will have questions covering material from the assignments and exercises (75%) and other course contents (25%).