Coursebooks

Systems for data science

CS-449

Lecturer(s) :

Koch Christoph

Language:

English

Summary

The course covers fundamental principles for understanding and building systems for managing and analyzing large amounts of data.

Content

Programming methods, including parallel programming:

Big data systems design and implementation:

Changing data:

Online / Streaming / Real-time analytics:

Keywords

Databases, data-parallel programming, NoSQL systems, query processing.

Learning Prerequisites

Required courses

CS-322: Introduction to database systems

Recommended courses

CS-323: Introduction to operating systems

CS-206 Parallelism and concurrency

Important concepts to start the course

Learning Outcomes

By the end of the course, the student must be able to:

Teaching methods

Ex cathedra; including exercises in class, practice with pen and paper or with a computer, and a project

Expected student activities

During the semester, the students are expected to:

Assessment methods

Project (30%); final exam in August (70%)

Supervision

Office hours Yes
Assistants Yes
Forum Yes
Others Office ours by appointment

Resources

Bibliography

Relevant resources (textbook chapters, articles, and videos) posted on moodle page.

In the programs

  • Data Science, 2019-2020, Master semester 2
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      6
    • Subject examined
      Systems for data science
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Project
      2 Hour(s) per week x 14 weeks
  • Data Science, 2019-2020, Master semester 4
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      6
    • Subject examined
      Systems for data science
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Project
      2 Hour(s) per week x 14 weeks
  • Computational science and Engineering, 2019-2020, Master semester 2
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      6
    • Subject examined
      Systems for data science
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Project
      2 Hour(s) per week x 14 weeks
  • Computational science and Engineering, 2019-2020, Master semester 4
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      6
    • Subject examined
      Systems for data science
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Project
      2 Hour(s) per week x 14 weeks
  • Data science minor, 2019-2020, Spring semester
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      6
    • Subject examined
      Systems for data science
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Project
      2 Hour(s) per week x 14 weeks

Reference week

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
Under construction
Lecture
Exercise, TP
Project, other

legend

  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German