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Graduate courses

Departments' graduate courses for PhD-students.

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  Study programme, year:  1 2

Study programme syllabus for
MPDSC - DATA SCIENCE AND AI, MSC PROGR Academic year: 2019/2020
DATA SCIENCE OCH AI, MASTERPROGRAM
Associated to: TKITE
The Study programme syllabus is adopted 2019-02-21 by Dean of Education and is valid for students starting the programme the academic year 2019/2020
 

Entry requirements:
 

General entry requirements:

Basic eligibility for advanced level

 

Specific entry requirements:

 

English proficiency:

An applicant to a programme or course with English as language of instruction must prove a sufficient level of English language proficiency. The requirement is the Swedish upper secondary school English course 6 or B, or equivalent. For information on other ways of fulfilling the English language requirement please visit Chalmers web site.

 

Undergraduate profile:

Major in Engineering, Technology, Science or Mathematics.

 

Prerequisities:

Mathematics (at least 30cr., including Multivariable Analysis, Linear Algebra, and Mathematical Statistics), Programming in a General-Purpose Language (e.g. C/C++/Java/Haskell or similar (at least 7,5 cr.)), and Algorithms and/or Data Structures (at least 7,5 cr.)

 
General organization:
 

Aim:

The Data Science master programme aims at providing the students with a good theoretical knowledge and practical skills regarding mathematical, statistical and computer science methods to manage, analyze and extract information from large scale data sets, as well as develop tools and algorithms in complex, computer intense and AI related applications.

 

Learning outcome:

Knowledge and understanding


On successful completion of the programme the student will be able to

  • describe what data science and machine learning is and can be in terms of question
    formulations, models and methods and their pros and cons,

  • master necessary tools and concepts, such as within probability theory, statistics,
    optimization, algorithms and software architecture, at a level where they can
    modify and develop them in the desired direction,

  • apply their practical experiences of modelling through implementation, simulation,
    analysis and testing of the models,
  • give a detailed account of the most up-to-date question formulations, the most
    recent technology and the most recent research results within a subdomain.


Skills and abilities


On successful completion of the programme the student will be able to

  • participate in the development of intelligent and automated digital
    systems, thereby improving, accelerating, and amplifying the
    digitalisation of society
  • create models of concrete problems and balance the model complexity against the amount
    and quality of data, as well as against time and computational power,

  • deduce and apply the methods in optimization and statistics, required for a correct
    analysis of the model behavior and experimental results,

  • create and implement efficient algorithms for analysis and simulation,

  • master field relevant programming languages and tools as well as be able to deal with
    different data formats,

  • quickly absorb and implement new knowledge in a changeable field,

  • communicate their results and conclusions verbally, in writing and visually to both experts
    and non-experts.


Judgement and approach


On successful completion of the programme the student will be able to

  • critically analyze models and algorithms with regards to efficiency and reliability,

  • give an account of the possibilities and limitations of the technology, and be
    responsible for its ethical, societal and environmental sustainability, and
    thereby contribute to a discussion of its role in the society.

 

Extent: 120.0 c

 

Thesis:

The programme includes a degree project (Master's thesis) corresponding to 30 credits. The rules for starting and carrying out the the degree project are described in the corresponding course plans for the second year of the study programme.

 

Courses valid the academic year 2019/2020:

See study programme

 

Accredited to the following programmes the accademic year 2019/2020:


Degree of Master of Science in Engineering
TKATK - ARCHITECTURE AND ENGINEERING
TKAUT - AUTOMATION AND MECHATRONICS ENGINEERING
TKDAT - COMPUTER SCIENCE AND ENGINEERING
TKELT - ELECTRICAL ENGINEERING
TKTEM - ENGINEERING MATHEMATICS
TKTFY - ENGINEERING PHYSICS
TKIEK - INDUSTRIAL ENGINEERING AND MANAGEMENT
TKITE - SOFTWARE ENGINEERING

 
Degree:
 Degree requirements:
  Degree of master of science (120 credits):
Passed courses comprising 120 credits
Passed advanced level courses (including degree project) comprising at least 90 credits
Degree project 30 credits
Advanced level courses passed at Chalmers comprising at least 45 credits
Courses (including degree project) within a major main subject 60 credits
Fulfilled course requirements according to the study programme
The prior award of a Bachelors degree, Bachelors degree in fine arts, professional or vocational qualification of at least 180 credits or a corresponding qualification from abroad.

See also the Local Qualifications Framework - first and second cycle qualifications
 

Title of degree:

Master of Science (120 credits). The name of the Master's programme and the major subject Information Technology are stated in the degree certificate. Specializations and tracks are not stated.

 

Major subject:

Software Engineering

 
Other information:
 

The programme syllabus for the second year will be published for the academic year 2020/2021.

 

More information about the programme (url):

https://www.chalmers.se/en/education/programmes/masters-info/Pages/Data-Science.aspx


Page manager Published: Thu 04 Feb 2021.