Syllabus for Big Data in Life Sciences

Big data i biovetenskap

Syllabus

  • 5 credits
  • Course code: 3FB034
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N, Bioinformatics A1N, Technology A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle
    G1N: has only upper-secondary level entry requirements
    G1F: has less than 60 credits in first-cycle course/s as entry requirements
    G1E: contains specially designed degree project for Higher Education Diploma
    G2F: has at least 60 credits in first-cycle course/s as entry requirements
    G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    GXX: in-depth level of the course cannot be classified.

    Second cycle
    A1N: has only first-cycle course/s as entry requirements
    A1F: has second-cycle course/s as entry requirements
    A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    AXX: in-depth level of the course cannot be classified.

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2021-05-10
  • Established by: The Faculty Board of Science and Technology
  • Applies from: week 26, 2021
  • Entry requirements: 120 credits including (1) 15 credits in computer science, including 5 credits in programming, and 15 credits in biology, or (2) 15 credits within the Master's Programme in Bioinformatics, including 5 credits in programming. English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6").
  • Responsible department: Department of Pharmaceutical Biosciences
  • Other participating department(s): Faculty of Science and Technology

Learning outcomes

On completion of the course the student shall be able to:

  • Explain theories and methods that are relevant for handling and analysis of massive datasets in life science;
  • Use modern systems for handling and analysis of massive datasets in life science;
  • Analyze properties of data-intensive life science applications and based on this suggest suitable strategies and architectures to meet application needs;
  • Critically analyse, discuss and present solutions in writing and orally.

Content

Methodology in life science applications where concepts and methods for large scale data management are used. Processing of life science data using the programming model MapReduce such as Apache Spark. Batch system on computational clusters. Reproducible data analysis using workflow systems. Analysis using software containers and micro-service frameworks such as Kubernetes. Use of large-scale storage systems and virtualised environments. Applications in life science, for example genomics, proteomics, metabolomics, cell biology, pharmaceutical development, and AI/Machine learning.

Instruction

Lectures, guest lectures, laboratory work, seminars and group supervision. Laboratory work and seminar classes are mandatory.

Assessment

Written exam. Written and oral presentation of assignments. Active participation in seminars and laboratory work.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

This course cannot be included in the same degree as 1TD065 Big Data in Life Sciences.

Reading list

Reading list

Applies from: week 33, 2021

Some titles may be available electronically through the University library.