Data Science for Genomics
Course content
There are three major subject areas of the course:
- Usage of R in applied statistics and data handling: This will be used throughout the course
- Visualization, handling and analysis of genomic data using the genome browser, the Unix command line and R
- Expression analysis using microarrays and DNA sequencing using R and public tools.
MSc Programme in Biochemistry
MSc Programme in Bioinformatics
MSc Programme in Biology
MSc Programme in Biology with minor subject
MSc Programme in Molecular Biomedicine
The student will achieve the following from attending the
course:
Knowledge:
After successfully completing the course, students will master the
fundamentals of computational analysis of large biological
datasets. This includes:
i) understanding the diverse laboratory techniques and biological
processes generating the data
ii) understanding and mastering the statistical and informatics
techniques used for visualization and analysis, including the
selection of appropriate techniques for a given data and question
iii) interpreting analysis results in a biological context, and
identify and apply follow-up analyses based on this.
Skills:
The skill set taught in the course can be divided into:
- Applied statistics, visualization and data handling within R and the Unix command line
- Knowledge of molecular biology techniques that generate genomics data - cDNA analysis, ChIP, RNA-seq and more, and their strengths and weaknesses
- Visualization techniques for the data above: genome browsers and R
- Techniques for data mining and data exploration
There is a special focus on hands-on exercises to develop analysis
skills; both within lessons, group work and in the final
evaluation. We also have one day with speakers from industry that
use similar techniques.
Competences:
- To be able to analyze, visualize and interpret cutting edge biological data sets using biological and statistical toolsets combined.
- To solve realistic problems in which finding the appropriate methods - and the specific programming syntax necessary - for attacking sub-questions question is an important part of the problem.
Hybrid between lectures and computer exercises.
See Absalon.
Students should have a molecular biology background
corresponding to those of students in Bioinformatics or Biomedicine
master programs (for instance "Molecular biology for non-life
students" in block 1 or a life-science oriented bachelor
education). Moreover, skills in statistics and R corresponding to
"Statistics for Molecular Biomedicine" in block 3 is
necessary.
Academic qualifications equivalent to a BSc degree is
recommended.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written assignment, 5 daysOral examination, 30 minutes with no preparation time
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners/co-examiners
- Re-exam
-
Same as the ordinary exam.
Criteria for exam assessment
In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.
Single subject courses (day)
- Category
- Hours
- Lectures
- 32
- Class Instruction
- 3
- Preparation
- 60
- Practical exercises
- 31
- Project work
- 60
- Exam
- 20
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NBIK23000U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 4
- Schedulegroup
-
B
- Capacity
- 65
The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student. - Studyboard
- Study Board for the Biological Area
Contracting department
- Department of Biology
Contracting faculty
- Faculty of Science
Course Coordinator
- Albin Gustav Sandelin (5-6772686f7446686f7534717b346a71)
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Courseinformation of students