BMB830: Biostatistics in R I

Study Board of Science

Teaching language: English
EKA: N210013112, N210013102
Censorship: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N210013101
ECTS value: 5

Date of Approval: 29-04-2019


Duration: 1 semester

Version: Approved - active

Comment

01014301 (former UVA) is identical with this course description. 

Entry requirements

None

Academic preconditions

Students taking the course are expected to:

  • Have knowledge in statistics 
  • Understand the basic principles of molecular biology

Course introduction

Modern experimental platforms generate large sets of often noisy data that requires its processing by appropriate analytic and statistical methods. High-confidence data interpretation is built upon correct application of methods such as statistical models and pattern recognition. Furthermore, proper visualization of the results helps presenting and understanding the results. This course introduces the students to the main concepts of biostatistics, data analysis and visualization, so they understand the principles to design and apply work flows that handle a certain data type. The course will have a theoretical and a practical part, with the objective to provide general understanding of data analysis and application of bioinformatics tools.

Among currently available software suits, the R scripting language became very popular to deal with biostatistics and analysis of large data sets, as it (i) provides a vast number of statistical tools, (ii) allows adaptation of the analysis to any experimental design, (iii) offers simple commands to operate on entire data sets, (iv) provides a wide range of methods for data visualization and (v) has a large and active community of researchers developing new tools. However, it requires the user to acquire scripting skills to take advantage of the many features. 

The course will introduce the students to basic programming of R scripts, data visualization and basic statistical models necessary to deal with data from modern high-throughput experiments.

Expected learning outcome

The learning objectives of the course are that the student demonstrates the ability to:
  • independently analyze biological data sets. 
  • work with large data amounts and carry out standard statistical analysis to identify relevant features. 
  • use standard algorithms for multi-variate analysis
  • design scripts for detailed visualization of their results. 
  • apply tools for data interpretation.
  • know how to objectively discuss applied data analysis methods presented e.g. in publications.

Content

The following main topics are contained in the course:
  • basic probability
  • different types of data modeling
  • basic statistical models
  • data visualization
  • data interpretation
  • basic multi-variate analysis

Literature

See Blackboard for syllabus lists and additional literature references.

Examination regulations

Prerequisites for participating in the exam a)

Timing

Autumn

Tests

Tutorial and exercises

EKA

N210013112

Censorship

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

0

Additional information

The prerequisite examination is a prerequisite for participation in exam element a)

Exam element a)

Timing

January

Prerequisites

Type Prerequisite name Prerequisite course
Examination part Prerequisites for participating in the exam a) N210013101, BMB830: Biostatistics in R I

Tests

Oral examination

EKA

N210013102

Censorship

Second examiner: External

Grading

7-point grading scale

Identification

Student Identification Card

Language

Normally, the same as teaching language

Examination aids

To be announced during the course 

ECTS value

5

Additional information

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

36 - hours per semester

Teaching Method


Teacher responsible

Name E-mail Department
Veit Schwämmle veits@bmb.sdu.dk

Timetable

Administrative Unit

Biokemi og Molekylær Biologi

Team at Registration & Legality

NAT

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