Big Data in Biotechnology

Course content

Many experimental procedures such as the various “-omics” techniques routinely employed within biotechnology produce vast amounts of data. Therefore, the amount of available data in many biotechnological disciplines is steadily increasing. While most biotechnological data is not necessarily of a size and complexity defined as big data, fundamental knowledge and skills of large-scale computing systems and analysis methods is required to make use of this wealth of information. The purpose of this course is to introduce the theory and practice of large-scale data analysis to students, which will allow them to perform and assess different types of ”-omics”-scale data procedures. Tentative list of data types to be covered in the course: Transcriptomic data (RNAseq), Metabolomic data (LC-MS), and Biological image data.

This course covers the fundamental challenges with analysis of large amounts of data, i.e. how to handle large data files and how to overcome computational/storage limitations. The course provides knowledge and skills to perform data wrangling and normalization. The students will obtain working knowledge of basic data handling, data analysis, and data visualization. Through in-depth focus on the handling and analysis of a relevant set of different data-types using programming-based analysis techniques, this course will address statistical and computational challenges of large-scale data analysis.

Basic knowledge of the experimental methods used to generate the data types used in the course will also be briefly covered, because an understanding of the experimental methods used to generate data is often needed to assess bias and confounding factors in data.

Education

MSc Programme in Biotechnology
MSc Programme in Biotechnology with a minor subject
MSc Programme in Environmental Science

Learning outcome

At course completion, the student will have:

Knowledge of

  • The general principles of large-scale data analysis
  • Common pitfalls in large-scale data analysis
  • The basic concepts underlying clustering and visualization techniques

 

Skills in

  • How to efficiently keep, move, and analyse large amounts of data
  • How to structure and perform large-scale data analyses in a coding-based software environment, such as for example R or Python
  • Handling and modifying large datasets
  • Visualization and dissemination of data

 

Competences in

  • Analysing different types of large-scale biotechnology data
  • Critically evaluating the quality of different types of biotechnology data
  • Assessing and understanding results of large-scale data analyses

Lectures and computer exercises

Original literature, software manuals and tutorials, and teacher provided compendia.

Participants should have basic knowledge of a programming-based scientific data software such as R or Python, at a level similar to students who have completed Mathematics and Data handling (MatDat) (LMAB10066U). Students lacking the required skills must expect to spend extra time familiarizing themselves with programming-based scientific data software such as R or Python.

Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)

Continuous feedback from teachers at computer exercises and discussion workshops. Feedback from teachers and peers (class) on oral presentations and answers to seminar questions.

ECTS
7,5 ECTS
Type of assessment
Written examination, 4 hours under invigilation
The course has been selected for ITX exam on Peter Bangs Vej.
Aid
Without aids
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

See learning outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 35
  • Preparation
  • 107
  • Theory exercises
  • 10
  • Practical exercises
  • 50
  • Exam
  • 4
  • English
  • 206