Introduction to Programming, Scientific Computing and Statistics

10 credits

Syllabus, Bachelor's level, 1TD349

Code
1TD349
Education cycle
First cycle
Main field(s) of study and in-depth level
Computer Science G2F
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 28 January 2021
Responsible department
Department of Information Technology

Entry requirements

A Bachelor's degree, equivalent to a Swedish Kandidatexamen, from an internationally recognised university. Also required is 45 credits in biology with 30 credits in molecular biology, cell biology, evolution and/or genetics; and 15 credits in mathematics/statistics.

Learning outcomes

On completion of the course, the student should be able to:

  • describe the key concepts covered in scientific computing and statistics, and perform tasks that require knowledge of these concepts;
  • describe and apply algorithms and methods covered in the course;
  • analyse properties of the computational algorithms, mathematical and statistical models, by using the analytical tools presented in the course;
  • apply basic experimental design methods;
  • solve computational problems in a structured way (by breaking down the problem into sub-problems) and implement in Matlab;
  • use basic Linux and shellscript.

Content

The course has three parts: scientific computing and basig programming, statistics and multivariate data analysis, and introduction to Linux .

Part 1(5 credits): Matrices, vectors and solution to linear equation systems, introduction to ordinary differential equations (ODE), numerical solution to ODE:s , introduction to Monte Carlo methods. Matlab and fundamentals in programming, e.g. control stuctures (if, for, while) and functions.

Part 2 (4 credits): Statistics and multivariate data analysis: fundamentals i statistics (distributions, expected value, varians, standard deviation etc.). Basics in univariate analysis (t-test, anova, correlation and regression). Principal component analysis. Predictive multivariate data analysis.

Part 3 (1 credits): Linux through bash (e.g. pipelines and commands like grep, awk and forth) and Shellscript.

Instruction

Lectures, problem solving classes, computer lab, assignments.

Om särskilda skäl finns får examinator göra undantag från det angivna examinationssättet och medge att en enskild student examineras på annat sätt. Särskilda skäl kan t ex vara besked om särskilt pedagogiskt stöd från universitetets samordnare för studenter med funktionsnedsättning.

Assessment

Written exam (part 1 and 2). Assignments (part 1, part 2 and part 3).

No reading list found.

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