Syllabus for Introduction to Programming, Scientific Computing and Statistics

Introduktion till programmering, beräkningsvetenskap och statistik

  • 10 credits
  • Course code: 1TD349
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: Computer Science G2F

    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: 2018-03-06
  • Established by:
  • Revised: 2019-02-08
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 30, 2019
  • 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 hp in molecular biology, cell biology, evolution and/or genetics; and 15 credits in mathematics/statistics.
  • Responsible department: Department of Information Technology

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;
  • analyze 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(4 credits): Matrices, vectors and solution to linear equation systems numerical solution to integrals, 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 (2 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).

Reading list

The reading list is missing. For further information, please contact the responsible department.