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Syllabus for

Academic year
TMS100 - Basics in mathematical statistics and computer science
Owner: BIMAS
7,0 Credits (ECTS 10,5)
Grading: TH - Five, Four, Three, Not passed
Level: A
Department: 0700 - Matematisk statistik MV CTH/GU

Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0100 Laboratory 1,0 c Grading: UG   1,0 c    
0200 Examination 3,0 c Grading: TH   3,0 c    
0300 Examination 3,0 c Grading: TH   3,0 c    

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For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

Introductory programming course.


The aim of this course is to provide some basic ideas, tools and techniques in mathematics, statistics and computer science. The course is intended for students with limited previous experiences of these areas, and it is specially designed to give the necessary mathematical prerequisites to follow later courses in the Master's programme in Bioinformatics.


The course consists of three parts: computer introduction (1cu), data structures and algorithms (3 cu), and mathematical statistics (3 cu).

Part 1: Computer introduction.
This part gives an introduction to the computer system at Mathematics Centre, and some of the commonly used software. It gives an introduction to the operating system Unix, and to software such as Emacs, Pine, Netscape.

Part 2: Data structures and algorithms.
This part of the course mainly considers data structures and algorithms, which in addition to basic programming are necessary for understanding algorithms and successfully implementing programs in any area of application. In addition to basic knowledge, the course emphasises the skills needed to independently analyse and solve algorithmic problems. Content:
Data structures and abstract data types. Common data structures such as trees, hash tables, graphs, heaps, relations, sets etc., both with respect to their abstract properties and their implementation. Common algorithms related to these data structures and basic analysis of their time and memory requirements. Algorithms for basic problems such as sorting, shortest path, minimal spanning tree, enumerative search. General algorithm design principles making it possible to design algorithms for new problems.

Part 3: Mathematical statistics.
Experimental research in the sciences and in engineering involves the use of experimental data, a sample, from which to draw conclusions about the nature of a phenomenon under study. However, inference based on sampled data will always be subject to uncertainty; the information provided by one sample depends on the particular sample chosen and will thus change from sample to sample. Statistics, sometimes called the science of data, includes methods to evaluate the reliability of conclusions based on data. The theory of statistics uses probability to measure the uncertainty associated with an inference. The goal of this part of the course is to present the mathematical tools and concepts necessary for incorporating the chance element in modelling and analysis of data, and to give insight into the various uses of statistical methods.

Content: Combinatorics. Probability. Conditional probability and independence. Random variables and some common probability distributions. Expectation. The central limit theorem. Maximum likelihood estimation. Hypothesis testing.


The course is organised with lectures and practically oriented homework and programming exercises.


Preliminary literature:
Part 2: Weiss: Data structures and algorithm analysis.
Part 3: Rice, J.A. Mathematical Statistics and Data Analysis. International Thomson Publishing, 1995.


Part 1: Exercises . Part 2 and 3: Exercises and a written exam.

Page manager Published: Thu 03 Nov 2022.