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Syllabus for

Academic year
TMS101 - Basics in mathematical statistics and computer science
Owner: BIMAS
7,0 Credits (ECTS 10,5)
Grading: TH - Five, Four, Three, Not passed
Level: A

Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0103 Examination, part A 3,5 c Grading: TH   3,5 c   18 Oct 2004 pm V,  08 Jan 2005 am V
0203 Examination, part B 3,5 c Grading: TH   3,5 c   21 Oct 2004 am V   15 Jan 2005 am V

In programs



Professor  Serik Sagitov


TMS100   Basics in mathematical statistics and computer science


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.


Part 1: 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 lists, trees and graphs, 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 and minimal spanning tree.

Part 2: 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.

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 1: Weiss: Data structures and algorithm analysis in Java.
Part 2: Rice, J.A. Mathematical Statistics and Data Analysis. International Thomson Publishing, 1995.


Part 1 and 2: Exercises and a written exam.

Page manager Published: Mon 28 Nov 2016.