M1031 Introduction to Mathematics and Statistics

Faculty of Science
Autumn 2024

The course is not taught in Autumn 2024

Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
RNDr. Radim Navrátil, Ph.D. (lecturer)
Guaranteed by
prof. Mgr. Petr Hasil, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
To provide students with a solid foundation in mathematics and statistics, with an emphasis on applications and data analysis relevant to biochemistry and molecular biology. The course focuses on the practical use of mathematical and statistical methods in biological research and experimental work.
Learning outcomes
Students will be able:
to apply methods of mathematical analysis in practice;
to select suitable probabilistic and statistical model for real data;
to compute basic characteristics of the data;
vizualize the data;
to build up and explain suitable statistical tests and apply statistical inference on real data.
Syllabus
  • Foundations of Mathematics for Scientific Applications - functions, derivatives, integrals and their applications in biology and biochemistry
  • Introduction to Statistics and Probability - definition of probability and random variables, commonly used discrete and continuous probability distributions
  • Data Processing and Preparation for Analysis - basic descriptive statistics for numerical and categorical data, data visualization
  • Probability Distributions and Their Applications - parameter estimates, confidence intervals and hypotheses testing
Literature
  • SCHWABISH, Jonathan A. Better data visualization : a guide for scholars, researchers, and wonks. New York: Columbia University Press, 2021, xi, 449. ISBN 9780231193108. info
  • WASSERMAN, Larry. All of statistics : a concise course in statistical inference. New York: Springer, 2010, xi, 442. ISBN 9780387217369. info
  • CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info
Teaching methods
Lectures will be accompanied by practical exercise classes utilizing computers.
Assessment methods
Homeworks and tests during the semester (40 points), final written exam (60 points). At least 50 % of averall points is needed to pass.
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
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