MV013 Statistics for Computer Science

Faculty of Informatics
Spring 2025
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
In-person direct teaching
Teacher(s)
prof. Mgr. Petr Hasil, Ph.D. (lecturer)
RNDr. Radim Navrátil, Ph.D. (lecturer)
Mgr. Markéta Makarová (seminar tutor)
Mgr. Pavel Morcinek (seminar tutor)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
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
Prerequisites
Mathematical analysis: functions, limits of sequences and functions, derivatives and integrals of real and multidimensional functions.
Linear algebra: matrices and determinants, eigenvalues and eigenvectors.
Probability theory: probability, random variables and vectors, their distributions and properties, limit theorems.
Statistics: Descriptive statistics, introduction to hypotheses testing, confidence intervals, linear regression model
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The aim of the course is to review and deepen fundamental knowledge of statistics so that students, upon completing the course, are able to process real-world data correctly and draw accurate results and conclusions from them. We will focus on advanced statistical methods that can be applied in situations where standard assumptions about data, such as normality or homoscedasticity, are not met.
Learning outcomes
Student will:
learn to select and apply appropriate statistical models for different types of data, enhancing their ability to analyze and solve real-world problems across various fields;
develop critical thinking skills to evaluate and interpret statistical models, ensuring they can make data-driven decisions and draw valid conclusions;
understand the assumptions underlying statistical models and how to address issues such as model mis-specification, allowing them to handle non-ideal data conditions effectively;
be able to communicate statistical findings clearly through reports, visualizations, and presentations, making complex statistical concepts accessible to non-experts;
enhance their skills in statistical software R, enabling them to efficiently manage and analyze datasets using modern computational tools.
Syllabus
  • Why do we need statistics?
  • Meeting the data
  • Nonparametric and parametric statistical models
  • From point estimates to confidence intervals
  • Alternatives to one sample t-test - parametric and nonparametric tests, bootstrapping
  • Normality of data and how to identify it
  • From one sample to two and even more samples
  • Linear regression models from statistical point of view
  • Finding connections - testing for independence
Literature
  • WASSERMAN, Larry. All of statistics : a concise course in statistical inference. New York: Springer, 2004, xix, 442. ISBN 9780387402727. info
  • CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info
Teaching methods
Lectures, practical exercise classes in the statistical software R.
Assessment methods
Homework, ROPOT 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 (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
Teacher's information
Capacity of the course is limited. Registration is required.
The course is also listed under the following terms Autumn 2015, Autumn 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.
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