Statistical Data Analysis

Course Description

Statistics plays a vital role in every human activity, while the ability to use statistical inferential methods and interpret the results are essential in engineering and science. This course gives a comprehensive introduction to the methods and practices of computer-based statistical data analysis. The course covers and intertwines four integral aspects of statistical analysis: data, statistical methods, mathematical foundations, and interpretation of results. The first part of the course gives an overview of statistical methods, approaches to data description, and data visualization and exploration methods. The second part is devoted to the foundations of statistical inference and covers the selection, application, and adequacy of parametric statistical tests for numeric and categorical data. The third part considers more advanced topics, such as non-parametric statistics, analysis of variance, and correlation analysis. All concepts are illustrated with examples and problem sets on real data in programming language R.

Forms of Teaching



Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 50 % 40 % 50 % 40 %
Mid Term Exam: Written 0 % 30 % 0 %
Final Exam: Written 0 % 30 %
Exam: Written 50 % 60 %

Week by Week Schedule

  1. Introductory lecture
  2. Descriptive statistics
  3. Statistical inference
  4. Hypothesis testing
  5. Statistical inference for metric data
  6. Statistical inference for categorical data
  7. Nonparametric methods
  8. Midterm exam
  9. Linear regression
  10. Linear regression
  11. Logistic regression
  12. Analysis of variance (ANOVA)
  13. Resampling methods
  14. Alternative approach to data analysis
  15. Final exam

Study Programmes

University undergraduate
Computing (study)
Free Elective Courses (5. semester)
Electrical Engineering and Information Technology (study)
Free Elective Courses (5. semester)
University graduate
Audio Technologies and Electroacoustics (profile)
Elective Courses (1. semester)
Communication and Space Technologies (profile)
Elective Courses (1. semester)
Computational Modelling in Engineering (profile)
Elective Courses (1. semester)
Computer Engineering (profile)
Elective Course of the Profile (1. semester) Elective Courses (1. semester)
Computer Science (profile)
Elective Courses (1. semester)
Control Systems and Robotics (profile)
Elective Courses (1. semester)
Data Science (profile)
Core-elective courses (1. semester)
Electrical Power Engineering (profile)
Elective Courses (1. semester)
Electric Machines, Drives and Automation (profile)
Elective Courses (1. semester)
Electronic and Computer Engineering (profile)
Elective Courses (1. semester)
Electronics (profile)
Elective Courses (1. semester)
Information and Communication Engineering (profile)
Elective Courses (1. semester) Elective Courses of the Profile (1. semester)
Network Science (profile)
Elective Courses (1. semester)
Software Engineering and Information Systems (profile)
Core-elective courses 1 (1. semester)


Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye (2016.), Probability and Statistics for Engineers and Scientists,
David Diez, Christopher Barr, Mine Çetinkaya-Rundel (2015.), OpenIntro Statistics,
Mirta Benšić, Nenad Šuvak (2013.), Primijenjena statistika,

Associate Lecturers

Laboratory exercises

For students


ID 229840
  Winter semester
L1 English Level
L1 e-Learning
45 Lectures
15 Laboratory exercises

Grading System

89 Excellent
76 Very Good
63 Good
50 Acceptable