SylabUZ
| Course name | Fundamentals of data analysis |
| Course ID | 11.2-WE-BizElP-PodAnalDanych-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | E-business |
| Education profile | practical |
| Level of studies | First-cycle Erasmus programme |
| Beginning semester | winter term 2022/2023 |
| Semester | 1 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
|
| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Exam |
| Class | 30 | 2 | - | - | Credit with grade |
Provide basic knowledge of qualitative and quantitative data analysis.
Form a critical view on the credibility of statistical analysis in engineering.
Give basic skills of uncertainty estimation in practical experimental studies in engineering.
Engineering mathematics.
Measurement uncertainty. Propagation of uncertainty. Random and systematic errors. Statistical sampling study. Frequency distribution. Histogram. Summary statistical measures of location, variability, asymmetry and concentration. Rejection of outliers.
Probability. Sample space. Basic definitions of probability: classical, frequency and modern. Fundamental properties of probability. Conditional probability. Independence. Total probability theorem. Bayes’ Theorem.
Discrete and continuous random variables. Discrete random variables. Distributions: binomial, Bernoulli, Poisson and geometric. Functions of random variables. Expected value and variance. Joint probabilisty distributions of many random variables. Independence of random variables. Continuous random variables. Uniform distribution. Exponential distribution. Cumulative distribution function of a random variable. Normal distribution.
Fundamentals of statistical inference. Types of random samples. Simple random sample. Distributions: chi-square, t-Student and Fisher-Snedecor. Point and interval estimation. Unbiasedness, consistency, efficiency and sufficiency. Parameter and non-parameter estimation. Confidence intervals for the mean. Limit theorems. Interval estimates of the proportion, variance, standard deviation, differences between proprtions and means. Determining the required sample size.
Hypothesis testing. One- and two-sided tests of the mean. Testing the proportion. Testing the variance. Selecting the test procedure.
Lecture, exercise classes.
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture – the passing condition is to obtain a positive marks from a written or oral exam.
Exercice classes – the passing condition is to obtain positive marks from all exercises and tests conducted during the semester.
Calculation of the final grade: lecture 50% + exercice classes 50%
Modified by dr hab. inż. Marek Kowal, prof. UZ (last modification: 06-04-2022 09:00)
