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Objectives and competences

The objective of this course is that the student learns statistical methods and how to use them in data science.

Content (Syllabus outline)

• Introduction to data (Research study design, hypothesis, research questions, types of variables, exploratory data analysis, statistical inference) • Probability and distributions (Defining probability, Conditional probability, Normal distribution, Binomial distribution) • Foundations for inference (Variability in estimates and the Central Limit Theorem, Confidence intervals, Hypothesis tests, Decision errors, significance, and confidence) • Inference for numerical variables (t-inference, Power, Comparing three or more means (ANOVA), Repeated measure (MANOVA)) • Inference for categorical variables (Comparing two proportions, Comparing three or more proportions (Chi-square)) • Introduction to linear regression (Relationship between two numerical variables, Linear regression with a single predictor, Outliers in linear regression, Inference for linear regression) • Multiple linear regression (Regression with multiple predictors, Inference for multiple linear regression, Model selection and diagnostic) • Bayesian and frequentist inference (Bayes probability, influence of prior beliefs, sequential statistics)

Learning and teaching methods

• predavanja, • računalniške vaje.

Intended learning outcomes - knowledge and understanding

Knowledge and understanding: • understand the principles of statistical analysis and its use in data science • select appropriate statistical methods and tools and employ them for both data analysis and validation of data science analysis outputs Transferable/Key skills and other attributes: • Communication skills: effectively present the results of statistical analysis and their interpretation to target groups • Use of programming tools: use of statistical software and languages. • Problem-solving in data science: design a research study, set and prove the hypothesis, and set and answer a research question.

Readings

• Peter Bruce, Andrew Bruce, Practical Statistics for Data Scientists: 50 Essential Concepts. O'Reilly Media, May 2017 • Bradley Efron and Trevor Hastie, Computer Age Statistical Inference: Algorithms, Evidence, and Data Science, Institute of Mathematical Statistics Monographs, Jul 2016 • Douglas A. Wolfe and Grant Schneide, Intuitive Introductory Statistics, Springer, Oct 2017 • Tina Štemberger, Univariatne in bivariatne statistične metode v edukaciji, Univerza na Primorskem, 2016

Prerequisits

No prerequisites.

  • red. prof. dr. PETER KOKOL, univ. dipl. inž. el.

  • Written examination: 50
  • Computer skills: 50

  • : 15
  • : 15
  • : 60

  • Slovenian
  • Slovenian