(in Polish) Advanced statistics
General data
Course ID:  2401CS11ASs2uz. 
Erasmus code / ISCED: 
(unknown)
/
(0542) Statistics

Course title:  (unknown) 
Name in Polish:  Advanced statistics 
Organizational unit:  Faculty of Philosophy and Social Sciences 
Course groups:  
ECTS credit allocation (and other scores): 
2.00 (differs over time)

Language:  Polish 
Prerequisites:  Basics in quantitative data analysis. Knowledge of basic statistical terms. 
Type of course:  (in Polish) przedmiot obowiązkowy 
Total student workload:  Contact hours with teacher:  participation in lectures  15 hrs  consultations 15 hrs Selfstudy hours:  preparation for lectures  15 hrs  preparation for examination 15 hrs Altogether: 60 hrs (2 ECTS) 
Learning outcomes  knowledge:  Student W1: is familiar with different data segmentation and visualization methods – K_W08 W2: thorough knowledge of quantitative data analysis – in such fields as modeling, data reduction, data visualization – K_W02, K_W05, K_W08 W3: is well acquainted with the concept of supervised and unsupervised learning approaches used in statistics – K_W07, K_W08 W4: knows how to perform advanced analysis of relationships between studied variables using partial least squares regression and correlation method – K_W06, K_W08 
Learning outcomes  skills:  Student U1: is able to design an advanced statistical data analysis pipeline in specialized software (R / SPSS) – K_U01, K_U06, K_U12 U2: can preprocess, classify, and visualize data using supervised and unsupervised algorithm (kmeans, hierarchical cluster analysis, decision trees)  K_U01 , K_U09, K_U12 U3: can conduct an analysis of relationship between variables using multivariate correlational and regression approaches (e.g., partial least square correlation / regression) – K_U04, K_U07, K_U12 U4: can perform modeling using SPSS / R software  K_U02, K_U03, K_U12 
Learning outcomes  social competencies:  Student K1: understands the need for appropriate data preprocessing and visualization – K_K01 K2: is well prepared to critically analyze data and draw wellinformed conclusions – K_K02 K3: is aware of power and limitations of statistical tools – K_K02 K4: while understanding own limitations and strong points, is able to add value to team works in advanced analytical projects  K_K05 
Teaching methods:  Expository teaching methods:  informative (conventional) lecture 
Expository teaching methods:   informative (conventional) lecture 
Short description: 
The course will get students acquainted with selected advanced concepts and methods in statistical data analysis. Students will solidify their knowledge about supervised and unsupervised approaches in data segmentation, visualization, and prediction. Students will learn one of the sophisticated methods used in correlational analysis and prediction. 
Full description: 
Advanced statistics is a course that delves deeper into the field of statistics. It is designed to provide students with a comprehensive understanding of some of more advanced analysis methods commonly used in neuroscience. First, students will be presented with theoretical and practical knowledge on how to use supervised and unsupervised methods used in data preprocessing, visualization, classification, and prediction (e.g., kmeans, hierarchical cluster analysis, decision trees). Then, the course, building on basics in probability theory, statistical inference, and modeling, will introduce advanced approaches for data modeling, such as multivariate partial least squares regression and correlation techniques (PLSR/PLSC). Students will be prepared to use appropriate tools offered by statistical software packages such as R, SPSS, and other accessible tools for data manipulation in order to design a desired analysis pipeline. Throughout the course, students are expected to develop a deep understanding of how to interpret statistical results, critically evaluate research findings, and effectively communicate statistical information to others. List of topics: 1. Unsupervised data reduction, segmentation, and visualization: – kmeans,  hierarchical cluster analysis 2. Supervised advanced data predictive models  decision trees 3. Multivariate correlation / regression models – partial least square correlation  partial least square regression 4. Writing scripts for advanced data statistical analysis using R / SPSS software 5. Statistical inference using learned methods and tools. 
Bibliography: 
• A. Field, J. Miles, and Z. Field, Discovering Statistics Using R, 1st edition. London ; Thousand Oaks, Calif: SAGE Publications Ltd, 2012. • A. Field, An Adventure in Statistics: The Reality Enigma, 1st edition. Los Angeles: SAGE Publications Ltd, 2016. • A. Krishnan, L. J. Williams, A. R. McIntosh, and H. Abdi, “Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review,” NeuroImage, vol. 56, no. 2, pp. 455–475, May 2011, doi: 10.1016/j.neuroimage.2010.07.034. • Bahnsen A.C., Aouada D., i Ottersten B., Exampledependent costsensitive decision trees, „Expert Systems with Applications”, 2015, t.42, nr 19, s. 6609–6619. • Kim J.K., Song H.S., Kim T.S., i Kim H.K., Detecting the change of customer behavior based on decision tree analysis, „Expert Systems”, 2005, t.22, nr 4, s. 193–205. • D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining: 4. Hoboken: Wiley, 2014. • M. KraskaMiller, Nonparametric Statistics for Social and Behavioral Sciences. Boca Raton, 2013. • Peng R.D., R Programming for Data Science, 2020. • R. S. Witte and J. S. Witte, Statistics, 11th Edition. Wiley, 2017. 
Assessment methods and assessment criteria: 
Assessment methods:  lecture: oral examination Assessment criteria: lecture: oral exam, with three openended questions regarding data visualization, modeling, and inference, respectively. Grading: satisfactory – 1 correct answers good – 2 correct answers very good – 3 correct answers 
Classes in period "Summer semester 2022/23" (past)
Time span:  20230220  20230930 
Navigate to timetable
MO TU W TH FR 
Type of class: 
Lecture, 15 hours


Coordinators:  Michał Komorowski  
Group instructors:  Michał Komorowski  
Students list:  (inaccessible to you)  
Examination: 
Course 
Examination
Lecture  Examination 
Copyright by Nicolaus Copernicus University in Torun.