1. komponenta

Lecture type	Total
Lectures	45

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE AIMS AND OBJECTIVES:
1. To familiarize students with the basic methods of data analysis and visualization
2. Explain the primary methods of collecting, cleaning and storing various types of data
3. To gather practical experience in the programming language Python and SQL databases
4. Provide an overview of basic statistical methods suitable for data processing
5. Provide insight into the basic methods of machine learning and their application in data analysis
6. Provide practical experience in the analysis of real data sets

COURSE DESCRIPTION AND SYLLABUS:
1. Introduction to the Python programming language: lists, records (tuples), dictionaries, functions, classes, files, numpy library (2 weeks)
2. Data storage and access: relational model and SQL language, unstructured data (JSON, text, Web) (2 weeks)
3. Basic concepts of statistics and probability: correlation, random variables, hypothesis testing, p-values, confidence intervals (2 weeks)
4. Data analysis in Python: Pandas library, dataframes, data cleaning, data wrangling, aggregation (2 weeks)
5. Data visualization: matplotlib and various data visualization methods (2 weeks)
6. Machine learning and data analysis: scikit-learn library, classification algorithms, naive bayes

1. Data wrangling with Pandas, NumPy, and IPython. 3rd edition, McKinney, Wes, O'Reilly Media, Inc.,, 2022.
2. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, Géron, Aurélien, O'Reilly Media, Inc., 2022.
3. An introduction to statistical learning. Vol. 112, James, Gareth, New York: Springer, 2013.

Enrollment :
Passed : Computer programming 1
Passed : Mathematical analysis 1

Izborni modul Računarstvo - Regular study - Mathematics

Izborni modul Računarstvo - Regular study - Mathematics

Izborni predmet 1, 2 - Regular study - Mathematics

Izborni predmet 1, 2 - Regular study - Mathematics