Data analytics tools and technologies (CS)
Type: For the student's choice
Department: system design
Curriculum
Semester | Credits | Reporting |
9 | 7 | Setoff |
Lectures
Semester | Amount of hours | Lecturer | Group(s) |
9 | 32 | Professor Ohirko I. V. | ФеІм-12 |
Laboratory works
Semester | Amount of hours | Group | Teacher(s) |
9 | 48 | ФеІм-12 | Professor Ohirko I. V. |
Опис навчальної дисципліни
The course is designed to provide participants with the necessary knowledge and practical skills for analytical work with real data of social, economic nature, etc. Typical problems of linear and nonlinear data description methods, forecasting stationary and non-stationary time series, and extracting typical significant components from the latter will be considered in a clear combination with real data analytics tasks and typical examples.
The purpose and objectives of teaching the discipline is to form a system of fundamental knowledge on the analysis of data of statistical indicators of social, economic and behavioral processes, as well as approaches to forecasting time series of the above processes.
Recommended Literature
- 4. Gartner Identifies the Top 10 Data and Analytics Trends. URL: https://www.gartner.com/en/newsroom/press-releases/2023-05-09-gartneridentifies-the-top-ten-data-and-analytics-trends-for-20230
- 5. Walter Enders. Applied Econometric Time Series. Wiley, 2-d edition, 2004. – 460 p.
- 6. Diggle P.J. Time Series: A Biostatistical Introduction. – Oxford, 1990.
- 7. Mingda Zhang Time Series: Autoregressive models AR, MA, ARMA, ARIMA http://people.cs.pitt.edu/~milos/courses/cs3750/lectures/class16.pdf
- 8. Ibid: Markov models: http://people.cs.pitt.edu/~milos/courses/cs3750/lectures/class15.pdf