SylabUZ
| Course name | BUSINESS DATA ANALYSIS |
| Course ID | 04.0-WZ-P-BDA-S18 |
| Faculty | Faculty of Economics and Management |
| Field of study | WEiZ - oferta ERASMUS |
| Education profile | - |
| Level of studies | Erasmus programme |
| Beginning semester | winter term 2023/2024 |
| Semester | 1 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| 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 |
| Laboratory | 30 | 2 | - | - | Credit with grade |
Student is familiar with some methods and tools for data analysis.
None.
Some issues concerning data collection, data cleaning, data visualization, regression analysis, and business intelligence. The impact of data quality on the obtained results. Data analysis methods for multidimensional enterprise databases related to, for example, a customer relationship management system or enterprise resource planning system. Presentation of dedicated software for data analysis in business areas such as sales, purchasing, materials management, etc. Multidimensional data analysis with the use of tools based on OLAP technology.
Software presentation in the computer lab, project method.
| Outcome description | Outcome symbols | Methods of verification | The class form |
Credit of laboratory refers to the solution of 5 analyses in different business areas with the use of dedicated software for data analysis, including the selection of useful data, suitable data analysis methods, and interpretation of results. Student can get maximal 1 point for each analysis. Moreover, student should prepare written work with the presentation of data analysis methods that are suitable for a given data set, data acquisition related to the selected business processes, and data quality in the context of enterprise databases. Student can get maximal 5 points for the above-mentioned written work. Consequently, student can obtain maximal 10 points related to solved exercises and written work. The criteria for a grade: 0-5.0 points „2.0”, 5.1-6.0 points „3.0”, 6.1-7.0 points „3.5”, 7.1-8.0 points „4.0”, 8.1-9.0 points „4.5”, 9.1-10.0 points „5.0”.
1. Grbich, C., Qualitative data analysis: An introduction. Sage, 2012.
2. Hanke J.E., Wichern D.W., Business Forecasting (9th Ed). Prentice Hall, 2008.
3. Hardy M.A., Bryman A., Handbook of Data Analysis. Sage, 2004.
4. Reichmann T., Controlling: concepts of management control, controllership, and ratios. Springer, 2012.
1. Carlberg C., Predictive Analytics. Que Publishing, 2012.
2. Dyche, Jill. The CRM handbook: A business guide to customer relationship management. Addison-Wesley Professional, 2002.
Lecturer: m.relich@wez.uz.zgora.pl
Modified by dr inż. Anna Łobos (last modification: 31-05-2023 19:59)
