| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Science | ||
| DATA SCIENCE | ||
Course unit
BUSINESS ECONOMIC AND FINANCIAL DATA
SCP7079231, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2018/19
BUSINESS ECONOMIC AND FINANCIAL DATA
SCP7079231, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2018/19
Information on the course unit
| Degree course |
Second cycle degree in DATA SCIENCE SC2377, Degree course structure A.Y. 2017/18, A.Y. 2019/20 |
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|---|---|---|
| Number of ECTS credits allocated | 6.0 | |
| Type of assessment | Mark | |
| Course unit English denomination | BUSINESS ECONOMIC AND FINANCIAL DATA | |
| Website of the academic structure | http://datascience.scienze.unipd.it/2019/laurea_magistrale | |
| Department of reference | Department of Mathematics | |
| Mandatory attendance | No | |
| Language of instruction | English | |
| Branch | PADOVA | |
| Single Course unit | The Course unit can be attended under the option Single Course unit attendance | |
| Optional Course unit | The Course unit can be chosen as Optional Course unit |
Lecturers
| Teacher in charge | MAURO BERNARDI | SECS-S/03 | |
|---|---|---|---|
| Other lecturers | OMAR PACCAGNELLA | SECS-S/03 |
ECTS: details
| Type | Scientific-Disciplinary Sector | Credits allocated | |
|---|---|---|---|
| Educational activities in elective or integrative disciplines | SECS-S/03 | Statistics for Economics | 6.0 |
Course unit organization
| Period | First semester |
|---|---|
| Year | 2nd Year |
| Teaching method | frontal |
| Type of hours | Credits | Teaching hours |
Hours of Individual study |
Shifts |
|---|---|---|---|---|
| Lecture | 6.0 | 48 | 102.0 | No turn |
| Start of activities | 30/09/2019 |
|---|---|
| End of activities | 18/01/2020 |
| Show course schedule | 2019/20 Reg.2017 course timetable |
| Prerequisites: | Basic statistics: descriptive statistics and probability. Inferential statistics: estimation, confidence intervals and hypothesis testing. |
| Target skills and knowledge: | This course aims at introducing the students to the main statistical features and concepts underlying the analysis of data collected over time, as well as providing the basic statistical solutions to analyse such data in economic, financial and business settings. |
| Examination methods: | Homework and Final Presentation. |
| Assessment criteria: | Students will be evaluated according to their level of knowledge of some tools and techniques to analyse economic, financial or business data and their ability to apply them to real cases. |
| Course unit contents: | Decomposing and analysing economic time series: latent component approaches and ARMA modelling.
Enhancing the analysis of economic and financial time series data: some case studies. Business and marketing data analyses: the joint use of cross-sectional and temporal dimension and the introduction of dynamic modelling. |
| Planned learning activities and teaching methods: | Lectures and Laboratories. Working in groups. |
| Additional notes about suggested reading: | Notes prepared by the teaching staff will be uploaded throughout the course. |
| Textbooks (and optional supplementary readings) |
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