| Statuss(Aktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Course title | Financial Data Analysis and Financial Solutions |
| Course code | EkonM014 |
| Credit points (ECTS) | 4 |
| Total Hours in Course | 108 |
| Number of hours for lectures | 8 |
| Number of hours for seminars and practical classes | 24 |
| Number of hours for laboratory classes | 0 |
| Independent study hours | 76 |
| Date of course confirmation | 23/10/2024 |
| Responsible Unit | Institute of Economics and Finance |
| Course developers | |
| Ph.D., pētn. Aija Pilvere |
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| There is no prerequisite knowledge required for this course | |
| Course abstract | |
| The study course provides in-depth knowledge of current issues and financial solutions of financial data analysis, emphasizing future needs and related challenges. The content of the course is designed so that the student acquires both theoretical knowledge and practical skills about the latest trends/ tools/ concepts/ principles/ techniques/ practices in financial data analysis and financial solutions. In this study course, students learn to perform financial data analysis, modelling and forecasting many different possible scenarios, which respectively depend on the obtained information, the interpretation of financial analysis after the obtained information for the development of financial solutions and decision-making.
The aim of the course is to provide the student with in-depth theoretical knowledge and practical skills in working with financial analysis data (their acquisition, processing, visualization and analysis), which are necessary for specialists of different ranges for successful and effective financial solutions, decisions. |
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| Learning outcomes and their assessment | |
| Knowledge:
• In-depth knowledge of the nature of financial data analysis and the need for financial solutions and decision-making. Calculations and practical work during lessons, test • Specialized knowledge of working with large volumes of information and data. Calculations and practical work during classes, exam. • Expanded knowledge and understanding of the application of various methods and tools, performing the necessary calculations, analyzing and communicating the obtained results, as well as evaluating the most suitable financial solutions. Calculations and practical work during classes, exam. Professional skills: • Able to apply various financial analysis solutions. Calculations and practical work during classes, exam. • Understands and is able to evaluate how to use methods based on artificial intelligence. Practical works during the lessons. • Able to work independently with financial data and analyze it. Exam. General skills: • Able to think critically, analyze and make decisions. Discussions, work in groups and individually in classes. • Can discuss and defend his ideas. Discussions in classes, exam. • Able to take responsibility for his work and decisions. Exam. Competence: • Able to work with financial analysis data (knows their acquisition, processing, visualization and analysis), which are necessary for financial specialists in making successful and effective financial solutions and decisions. Calculations and practical work during classes, test and exam. • Able to critically analyze and make effective decisions in the change process and justify the decision. Calculations and practical work during classes, exam. |
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| Course Content(Calendar) | |
| Lectures (8h)
1. Introduction: Financial data and its acquisition, analysis, management, importance in the financial sector. (0.5h) 2. Data science: Data science model (problem understanding, data, visualizations, hypothesis(es), analysis, communication of results), importance and advantages of data science in financial data analysis, solutions, decisions. (1.5h) 3. Data: Data analysis, linking of data and correspondingly obtained results, data quality and its evaluation, data decision tables, solutions for missing data management. (1h) 4. Visualization: Visualization in the process of data science, diagrams and graphs, defining questions using visualizations, design – as a tool for data visualization and communication. (1h) 5. Time series and forecasting: "Connecting yesterday's data with tomorrow's forecasts", data models and their evaluation, time series and identified problem(s) and evaluation of the most suitable techniques for time series forecasting ("time series forecasting"). (0.5h) 6. Artificial Intelligence ("AI"), Machine Learning ("Machine Learning"), Power Bi, Intelligent Automation ("Intelligent Automation") and Sustainable Automation ("Sustainable Automation"): understanding artificial intelligence, machine learning, autonomous agents, real-time intelligent systems, automation, human-artificial intelligence interaction. (3h) 7. Financial data analysis and future solutions: perspectives and challenges. (0.5h) Practical works (24h) 1. Introduction/ Data science/ Data: (4h) Discussions during lectures about topics supported by practical examples, scientific researches, current events in this field (1h). During the lecture, practical work (in groups) in form of the case study created for this study course based on a practical situation/experience (1h). Practical work (individually) about data science in the context of financial analysis (2h). 2. Visualization: (1h) During the lecture, practical work (in groups) in form of the case study created for this study course based on a practical situation/experience. 3. Artificial Intelligence ("AI"), Machine Learning ("Machine Learning")": (1h) Overall understanding of artificial intelligence, machine learning, autonomous agents, real-time intelligent systems and human-artificial intelligence interaction and during the lecture practical work (in groups) in form of the case study created for this study course based on a practical situation/experience. 4. Power Bi: (9h) Individual independent work, during which the student will have to create analysis and visualization of financial data analysis using publicly available platform for Power Bi. 5. Intelligent Automation ("Intelligent Automation") and Sustainable Automation ("Sustainable Automation"): (9h) Individual independent work, during which the student will have to create financial data analysis and visualization using publicly available platform Power Automate. |
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| Requirements for awarding credit points | |
| Exam. Cumulative rating consisting of:
• Test on data, data science and visualization – 20%; • Calculations, discussions and practical tasks during classes for each learned topic within the study course – 35%; • Exam – independent work with presentation: data selection, processing, visualization, analysis and presentation of financial data of the organization 45%. |
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| Description of the organization and tasks of students’ independent work | |
| Studying and reviewing course materials to prepare for a test on data, data science, and visualization. Students independently prepare for each lesson by reading the assigned materials and information. Students, in agreement with the teacher, choose an organization for which they will perform individual independent work, performing data selection, data processing, visualization, analysis and development of recommendations for improvement, and prepare a presentation for the exam. | |
| Criteria for Evaluating Learning Outcomes | |
| The test can be passed if at least 50% of the questions are answered correctly. The practical tasks are prepared and checked in the lessons after learning each topic, and it is necessary to get at least 70% for the practical tasks in order for the practical tasks to be counted. The exam can be taken by students who have at least 50% correctly answered test questions and at least 70% obtained for practical work in classes. The exam task is evaluated in accordance with the evaluation criteria and procedures specified in the practical exam task. | |
| Compulsory reading | |
| Pamatliteratūra.
1.Köseoğlu S.D. Financial Data Analytics. Theory and Application (Contributions to Finance and Accounting). Springer Cham, 2022., 384 lpp., ISBN: 978-3-030-83798-3 2.Consoli S., Recupero D.R., Saisana M. Data Science for Economics and Finance: Methodologies and Applications. Springer Nature., Springer Cham, 2021., 355 lpp., ISBN 978-3-030-66890-7 (brīvpieejas) 3.Ferrari A., Russo M. Introducing Microsoft Power BI. 2016., 189 lpp., ISBN 978-1-5093-0228-4 (brīvpieejas) www.microsoftpressstore.com/store/introducing-microsoft-power-bi-9781509302284 4.Collab365. Beginner Ebook, The Beginners Guide to Power Automatev, 65 lpp., https://s3.amazonaws.com/media.collab365.community/Academycompanionebooks/TheBeginnersGuidetoPowerAutomatev2.pdf (brīvpieejas) 5.Microsoft Power BI, www.microsoft.com/en-us/power-platform/products/power-bi/learning (brīvpieejas) 6.Rad R., Power BI from Rookie to Rock, 2017, https://radacad.com/download-free-power-bi-book-pdf-format (brīvpieejas) |
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| Further reading | |
| Papildliteratūra.
1.Brott P., Mastering Microsoft Power Bi. Packt., 2018. www.packtpub.com/free-ebook/mastering-microsoft-power-bi/9781788297233 (brīvpieejas) 2.Microsoft Power Bi, https://www.microsoft.com/en-us/power-platform/products/power-bi?msockid=25f726da7836693111ce3585799c68de (brīvpieejas) 3.Microsoft Power Automate rīks, https://www.microsoft.com/en-us/power-platform/products/power-automate?msockid=25f726da7836693111ce3585799c68de |
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| Periodicals and other sources | |
| Periodika un vadlīnijas.
1.Harvard Business Review 2.Eiropas Parlaments, EU AI Act: first regulation on artificial intelligence, 2023, https:// www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence 3.Journal of Automation and Intelligence: https:// www.sciencedirect.com/journal/journal-of-automation-and-intelligence 4.Microsoft Power BI Blog, https://powerbi.microsoft.com/en-us/blog/ |
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| Notes | |
| Academic master's study program "Sustainable Finance" | |