(in Polish) Podstawy programowania i analiza danych w Python 2600-IADdz1PPADP
1) Introduction to Python and its work environment.
2) Python basics.
3) Built-in data structures, functions, and files.
4) NumPy basics.
5) Introduction to the pandas library.
6) Reading and writing data.
7) Data cleaning and preparation.
8) Data processing.
9) Data visualization.
10) Data aggregation and group operations.
11) Time series.
12) Introduction to modeling.
13) Practical data analysis examples.
Type of course
Learning outcomes
K_W01 - Demonstrates in-depth knowledge of research methodology and terminology within the discipline of economics and finance and its complementary disciplines (management, quality, and legal sciences).
K_W05 - Complex technological, social, political, legal, economic, and ecological processes and phenomena, including fundamental dilemmas of modern civilization and their impact on financial decisions in organizations, the functioning of the entire economy, and organizations in the development of information systems.
K_W06 - Principles of industrial property protection and copyright.
K_W07 - Information technology and numerical methods necessary for solving financial problems; familiar with selected software used in finance.
K_U03 - Uses appropriate source selection and adapts existing or develops new methods and tools, including advanced information and communication techniques, to recognize, diagnose, and solve problems related to financial decisions in the field of investment and data analysis.
K_U06 - Independently and collaboratively prepare analyses, diagnoses, and reports on complex and unusual issues related to investments and data analysis in organizations, present them communicatively to diverse audiences, and lead debates, also in English, using advanced IT and communication tools.
K_U08 - Plan, organize, and manage teamwork, collaborate in teams, and take a leading role in team activities.
K_K05 - Adhere to, enforce, and develop professional ethical standards and build on the professional achievements.
Assessment criteria
- quizzes (open-ended and closed-ended questions) in person or remotely on the Kampus platform,
- class participation (exercises, case studies),
- project.
Practical placement
Professional practice is not required to complete the course
Bibliography
Basic literature:
1) McKinney W., Python w analizie danych. Przetwarzanie danych za pomocą pakietów pandas i NumPy oraz środowiska Jupyter, Wydanie III, Helion, 2023.
2) Matthes E., Python. Instrukcje dla programisty, Wydanie III, Helion, 2023.
3) Lubanovic B., Python. Nowoczesne programowanie w prostych krokach, Wydanie II, Helion, 2021.
Additional literature:
1) VanderPlas J., Python Data Science. Niezbędne narzędzia do pracy z danymi. Wydanie II, O'Reilly Media, 2023.