Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Wydział Ekonomiczny - Economics (S1)
specjalność: Accounting and Finance in Economic Entities

Sylabus przedmiotu Fundamentals of data science in business:

Informacje podstawowe

Kierunek studiów Economics
Forma studiów studia stacjonarne Poziom pierwszego stopnia
Tytuł zawodowy absolwenta licencjat
Obszary studiów charakterystyki PRK
Profil ogólnoakademicki
Moduł —
Przedmiot Fundamentals of data science in business
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Ekonomii, Finansów i Rachunkowości
Nauczyciel odpowiedzialny Błażej Suproń <Blazej.Supron@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 2,0 ECTS (formy) 2,0
Forma zaliczenia zaliczenie Język angielski
Blok obieralny 2 Grupa obieralna 3

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
ćwiczenia audytoryjneA4 25 2,01,00zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1The student has basic knowledge of statistics and econometrics.
W-2The student has basic knowledge of information technology.
W-3The student is able to supplement and improve acquired knowledge and skills.

Cele przedmiotu

KODCel modułu/przedmiotu
C-1Introducing students to the principles of data processing and analysis.
C-2Developing practical skills in data mining and business data visualization.

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
ćwiczenia audytoryjne
T-A-1Acquisition and processing of business data. Open-source repositories of economic and business data. The use of AI in data analysis.2
T-A-2Configuration of the programming environment. Using notebooks for business data analysis and visualization.4
T-A-3Loading Data from Various Sources, Transformation, Grouping, and Data Organization. Utilizing scripts.4
T-A-4Operations on Different Data Types and Variables. Merging Various Datasets.4
T-A-5Visualization of Economic and Business Data. Exploratory Data Analysis.4
T-A-6Utilizing Basic Statistical Functions for Economic and Business Problem Analysis. Forecasting Using Simple Models.2
T-A-7Foundations of Machine Learning. Applying Basic Classification and Regression Algorithms in Business Tasks.4
T-A-8Final Project Presentation1
25

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
ćwiczenia audytoryjne
A-A-1participation in classes25
A-A-2Project preparation10
A-A-3Preparation for classes15
50

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1Computer-based laboratory exercises and didactic discussion.
M-2Students' independent work with educational materials.

Sposoby oceny

KODSposób oceny
S-1Ocena podsumowująca: Project for verifying course objectives and acquired skills.

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
Ec_1A_O07/4.2_W01
The student knows the basic concepts and processes related to data analysis and their application in a business context.		Ec_1A_W03—C-1, C-2T-A-1, T-A-2, T-A-3, T-A-4, T-A-5, T-A-6, T-A-7, T-A-8M-1, M-2S-1
Ec_1A_O07/4.2_W02
The student understands methods of data acquisition, processing, analysis, and visualization, as well as the principles of popular machine learning algorithms.		Ec_1A_W01—C-1, C-2T-A-1, T-A-2, T-A-3, T-A-4, T-A-5, T-A-6, T-A-7, T-A-8M-1, M-2S-1

Zamierzone efekty uczenia się - umiejętności

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
Ec_1A_O07/4.2_U01
The student can effectively process, analyze, and visualize data, and present it in clear reports and presentations tailored to the needs of stakeholders.		Ec_1A_U03, Ec_1A_U04—C-1, C-2T-A-2, T-A-3, T-A-4, T-A-5, T-A-6M-1S-1
Ec_1A_O07/4.2_U02
The student can apply basic machine learning algorithms to solve business problems and assess the effectiveness and quality of developed models.		Ec_1A_U04—C-2, C-1T-A-1, T-A-6, T-A-7M-1, M-2S-1

Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
Ec_1A_O07/4.2_K01
The student can work effectively in a team, taking initiative in analytical projects and supporting group collaboration.		Ec_1A_K02—C-2T-A-2, T-A-4, T-A-6, T-A-8M-1, M-2S-1

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
Ec_1A_O07/4.2_W01
The student knows the basic concepts and processes related to data analysis and their application in a business context.		2,0
3,0The student knows and understands the basic concepts related to data analysis. They can list fundamental data analysis processes but do not always fully grasp their application in a business context. They demonstrate a basic knowledge of analytical tools and methods.
3,5The student knows the basic concepts of data analysis and can define them correctly. They understand the main stages of data analysis and can identify their application in business at a basic level. They demonstrate knowledge of fundamental analytical methods but may struggle with their practical application.
4,0The student has a good understanding of data analysis concepts and processes. They can identify their application in various business areas. They are familiar with popular analytical tools and methods and can correctly discuss their practical use.
4,5The student demonstrates a solid knowledge of data analysis concepts and processes. They can independently identify examples of data analysis applications in business and determine appropriate methods for different situations. They confidently use analytical terminology and can logically justify their reasoning.
5,0The student possesses a very strong understanding of data analysis concepts, methods, and processes, along with their business applications. They can critically analyze and compare various analytical methods, providing specific examples of their use. They have an excellent command of analytical tools and understand their limitations. They confidently present and explain data analysis-related topics.
Ec_1A_O07/4.2_W02
The student understands methods of data acquisition, processing, analysis, and visualization, as well as the principles of popular machine learning algorithms.		2,0
3,0The student has a general knowledge of methods for acquiring, processing, analyzing, and visualizing data but does not always describe them correctly. They are familiar with basic machine learning algorithms but struggle to explain their principles. Their knowledge of tools and techniques is limited to the most fundamental concepts.
3,5The student understands basic methods of data acquisition, processing, and analysis and is familiar with essential visualization techniques. They can generally explain the principles of several popular machine learning algorithms, though they may struggle with a more detailed explanation. They demonstrate a basic understanding of tools used in data analysis.
4,0The student has a good understanding of methods for acquiring, processing, analyzing, and visualizing data and can describe them correctly. They can explain the principles of the most commonly used machine learning algorithms and identify their potential applications. They possess practical knowledge of tools used in Data Science and understand their fundamental limitations.
4,5The student demonstrates solid knowledge of data analysis and visualization methods and can compare different approaches to data processing. They understand the mechanisms of key machine learning algorithms, can classify them, and identify their strengths and weaknesses. They confidently use Data Science terminology.
5,0The student has an excellent understanding of data acquisition, processing, analysis, and visualization methods and can explain their applications in various business contexts. They thoroughly understand the principles of popular machine learning algorithms, can compare their effectiveness, and identify the most suitable applications. They confidently work with analytical tools and take a critical approach to their selection and interpretation of results.

Kryterium oceny - umiejętności

Efekt uczenia sięOcenaKryterium oceny
Ec_1A_O07/4.2_U01
The student can effectively process, analyze, and visualize data, and present it in clear reports and presentations tailored to the needs of stakeholders.		2,0
3,0The student can perform basic data operations, such as initial preprocessing and analysis. They create simple visualizations, but these may be inaccurate or unclear. Their reports and presentations contain basic information but are not very structured or fully tailored to the audience. The student knows and uses fundamental programming commands in the field of data science.
3,5The student efficiently conducts basic data analyses and can create correct, though not highly advanced, visualizations. Their reports and presentations contain key information but may need refinement in terms of aesthetics and adaptation to different stakeholder groups. The student knows and uses basic programming commands in data science and can work with more complex commands with the help of documentation.
4,0The student can comprehensively process and analyze data using appropriate methods and tools. They create clear and accurate visualizations that effectively illustrate analysis results. Their reports and presentations are well-structured, readable, and adapted to audience expectations. The student knows and applies complex programming structures in data science.
4,5The student processes and analyzes data fluently, applying different techniques depending on the context. They create attractive, readable, and methodologically sound visualizations. Their reports and presentations are well-organized, logical, and tailored to different stakeholder groups, considering their needs and knowledge level. The student knows and applies complex programming structures in data science, including loops and automation techniques.
5,0The student demonstrates advanced skills in data processing, analysis, and visualization. They create aesthetically pleasing, professional visualizations suited to specific problems. Their reports and presentations are clear, logical, and fully adapted to stakeholder needs, containing essential insights and recommendations. They effectively communicate analysis results, adjusting language and format to the audience. The student knows and applies advanced programming structures and commands in data science.
Ec_1A_O07/4.2_U02
The student can apply basic machine learning algorithms to solve business problems and assess the effectiveness and quality of developed models.		2,0
3,0The student can apply basic machine learning algorithms but struggles with selecting the appropriate one for specific business problems. They understand basic model evaluation metrics but do not always interpret them correctly. Their analysis is superficial, and conclusions may be imprecise.
3,5The student can correctly select a basic algorithm for a specific business problem and implement it. They use fundamental model evaluation metrics, though their interpretation may be incomplete. Their conclusions are correct but may require further justification.
4,0The student can apply various machine learning algorithms to solve business problems and accurately assess their effectiveness. They can use and interpret basic evaluation metrics, identifying the strengths and weaknesses of models. Their analysis is logical and coherent.
4,5The student demonstrates the ability to consciously select ML algorithms for specific business cases, compare different models, and choose the optimal solution. They can thoroughly evaluate model quality using various metrics and analyze potential prediction errors. Their interpretation of results is precise and well-justified.
5,0The student proficiently applies machine learning algorithms to solve business problems and critically assesses model effectiveness. They can tailor evaluation methods to the specific problem, correctly analyze results, and identify opportunities for model improvement. They demonstrate a deep understanding of algorithm functionality and limitations.

Kryterium oceny - inne kompetencje społeczne i personalne

Efekt uczenia sięOcenaKryterium oceny
Ec_1A_O07/4.2_K01
The student can work effectively in a team, taking initiative in analytical projects and supporting group collaboration.		2,0
3,0The student participates in team activities but has limited engagement. They complete assigned tasks but rarely take initiative. They may struggle with communication and collaboration with other team members.
3,5The student demonstrates basic teamwork skills, actively participates in tasks, but their initiative is limited. They support other team members, though not always effectively. Their communication is adequate but may need improvement.
4,0The student collaborates effectively in a team, actively contributing to analytical projects and taking initiative in problem-solving. They support team members and communicate effectively with others.
4,5The student not only works efficiently in a team but also actively contributes to organizing teamwork. They can motivate others, propose improvements in project execution, and effectively resolve collaboration challenges.
5,0The student demonstrates a high level of initiative in teamwork, effectively coordinating group efforts and fostering collaboration. They take on a leadership role in analytical projects, ensuring efficient communication and constructive conflict resolution. Their teamwork skills contribute to achieving high-quality group outcomes.

Literatura podstawowa

  1. Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund, R for data science, O'Reilly Media, Inc., 2023, https://r4ds.hadley.nz
  2. Kuhn, Max, and Julia Silge, Tidy modeling with R, O'Reilly Media, Inc, 2022, https://www.tmwr.org
  3. VanderPlas, Jake, Python data science handbook: Essential tools for working with data, O'Reilly Media, Inc., 2016

Literatura dodatkowa

  1. RPubs blog, 2024, https://rpubs.com/

Treści programowe - ćwiczenia audytoryjne

KODTreść programowaGodziny
T-A-1Acquisition and processing of business data. Open-source repositories of economic and business data. The use of AI in data analysis.2
T-A-2Configuration of the programming environment. Using notebooks for business data analysis and visualization.4
T-A-3Loading Data from Various Sources, Transformation, Grouping, and Data Organization. Utilizing scripts.4
T-A-4Operations on Different Data Types and Variables. Merging Various Datasets.4
T-A-5Visualization of Economic and Business Data. Exploratory Data Analysis.4
T-A-6Utilizing Basic Statistical Functions for Economic and Business Problem Analysis. Forecasting Using Simple Models.2
T-A-7Foundations of Machine Learning. Applying Basic Classification and Regression Algorithms in Business Tasks.4
T-A-8Final Project Presentation1
25

Formy aktywności - ćwiczenia audytoryjne

KODForma aktywnościGodziny
A-A-1participation in classes25
A-A-2Project preparation10
A-A-3Preparation for classes15
50
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięEc_1A_O07/4.2_W01The student knows the basic concepts and processes related to data analysis and their application in a business context.
Odniesienie do efektów kształcenia dla kierunku studiówEc_1A_W03He / she knows and understands the research methodology in the field of economics and finance at an advanced level
Cel przedmiotuC-1Introducing students to the principles of data processing and analysis.
C-2Developing practical skills in data mining and business data visualization.
Treści programoweT-A-1Acquisition and processing of business data. Open-source repositories of economic and business data. The use of AI in data analysis.
T-A-2Configuration of the programming environment. Using notebooks for business data analysis and visualization.
T-A-3Loading Data from Various Sources, Transformation, Grouping, and Data Organization. Utilizing scripts.
T-A-4Operations on Different Data Types and Variables. Merging Various Datasets.
T-A-5Visualization of Economic and Business Data. Exploratory Data Analysis.
T-A-6Utilizing Basic Statistical Functions for Economic and Business Problem Analysis. Forecasting Using Simple Models.
T-A-7Foundations of Machine Learning. Applying Basic Classification and Regression Algorithms in Business Tasks.
T-A-8Final Project Presentation
Metody nauczaniaM-1Computer-based laboratory exercises and didactic discussion.
M-2Students' independent work with educational materials.
Sposób ocenyS-1Ocena podsumowująca: Project for verifying course objectives and acquired skills.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student knows and understands the basic concepts related to data analysis. They can list fundamental data analysis processes but do not always fully grasp their application in a business context. They demonstrate a basic knowledge of analytical tools and methods.
3,5The student knows the basic concepts of data analysis and can define them correctly. They understand the main stages of data analysis and can identify their application in business at a basic level. They demonstrate knowledge of fundamental analytical methods but may struggle with their practical application.
4,0The student has a good understanding of data analysis concepts and processes. They can identify their application in various business areas. They are familiar with popular analytical tools and methods and can correctly discuss their practical use.
4,5The student demonstrates a solid knowledge of data analysis concepts and processes. They can independently identify examples of data analysis applications in business and determine appropriate methods for different situations. They confidently use analytical terminology and can logically justify their reasoning.
5,0The student possesses a very strong understanding of data analysis concepts, methods, and processes, along with their business applications. They can critically analyze and compare various analytical methods, providing specific examples of their use. They have an excellent command of analytical tools and understand their limitations. They confidently present and explain data analysis-related topics.
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięEc_1A_O07/4.2_W02The student understands methods of data acquisition, processing, analysis, and visualization, as well as the principles of popular machine learning algorithms.
Odniesienie do efektów kształcenia dla kierunku studiówEc_1A_W01He / she knows and understands at an advanced level economic and social facts and phenomena as well as theories explaining the complex relationships between them constituting the basic general knowledge in the discipline of economics and finance, as well as selected issues in the field of detailed knowledge
Cel przedmiotuC-1Introducing students to the principles of data processing and analysis.
C-2Developing practical skills in data mining and business data visualization.
Treści programoweT-A-1Acquisition and processing of business data. Open-source repositories of economic and business data. The use of AI in data analysis.
T-A-2Configuration of the programming environment. Using notebooks for business data analysis and visualization.
T-A-3Loading Data from Various Sources, Transformation, Grouping, and Data Organization. Utilizing scripts.
T-A-4Operations on Different Data Types and Variables. Merging Various Datasets.
T-A-5Visualization of Economic and Business Data. Exploratory Data Analysis.
T-A-6Utilizing Basic Statistical Functions for Economic and Business Problem Analysis. Forecasting Using Simple Models.
T-A-7Foundations of Machine Learning. Applying Basic Classification and Regression Algorithms in Business Tasks.
T-A-8Final Project Presentation
Metody nauczaniaM-1Computer-based laboratory exercises and didactic discussion.
M-2Students' independent work with educational materials.
Sposób ocenyS-1Ocena podsumowująca: Project for verifying course objectives and acquired skills.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student has a general knowledge of methods for acquiring, processing, analyzing, and visualizing data but does not always describe them correctly. They are familiar with basic machine learning algorithms but struggle to explain their principles. Their knowledge of tools and techniques is limited to the most fundamental concepts.
3,5The student understands basic methods of data acquisition, processing, and analysis and is familiar with essential visualization techniques. They can generally explain the principles of several popular machine learning algorithms, though they may struggle with a more detailed explanation. They demonstrate a basic understanding of tools used in data analysis.
4,0The student has a good understanding of methods for acquiring, processing, analyzing, and visualizing data and can describe them correctly. They can explain the principles of the most commonly used machine learning algorithms and identify their potential applications. They possess practical knowledge of tools used in Data Science and understand their fundamental limitations.
4,5The student demonstrates solid knowledge of data analysis and visualization methods and can compare different approaches to data processing. They understand the mechanisms of key machine learning algorithms, can classify them, and identify their strengths and weaknesses. They confidently use Data Science terminology.
5,0The student has an excellent understanding of data acquisition, processing, analysis, and visualization methods and can explain their applications in various business contexts. They thoroughly understand the principles of popular machine learning algorithms, can compare their effectiveness, and identify the most suitable applications. They confidently work with analytical tools and take a critical approach to their selection and interpretation of results.
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięEc_1A_O07/4.2_U01The student can effectively process, analyze, and visualize data, and present it in clear reports and presentations tailored to the needs of stakeholders.
Odniesienie do efektów kształcenia dla kierunku studiówEc_1A_U03He / she is able to solve tasks and problems in conditions that are not fully predictable, properly selecting sources and information from them, making their evaluation, making a critical analysis of them and synthesising them
Ec_1A_U04He / she is able to select and apply appropriate methods and tools, including advanced information and communication techniques for analysing and forecasting economic processes and phenomena and solving economic problems
Cel przedmiotuC-1Introducing students to the principles of data processing and analysis.
C-2Developing practical skills in data mining and business data visualization.
Treści programoweT-A-2Configuration of the programming environment. Using notebooks for business data analysis and visualization.
T-A-3Loading Data from Various Sources, Transformation, Grouping, and Data Organization. Utilizing scripts.
T-A-4Operations on Different Data Types and Variables. Merging Various Datasets.
T-A-5Visualization of Economic and Business Data. Exploratory Data Analysis.
T-A-6Utilizing Basic Statistical Functions for Economic and Business Problem Analysis. Forecasting Using Simple Models.
Metody nauczaniaM-1Computer-based laboratory exercises and didactic discussion.
Sposób ocenyS-1Ocena podsumowująca: Project for verifying course objectives and acquired skills.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student can perform basic data operations, such as initial preprocessing and analysis. They create simple visualizations, but these may be inaccurate or unclear. Their reports and presentations contain basic information but are not very structured or fully tailored to the audience. The student knows and uses fundamental programming commands in the field of data science.
3,5The student efficiently conducts basic data analyses and can create correct, though not highly advanced, visualizations. Their reports and presentations contain key information but may need refinement in terms of aesthetics and adaptation to different stakeholder groups. The student knows and uses basic programming commands in data science and can work with more complex commands with the help of documentation.
4,0The student can comprehensively process and analyze data using appropriate methods and tools. They create clear and accurate visualizations that effectively illustrate analysis results. Their reports and presentations are well-structured, readable, and adapted to audience expectations. The student knows and applies complex programming structures in data science.
4,5The student processes and analyzes data fluently, applying different techniques depending on the context. They create attractive, readable, and methodologically sound visualizations. Their reports and presentations are well-organized, logical, and tailored to different stakeholder groups, considering their needs and knowledge level. The student knows and applies complex programming structures in data science, including loops and automation techniques.
5,0The student demonstrates advanced skills in data processing, analysis, and visualization. They create aesthetically pleasing, professional visualizations suited to specific problems. Their reports and presentations are clear, logical, and fully adapted to stakeholder needs, containing essential insights and recommendations. They effectively communicate analysis results, adjusting language and format to the audience. The student knows and applies advanced programming structures and commands in data science.
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięEc_1A_O07/4.2_U02The student can apply basic machine learning algorithms to solve business problems and assess the effectiveness and quality of developed models.
Odniesienie do efektów kształcenia dla kierunku studiówEc_1A_U04He / she is able to select and apply appropriate methods and tools, including advanced information and communication techniques for analysing and forecasting economic processes and phenomena and solving economic problems
Cel przedmiotuC-2Developing practical skills in data mining and business data visualization.
C-1Introducing students to the principles of data processing and analysis.
Treści programoweT-A-1Acquisition and processing of business data. Open-source repositories of economic and business data. The use of AI in data analysis.
T-A-6Utilizing Basic Statistical Functions for Economic and Business Problem Analysis. Forecasting Using Simple Models.
T-A-7Foundations of Machine Learning. Applying Basic Classification and Regression Algorithms in Business Tasks.
Metody nauczaniaM-1Computer-based laboratory exercises and didactic discussion.
M-2Students' independent work with educational materials.
Sposób ocenyS-1Ocena podsumowująca: Project for verifying course objectives and acquired skills.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student can apply basic machine learning algorithms but struggles with selecting the appropriate one for specific business problems. They understand basic model evaluation metrics but do not always interpret them correctly. Their analysis is superficial, and conclusions may be imprecise.
3,5The student can correctly select a basic algorithm for a specific business problem and implement it. They use fundamental model evaluation metrics, though their interpretation may be incomplete. Their conclusions are correct but may require further justification.
4,0The student can apply various machine learning algorithms to solve business problems and accurately assess their effectiveness. They can use and interpret basic evaluation metrics, identifying the strengths and weaknesses of models. Their analysis is logical and coherent.
4,5The student demonstrates the ability to consciously select ML algorithms for specific business cases, compare different models, and choose the optimal solution. They can thoroughly evaluate model quality using various metrics and analyze potential prediction errors. Their interpretation of results is precise and well-justified.
5,0The student proficiently applies machine learning algorithms to solve business problems and critically assesses model effectiveness. They can tailor evaluation methods to the specific problem, correctly analyze results, and identify opportunities for model improvement. They demonstrate a deep understanding of algorithm functionality and limitations.
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięEc_1A_O07/4.2_K01The student can work effectively in a team, taking initiative in analytical projects and supporting group collaboration.
Odniesienie do efektów kształcenia dla kierunku studiówEc_1A_K02He / she is ready to fulfil social obligations, co-organise projects for the social environment, as well as initiate activities for the public interest
Cel przedmiotuC-2Developing practical skills in data mining and business data visualization.
Treści programoweT-A-2Configuration of the programming environment. Using notebooks for business data analysis and visualization.
T-A-4Operations on Different Data Types and Variables. Merging Various Datasets.
T-A-6Utilizing Basic Statistical Functions for Economic and Business Problem Analysis. Forecasting Using Simple Models.
T-A-8Final Project Presentation
Metody nauczaniaM-1Computer-based laboratory exercises and didactic discussion.
M-2Students' independent work with educational materials.
Sposób ocenyS-1Ocena podsumowująca: Project for verifying course objectives and acquired skills.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student participates in team activities but has limited engagement. They complete assigned tasks but rarely take initiative. They may struggle with communication and collaboration with other team members.
3,5The student demonstrates basic teamwork skills, actively participates in tasks, but their initiative is limited. They support other team members, though not always effectively. Their communication is adequate but may need improvement.
4,0The student collaborates effectively in a team, actively contributing to analytical projects and taking initiative in problem-solving. They support team members and communicate effectively with others.
4,5The student not only works efficiently in a team but also actively contributes to organizing teamwork. They can motivate others, propose improvements in project execution, and effectively resolve collaboration challenges.
5,0The student demonstrates a high level of initiative in teamwork, effectively coordinating group efforts and fostering collaboration. They take on a leadership role in analytical projects, ensuring efficient communication and constructive conflict resolution. Their teamwork skills contribute to achieving high-quality group outcomes.