Administracja Centralna Uczelni - Wymiana międzynarodowa (S1)
Sylabus przedmiotu Data Science and AI in Business:
Informacje podstawowe
| Kierunek studiów | Wymiana międzynarodowa | ||
|---|---|---|---|
| Forma studiów | studia stacjonarne | Poziom | pierwszego stopnia |
| Tytuł zawodowy absolwenta | |||
| Obszary studiów | — | ||
| Profil | |||
| Moduł | — | ||
| Przedmiot | Data Science and AI 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) | 3,0 | ECTS (formy) | 3,0 |
| Forma zaliczenia | zaliczenie | Język | angielski |
| Blok obieralny | — | Grupa obieralna | — |
Formy dydaktyczne
Wymagania wstępne
| KOD | Wymaganie wstępne |
|---|---|
| W-1 | The course requires basic knowledge of statistics and econometrics. |
Cele przedmiotu
| KOD | Cel modułu/przedmiotu |
|---|---|
| C-1 | Develop skills in analyzing business data and applying AI techniques for problem-solving and decision-making. |
| C-2 | Gain practical proficiency in R programming and industry tools such as Tidyverse, Tidymodels, TensorFlow, and Keras. |
| C-3 | Understand and evaluate the ethical implications of AI applications in business and their societal impact. |
| C-4 | Enhance the ability to communicate data-driven insights effectively and adapt to evolving business and technological environments. |
Treści programowe z podziałem na formy zajęć
| KOD | Treść programowa | Godziny |
|---|---|---|
| laboratoria | ||
| T-L-1 | Introduction to R Environment: Overview of R's capabilities for business data analysis, including essential packages and the tidy concepts that streamline data workflows in commercial applications. | 3 |
| T-L-2 | Data Handling with Tidyverse: Business-focused data transformation and organization using Tidyverse, ensuring efficient and reliable data preparation for decision-making and strategic insights. | 3 |
| T-L-3 | Variable Types and Data Transformations: Practical approaches to transforming variables, understanding their business relevance, and preparing data for advanced modeling in dynamic market environments. | 3 |
| T-L-4 | Data Visualization with ggplot2: Creating compelling, business-oriented visualizations that communicate key insights and support data-driven decision-making in management and strategy. | 3 |
| T-L-5 | Exploratory Data Analysis (EDA): Using EDA to uncover trends, patterns, and business opportunities through deep data exploration and insights. | 3 |
| T-L-6 | Working with Various Data Types and Big Data: Handling complex and large-scale business data to enhance operational efficiency, including techniques for integrating diverse data sources. | 3 |
| T-L-7 | Basic Programming and Iterative Functions for Data Analysis: Developing custom functions and applying iterative processes to streamline data analysis, driving efficiency in business analytics. | 3 |
| T-L-8 | Machine Learning with Tidymodels: Regression and Classification: Applying machine learning methods to real-world business problems, focusing on predictive modeling and classification to optimize operations and customer insights. | 3 |
| T-L-9 | Deep Learning with Tensorflow and Keras: Leveraging advanced deep learning techniques to address complex business challenges, such as customer behavior prediction and process optimization. | 3 |
| T-L-10 | Project Presentation: Economic Analysis of a Selected Business Problem: A capstone project where students analyze a real-world business issue using data science and AI techniques, presenting actionable insights and recommendations. | 3 |
| 30 | ||
Obciążenie pracą studenta - formy aktywności
| KOD | Forma aktywności | Godziny |
|---|---|---|
| laboratoria | ||
| A-L-1 | Participation in laboratories | 30 |
| A-L-2 | Student's own work on the project | 30 |
| A-L-3 | Analysis of documentation and literature | 15 |
| 75 | ||
Metody nauczania / narzędzia dydaktyczne
| KOD | Metoda nauczania / narzędzie dydaktyczne |
|---|---|
| M-1 | Laboratory exercises: independent development of problem tasks |
Sposoby oceny
| KOD | Sposób oceny |
|---|---|
| S-1 | Ocena podsumowująca: Assessment of the student's project |
| S-2 | Ocena formująca: Assessment of tasks solved by students |
Zamierzone efekty uczenia się - wiedza
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| WM-WEKON_1-_DSAI_W01 Understanding of key concepts in data science and AI relevant to business decision-making. | — | — | C-1 | T-L-1, T-L-5 | M-1 | S-1 |
| WM-WEKON_1-_DSAI_W02 Knowledge of R programming and data analysis frameworks, including Tidyverse, Tidymodels, TensorFlow, and Keras. | — | — | C-2 | T-L-7, T-L-1, T-L-9 | M-1 | S-2, S-1 |
| WM-WEKON_1-_DSAI_W03 Insight into the integration of machine learning and deep learning in solving business problems | — | — | C-2, C-3, C-1 | T-L-5, T-L-8, T-L-3, T-L-2, T-L-6, T-L-10 | M-1 | S-2, S-1 |
Zamierzone efekty uczenia się - umiejętności
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| WM-WEKON_1-_DSAI_U01 Ability to manipulate, analyze, and visualize business data using R and its core packages. | — | — | C-2, C-1 | T-L-2, T-L-3 | M-1 | S-2, S-1 |
| WM-WEKON_1-_DSAI_U02 Proficiency in applying machine learning models for business forecasting, classification, and optimization. | — | — | C-2, C-4, C-1, C-3 | T-L-9, T-L-8 | M-1 | S-1, S-2 |
| WM-WEKON_1-_DSAI_U03 Skill in communicating data-driven insights to support strategic business decisions. | — | — | C-1 | T-L-4, T-L-5 | M-1 | S-2 |
Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| WM-WEKON_1-_DSAI_K01 Capacity to work collaboratively on data-driven projects, demonstrating leadership in problem-solving. | — | — | C-4, C-3 | T-L-8, T-L-10, T-L-2, T-L-1, T-L-4, T-L-3, T-L-9 | M-1 | S-2, S-1 |
| WM-WEKON_1-_DSAI_K02 Ethical awareness of AI applications and their impact on business and society. | — | — | C-3, C-4 | T-L-10, T-L-4 | M-1 | S-2 |
| WM-WEKON_1-_DSAI_K03 Adaptability to rapidly evolving technologies and business environments. | — | — | C-3, C-4 | T-L-1, T-L-10 | M-1 | S-2 |
Kryterium oceny - wiedza
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| WM-WEKON_1-_DSAI_W01 Understanding of key concepts in data science and AI relevant to business decision-making. | 2,0 | |
| 3,0 | Demonstrates basic understanding of key data science and AI concepts with limited ability to apply them to business decision-making. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 | ||
| WM-WEKON_1-_DSAI_W02 Knowledge of R programming and data analysis frameworks, including Tidyverse, Tidymodels, TensorFlow, and Keras. | 2,0 | |
| 3,0 | Shows foundational knowledge of R programming and familiarity with basic features of data analysis frameworks such as Tidyverse, Tidymodels, TensorFlow, and Keras, with minimal practical application | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 | ||
| WM-WEKON_1-_DSAI_W03 Insight into the integration of machine learning and deep learning in solving business problems | 2,0 | |
| 3,0 | Demonstrates a basic understanding of the integration of machine learning and deep learning in addressing business problems, with limited ability to provide practical examples. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Kryterium oceny - umiejętności
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| WM-WEKON_1-_DSAI_U01 Ability to manipulate, analyze, and visualize business data using R and its core packages. | 2,0 | |
| 3,0 | Shows limited ability to manipulate, analyze, and visualize business data using basic functions in R and its core packages. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 | ||
| WM-WEKON_1-_DSAI_U02 Proficiency in applying machine learning models for business forecasting, classification, and optimization. | 2,0 | |
| 3,0 | Demonstrates basic understanding of machine learning models with minimal ability to apply them to business forecasting, classification, or optimization tasks. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 | ||
| WM-WEKON_1-_DSAI_U03 Skill in communicating data-driven insights to support strategic business decisions. | 2,0 | |
| 3,0 | Presents data-driven insights with limited clarity and relevance to strategic business decisions. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Kryterium oceny - inne kompetencje społeczne i personalne
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| WM-WEKON_1-_DSAI_K01 Capacity to work collaboratively on data-driven projects, demonstrating leadership in problem-solving. | 2,0 | |
| 3,0 | Participates in collaborative projects with limited contribution to problem-solving or leadership. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 | ||
| WM-WEKON_1-_DSAI_K02 Ethical awareness of AI applications and their impact on business and society. | 2,0 | |
| 3,0 | Demonstrates a basic understanding of ethical considerations in AI with limited ability to evaluate their impact on business and society. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 | ||
| WM-WEKON_1-_DSAI_K03 Adaptability to rapidly evolving technologies and business environments. | 2,0 | |
| 3,0 | Shows minimal adaptability to new technologies and business changes, requiring guidance to respond effectively. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Literatura podstawowa
- Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund., R for data science., O'Reilly Media, Inc., 2023
- Kuhn, Max, and Julia Silge., Tidy modeling with R., O'Reilly Media, Inc., 2022