Data Science for (Social) Good (2235.YR.015781.1)

General information

Type:	OBL	Curs:	1	Period:	S semester
ECTS Credits:	2 ECTS

Teaching Staff:

Students should have successfully completed the course "Programming with Data" or any other equivalent course, including the elective course "Advanced Python Programming" or "Intermediate Python Programming & a Glimpse into AI Applications".

Students should have a basic understanding of computer programming and demonstrate profficiency in Python. This includes working with librares and modules in general and specifically with Pandas.They should also be familiar with basic concepts in statistics and probability. Basic knowledge of data analysis and visualization techniques will be helpful, yet not strictly required.

This course has a workload of 2ECTS, which corresponds to 50 hours of work by the student. These hours will be approximately distributed as follows:

- In-class sessions (15 hours). Lecture hours will be devoted to introducing the necessary theoretical and practical concepts.
- Independent field work (15 hours). You will spend substantive time working in the field to learn by doing. You will be required to observe, listen, participate and engage with social organizations in understanding their needs.
- Collaborative work (20 hours).

This course aims to provide students with a deeper understanding of data science fundamentals while emphasizing the responsible use of technology for positive social impact. Students will develop practical skills in data extraction, manipulation, and exploratory data analysis. By engaging in service learning activities, students also immerse themselves in the challenges faced by real organizations. By delving into the struggles, constraints, limited resources, and overall difficulties experienced by social entities when implementing data science projects, students will gain a deep appreciation for the intricate landscape of leveraging data for social impact.

In today's rapidly evolving digital era, organizations, including nonprofits and NGOs, are increasingly recognizing the immense potential of data science to drive positive change. However, the implementation of data-driven initiatives often encounters significant roadblocks and hurdles that must be overcome. Through this course, students will gain firsthand insights into these challenges and understand the complexities associated with gathering, handling, and utilizing data effectively in real-world scenarios. Through a combination of lectures, hands-on labs, case studies, project-based learning, group discussions, and guest speaker sessions, students will develop a comprehensive skill set encompassing data science techniques, ethical considerations, and effective communication. They will analyze and discuss real-world data science projects for social good, engaging in critical reflections on the ethical implications and societal consequences of data-driven solutions.

This course aligns with the program's objective of preparing students to become responsible leaders who can apply artificial intelligence and data science techniques to create value for businesses and society. By sensitizing students to the struggles, constraints, and limited resources of real organizations, while emphasizing the value of observation, listening, and holistic problem-solving, the course will equip students with the necessary tools to make a positive difference in the world through responsible and impactful data science practices.

By exploring the multifaceted aspects of data science within organizations, students will develop a heightened awareness of the vital role that observation and active listening play in uncovering the true needs and aspirations of these entities. They will learn that the genuine requirements of organizations may not always be readily apparent, necessitating a careful study of their context to truly comprehend the bigger picture. Through this lens, students will develop a more holistic understanding of the organizations they serve, emphasizing the importance of empathy, and a comprehensive approach to problem-solving.

By the end of the course, students will have achieved the following objectives:

- Develop a comprehensive understanding of data science fundamentals: Students will gain a solid foundation in data science concepts, techniques, and methodologies, including data extraction, manipulation, exploratory data analysis, and visualization.
- Gain insight into the challenges of implementing data science in organizations: Students will be exposed to the real-world challenges faced by NGOs and other organizations when implementing data science projects. They will learn about the constraints, limited resources, and difficulties encountered in the collection, management, and utilization of data for social impact.
- Cultivate empathy and observation skills: Students will recognize the value of observation and active listening in understanding the true needs and aspirations of organizations. They will learn to empathize with the challenges faced by NGOs and develop observational skills to uncover underlying requirements that may not be immediately apparent.
- Apply data science techniques to address real-world problems: Through project-based learning and collaboration with nonprofit organizations, students will have the opportunity to apply their data science skills to solve real-world challenges. They will work on meaningful projects, leveraging data science methodologies to provide actionable insights and solutions to address the needs of organizations and contribute to their efforts.
- Evaluate the ethical implications and social impact of data-driven solutions: Students will critically analyze the ethical dimensions and social consequences of data science applications. They will consider the potential biases, privacy concerns, and equity issues associated with data-driven decision-making, and develop a responsible and ethical mindset in utilizing data science for social good.
- Communicate effectively about data science projects and their societal implications: Students will enhance their communication skills by effectively conveying their data science projects, methodologies, and findings. They will learn to communicate complex technical concepts in a clear and understandable manner to stakeholders with varying levels of data literacy. Additionally, they will be able to articulate the societal implications and potential benefits of their data-driven solutions.
- Foster a sense of social responsibility and a commitment to positive social impact: Through the exploration of data science for social good initiatives and engaging with nonprofit organizations, students will develop a deep understanding of their role as responsible leaders in leveraging technology for positive social change. They will be motivated to apply their data science skills and knowledge to make a meaningful difference in addressing societal challenges.