Lecture (LEC)

• Above class meets for the thirteen week session
• Above class has reserved seats for Data Science MS Online Students and Graduate Certificate Students
• If you are not a Data Science MS Online or Data Science Graduate Certificate Student, you must contact the department to enroll

Course Context In the last decade, data generated from the Internet, sensors, wearables, and online communities has exploded, creating immense opportunities to empirically study social, behavioral, and environmental phenomena. This course will equip you with the AI tools needed to work with ¿big social data¿ and beyond, enabling you to derive insights from complex datasets. Our interdisciplinary approach spans AI applications to real-world issues in behavioral research, health, environmental studies, and design. Alongside other quantitative methods, you will briefly engage with LLMs to explore how they can complement traditional data analysis and presentation methods. Objectives By the end of this course, you will be able to: - Develop foundational knowledge in AI and machine learning for real-world data analysis. - Utilize Python libraries such as scikit-learn for machine learning, Pandas and NumPy for data handling, and Matplotlib and others for visualization. - Prepare and manipulate basic data types, including numerical, categorical, and textual data, to make datasets ready for analysis. - Analyze data using exploratory visualization techniques to identify patterns, trends, and insights. - Perform data cleaning and transformation, including merging, reshaping, and aggregating datasets. - Apply essential machine learning models for tasks like classification, clustering, and regression using scikit-learn. - Understand and apply basic feature engineering techniques, such as dimensionality reduction, to enhance model performance. - Have a basic knowledge of deep learning architectures and basic embedding/vectorial representations of data. - Explore fundamental NLP techniques using libraries like NLTK and spaCy to work with textual data. - Use large language models (LLMs) for general machine learning tasks using local or API-based models. (optional modules) - You will showcase your learned skills by undertaking a semester-long project. This project will require detailed documentation of each step involved in its development, from initial concept to final execution. More information here: https://github.com/filipinascimento/usable_ai