This course introduces students to social data analytics. Students will explore data collection, cleaning, visualization, and modern data science techniques, including optional topics such as Bayes, big data, Monte Carlo simulation, and machine learning. Using tools such as R and/or Python, students will apply these methods to analyze social phenomena through hands-on assignments/projects. Ethical considerations and societal impacts of data use are emphasized. By integrating modern analytical approaches, students gain skills to interpret social data and address real-world issues, preparing them for data-driven research in sociology.

CILO-1: Demonstrate an understanding of basic mathematics, probability, and modern statistical techniques relevant to social data analytics and science in general. CILO-2: Discuss the ethical considerations and societal implications of data-driven decision-making in social science research, especially those relevant to modern social data. CILO-3: Apply tools like R or Python to clean, preprocess, and manage modern social datasets. CILO-4: Create effective visualizations to communicate patterns and insights from modern social data. CILO-5: Perform exploratory data analysis and apply foundational statistical and data science methods to identify patterns, ascertain relationships, and analyze and predict social trends.