An introduction to data science and data science life cycle focusing on the exploratory data analysis (EDA) phase. Datasets used for the EDA will be drawn from social justice issues.  Topics include introduction to data science and its life cycle, missing data and how to address them, biases in data collection and training, data pre-processing, learning to ask questions of the data, data visualization through explorations with no-code tools (for example, spreadsheets, CODAP, etc.), discovering patterns and anomalies in the data, composing stories told by the data through simple hypothesis testing and simple regression modeling, and writing reproducible reports. 

An introduction to computing tools needed to do data science. This course assumes no prior knowledge of such tools. Using tabular datasets of social justice issues, topics will cover, information about the tools, their interfaces, and using the tools for data pre-processing and cleaning, data wrangling (i.e., transforming to “tidy” data to make it suitable for analysis), data visualization, discovering patterns and anomalies in the data, composing stories told by the data through simple hypothesis testing and simple regression modeling, and writing reproducible reports. 

This course introduces the fundamentals of machine learning (ML) with a focus on applications in social sciences. Students will learn key concepts, methods, and tools for analyzing and interpreting data, enabling them to apply machine learning techniques to real-world social science problems. Key ML algorithms like learned decision trees, logistic regression, artificial neural networks will be introduced in this course. Datasets used for the ML instruction and practice will be drawn from social justice issues. Students will carry out ML experiments with real datasets. The hands-on experiments will use low code software like Orange Data Mining, Weka, and Blockly-DS to increase student understanding of the typical elements of the ML workflow. Equipped with this understanding, the concepts of the Python programming language and supporting tools like programming notebooks and ML libraries in Python will be introduced so students are exposed to how ML software is developed in the industry. (Students pursuing BS in Mathematics cannot use this course to satisfy major requirements.)

Under development.

Under development.