An introduction to data science focusing on the exploratory data analysis (EDA) phase. Topics include an introduction to data science and its life cycle, missing data, biases in data, data pre-processing, no-code data visualization, discovering patterns and anomalies in the data, simple hypothesis testing and simple regression modeling, and writing reproducible reports.

This course is an introduction to computing tools needed to do data science. This course assumes no prior knowledge of such tools. Using tabular datasets, 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. Students will carry out ML experiments with real datasets. The hands-on experiments will use low code software like Orange Data Mining, Weka, KNIME 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.)

An introduction to the issues of ethics and responsibility in data science. Topics include Value Sensitive Design (a theoretical framework for technology design incorporating human values), ethical considerations at each stage of the data science lifecycle - data collection, data preprocessing, modeling, testing and validation, and deployment - and the resulting responsibility of the data science practitioner, issues related to algorithmic fairness, transparency and interpretability of models and systems, and data privacy and protection.

This course provides the capstone experience for students pursuing the Data Science Certificate for Non-Computing Majors. Students are required to implement and document a data-enabled project within the context of their major discipline. The course requires students to apply their data science and data analytic skills and domain knowledge to analyze and visualize current real-world issues from their major discipline. This course requires the submission of a written reproducible report and an oral presentation of the project in an open forum. The selection of projects must be approved by the Coordinator of the Data Science Certificate for Non-Computing Majors and the supervising discipline faculty.