An introduction to computational thinking and quantitative reasoning, preparing students for further coursework (especially Foundations of Data Science, Data 8). Emphasizes the use of computation to gain insight about quantitative problems. Uses data from various domains in the social sciences in order to develop an understanding of the societal implications of data science. Expressions, data types, collections, and tables in Python. Programming practices, abstraction, and iteration. The data science lifecycle. Visualizing univariate and bivariate data with bar charts, histograms, plots, and maps. Introduction to statistical concepts including averages and distributions, prediction, causality, probability, sampling, and inference.

This foundational Data Science course combines inferential and computational thinking as applied to the fundamentals of social inquiry. After taking this course, students will be able to explain how data arise from social experiences and use it to ask and answer questions to better understand social phenomena from the past and present. They will also be able to identify different classes of social phenomena that are open to particular forms of quantitative analysis, and to appreciate the strengths and limits of these approaches. Students will apply critical concepts and skills in computer programming and quantitative interpretation to analyze real-world datasets in their appropriate human contexts, including economic processes and outcomes, public health, spatial phenomena, environmental justice, and social networks. The course also delves into social issues surrounding data analysis such as privacy and design. For example, students will be able to think critically about producing information on the distribution of pollution and the demographics of people who live in polluted areas. This course exposes students to core concepts and tools available in a Python environment that can be used to ask and answer questions about societies. Students will understand the process of using data for statistical analysis and how to develop a variety of figures, combined with text, to communicate their findings. The course can serve as a precursor to Data 8: Foundations of Data Science. The focus is on data exploration and identifying patterns relevant to human societies rather than inferences and predictions.

Credit Restrictions

Students will receive no credit for DATA 6 after completing DATA C8.

Repeat Rules

Course is not repeatable for credit.

Requirements class fulfills