Data Science

TBD

Students will delve into the current landscape of data science, exploring its theories, tools, and practical applications. This course aims to equip students with a comprehensive understanding of data science and empower them to apply its skills effectively in comprehending the complexities of our world. Through the art of storytelling, data visualization, data cleaning, statistical analysis, and the development of practical products and tools, students will gain valuable insights derived from the practical application of data science. By the end of this course, students will possess the knowledge and proficiency to unravel meaningful patterns and trends, and to derive valuable insights from data to inform decision-making.

This course is designed to help students think explicitly about their social responsibility as data scientists and the impact on the world of what they are building and analyzing. Using contemporary case studies from recent news stories and legal cases, students will learn about issues such as intellectual copyright, consent, data security, differences between privacy and confidentiality, difficulties of anonymization, and bias in artificial intelligence. In addition, students will be engaged with fundamental questions of justice in relation to data and computing in American society.

In this course students will develop an understanding of statistics as a research tool. Students are expected to have some base knowledge of statistics from a prior course. Emphasis will be made on understanding statistical concepts and applying and interpreting tests of statistical inference for real life application. Content will include but not be limited to: visual representations of data, descriptive statistics, correlation and simple regression, sampling distributions, and the assumptions associated with and the application of selected inferential statistical procedures. Throughout the course there will be a strong emphasis on how statistical modeling can be a driving force for Social Justice.

In this course students will cultivate the ability to think critically about telling stories related to social justice questions that can be answered using data. Topics include urban food insecurity, minority health disparity related to COVID-19, policing, and community safety.  Students will learn the principles behind effective data visualization and how to implement them in real analyses using Tableau software.

This course provides a broad understanding of the principles underlying data engineering and management. With the introduction of SQL, this course aims to help students get started with developing their own datasets through harvesting, processing, aggregation, analysis, and visualization to make data-driven decision.  Working with a repository of public use data, students will discuss real case studies focused on specializations in the areas of Minority Health & Health Disparities, Environmental Justice, and Economic Empowerment. This course will utilize Python and SQL. Real datasets are used in comprehensive projects.

The main aim of the course is to provide skills to apply machine learning algorithms on real applications. Students will gain experience using several supervised (classification) and unsupervised (clustering) techniques for creating predictive models. These include Bayesian methods, decision-tree methods, kernel-based methods and neural networks methods, as well as clustering and dimensionality reduction.

 This course will introduce students to how to apply data science skills to social issues. This course is broken down into two phases – (1) developing the critical lens to ask the right questions data and technology can answer, and (2) learning the concepts, techniques, and tools needed to deal with facets of applying data science in practice. Case studies topics include Black wealth equity, emphasizing reparations, historical patterns of Black economic growth, housing and property ownership, youth and adult employment. Real datasets are used in comprehensive projects.

The Capstone Project provides an opportunity for students to engage in high-level inquiry focusing on an area of specialization within data science. Students will complete a Capstone Projects.

The Applied Data Science and Analytics Practicum /Internship course is an opportunity for students to apply principles, tools, theories, ideas, and skills learned in the classroom to a real-world problem. Student can engage in an (1) External Internship with an organization or government agency, (2) Internal Case Study Internship provided by the department, or (3) Internal Practicum research project to exercise their skills in Data Science and Data Analytics, and understand the culture around computing and management systems, operations, resources within a specialty area. The experience is obtained with organizations approved by the Center for Applied Data Science and Analytics Program under the guidance of faculty.

Data collection, visualization, and analysis have been entangled in the struggle for racial and social justice because they can make injustice visible, imaginable, and thus actionable. With a focus on environmental justice, along with critical social science literature, students will gain a historical understanding of how minority and marginalized communities face bias and discrimination regarding environmental data, create maps and data visualizations using QGIS, Arch GIS and Tableau, identify how gaps in data can have short-and long-term effects on communities and how these gaps can be exacerbated when combined with social inequalities.

Geographic Information Systems (GIS) are tools for managing, describing, analyzing, and presenting information. The course seeks to provide students with a basic level of familiarity with several aspects of Geographic Information Systems and Geographic Information Science.

This course will equip students on how to convert text to formats suitable for data analysis. Students will be introduced to data measurement tools, supervised learning approaches for document classification, vector representations, contextualized embeddings, pre-processing and natural language processing skills.

This course is an exploration of mapping and geospatial analyses related tools that can be used to compile and visualize data. Student will join the process of mapping and exploring spaces with a study of Black history, culture, and storytelling.

• Social Science Research Council
• data.org
• Johns Hopkins Data Science Lap
• Distributed AI Research Institute (DAIR)
• ACLU
• Brookings
• AIM AHEAD
• Black in AI

• Esri
• Accenture
• Amazon
• Google
• Mastercard
• AstraZeneca

• Prison Cells to PhD program
• NorthStar GIS
• National Black Justice Coalition
• Martha's Kitchen
• Arlington School District
• Black Wealth Data Center
• Data for Black Lives