Upcoming DSA Courses
See what upcoming courses are being offered in Spring 2023.
DSC Spring 2023 Courses
Biomedical Data Sharing
Course/Section: DSC 495-604
Day/Time: Tuesday, 6:00pm-6:50pm
Students will learn to articulate the benefits and potential risks of biomedical data sharing and explore the evolution of data sharing policy, from initial conception to current and future considerations. Students will discuss FAIR principles of data sharing and the technical and ethical barriers to sharing present in many existing data sets, examine how private information (such as personal identifying information (PII) and personal health information (PHI)) are safeguarded in data sharing; and learn how data are collected, deposited, and how access to those sets is managed in federal repositories. Students will discuss the differences in domestic versus foreign data sharing policies and impacts on research and international collaboration. Students will investigate the role of the private sector in data sharing from research, discovery, and legal perspectives. Final project puts ideas into practice.
Course Delivery: Online
Prerequisites:
Data Communication
Course/Section: DSC 295-004
Day/Time: Thursday, 11:45am-12:35pm
Employers want to hire people who can communicate to a wide variety of audiences. Many disciplines have their own specialized language or jargon. It is easy to fall into the habit of using that jargon to communicate data analysis results or concepts to the public, or to “cherry-pick” and selectively present data or results to convey a specific idea. Students will fine-tune their awareness regarding the use of discipline-specific language. Students will present scientific results and concepts clearly, unambiguously, and with a minimum of jargon.
Course Delivery: In Person
Prerequisites: None needed
Data in Motion
Course/Section: DSC 295-301
Day/Time: Tuesday, 11:45am-12:35pm
What kind of data interests you? How can you communicate it in unexpected ways? To investigate these questions, students will communicate data and other information through movement. No prior dance experience is required. Students will collaborate with their peers – a mix of dancers and people interested in data, communication and moving. Students will both create movement and participate in movement created by others.
Course Delivery: Hybrid – Online and In Person
Prerequisites: None needed
Data Science for Cybersecurity
Course/Section: DSC 295-603
Day/Time: Tuesday, 6:00pm-6:50pm
In the past year, a cyberattack occurred once every 39 seconds on average. Students will investigate the use of data to discover, explore, and address relevant cybersecurity use cases. Students will become familiar with fundamental approaches to tackle common cybersecurity problems using Python. No prior programming experience required.
Course Delivery: Online
Prerequisites: None needed
Data Science for Social Good
Course/Section: DSC 295-601
Day/Time: Monday, 4:30pm-5:20pm
Data science offers powerful tools for addressing a multitude of societal challenges, yet it is no panacea and will require collaboration and commitment from across society to fulfill its promise. Students will investigate the growing use of data science in the social impact space, drawing from real-world examples aligned with the United Nation’s Sustainable Development Goals. These examples will span practice areas and approaches, including machine learning, natural language processing, and image recognition. Students will discuss the challenges of implementing data science for social good solutions, including considerations of community involvement, bias, and equity and identify best practices.
Course Delivery: Online
Prerequisites: None needed
Data Visualization
Course/Section: DSC 295-602
Day/Time: Tuesday, 11:45am-12:35pm
Visualizations can be one of the most effective means to communicate quantitative information. Students will apply the principles of effective visualization and interpret data displays. Students will evaluate current examples in the media and learn tools for creating static, interactive, and dynamic data displays. No programming experience needed.
Course Delivery: Online
Prerequisites: None needed
Data Wrangling and Web Scraping
Course/Section: DSC 495-016
Day/Time: Tuesday, 4:30pm-5:20pm
Finding, cleaning, and preparing data is often required prior to conducting any data analysis. These wrangling processes often account for the majority of the time spent working with data and learning these concepts is fundamental to the data science process. Students will learn how to manipulate and clean data for analyses and visualizations, read data from web pages, and merge multiple data sets of reasonable sizes. Requires knowledge of data structures, for and while loops, list comprehension, basic understanding of a programming language.
Course Delivery: In Person
Prerequisites: knowledge of data structures, for and while loops, list comprehension, basic understanding of a programming language
Epidemiology: BIG Data for Disease & Disparities
Course/Section: DSC 495-606
Day/Time: Thursday, 6:00pm-6:50pm
Epidemiology is a data driven field driven by the past, current and future history of a population to develop resilient public health and other public policies. Students will manipulate data sets to understand the unequal distribution of disease, illness, injuries, disability, and death within a population. Students will utilize data sources, data retrieval and data scrubbing; and will investigate causality and data interpretation using the basic quantitative analyses of epidemiology. Using these skills, students will create their own study of interest on topics like gun violence and climate change disparities.
Course Delivery: Online
Prerequisites: basic understanding of statistical concepts, basic understanding of Microsoft Excel
Exploratory Data Analysis for Big Data
Course/Section: DSC 495-017
Day/Time: Thursday, 4:30pm-5:20pm
Exploratory data analysis (EDA) focuses on summarizing the main characteristics of a dataset, often using visualization methods. The goal is not formal modeling or hypothesis testing, but understanding and exploring data to formulate hypotheses for further investigation. EDA techniques will be presented and students will apply these methods to large data sets. Requires knowledge of data structures, for and while loops, list comprehension, basic understanding of a programming language.
Course Delivery: In Person
Prerequisites: knowledge of data structures, for and while loops, list comprehension, basic understanding of a programming language
Introduction to R/Python for Data Science
- Course: DSC 295
- Section: 003, Day/Time: Thursday, 10:40am-11:30am
- Section: 002, Day/Time: Thursday, 3:00pm-3:50pm
- Section: 005, Day/Time: Friday, 10:40am-11:30am
- Section: 006, Day/Time: Friday, 11:45am-12:35pm
Students will develop introductory skills in R and Python needed for data science. Topics include data types, data structures, control structures, good coding practices, and reproducible coding. Students will become acquainted with basic data science algorithms and their implementations in R and Python. Skills acquired in this course serve as a foundation for many of the Data Science Academy classes that suggest some experience with R or Python. No prior programming experience required.
Course Delivery: All sections are In Person
Prerequisites: None needed
Machine Learning for Computer Vision
Course/Section: DSC 495-013
Day/Time: Tuesday, 3:00pm-3:50pm
Machine learning (ML) is the “field of study that gives computers the ability to learn without being explicitly programmed.” Students will deconstruct the fundamental ideas behind popular ML algorithms used in computer vision using a project-centered approach. Students will create projects from successful ML use cases in image recognition and learn how to deploy ML algorithms in embedded hardware platforms. Each week students will be encouraged to build and tailor the ML projects discussed to their specific domains of interest. Students should have basic knowledge of a programming language, such as appropriate use of data structures, such as lists and matrices, and flow control mechanisms, such as loops. Students should also be familiar with matrix-vector multiplication and the norm of a vector.
Course Delivery: In Person
Prerequisites: some programming experience, or familiarity with a programming language
R for Data Science and Visualization
Course/Section: DSC 295-001
Day/Time: Wednesday, 11:45am-12:35pm
A large part of data science is being able to manipulate the data you have for the analysis you wish to do. Students will format and clean data, extract relevant information from the data, and manipulate data for analysis. Students will also explore visualizations, how different types of visualizations convey different meanings, and how to pick the most accurate way to represent analyses that will interest the intended audience.
Course Delivery: In Person
Prerequisites: None needed
R for Social Sciences
Course/Section: DSC 295-604
Day/Time: Wednesday, 11:45am-12:35pm
Data analytics represents one of the most competitive and thriving fields within the social sciences. Employers from public, private, and nonprofit firms across the country need workers with expertise in this area. Working with data requires a data programming skill set and knowledge of statistics. Students will apply programming principles to basic statistical concepts and data analysis using R. Students will, read data, access packages, write functions, debug, and organizing code in the R programming language.
Course Delivery: Online
Prerequisites: None needed
Text Analytics Using Intermediate Python
Course/Section: DSC 595-601
Day/Time: Tuesday, 6:00pm-6:50pm
This course will provide a broad overview of text analysis and natural language processing (NLP), including a significant amount of introductory material but with extensions to state-of-the-art methods. All aspects of the text analysis pipeline will be covered including data preprocessing, converting text to numeric representations (from simple aggregation methods to more complex embeddings), and training supervised and unsupervised learning methods for standard text-based tasks such as named entity recognition (NER), sentiment analysis, and topic modeling. The course will alternate between presentation and hands-on exercises in Python. Translations from Python to R will be provided for students more comfortable in that language and students can create a project in the language of their choosing, however all illustrative examples will be in Python. Students should be familiar with Python (preferably), R, or both and have a basic understanding of statistics and/or machine learning concepts. In particular, students should have experience applying supervised and/or unsupervised methods, such as regression, classification, dimension reduction, and clustering, and understand how to assess model performance using appropriate metrics. Students will gain the practical skills necessary to begin using text analysis tools for their tasks, an understanding of the strengths and weaknesses of these tools, and an appreciation for the ethical considerations of using these tools in practice.
Course Delivery: Online
Prerequisites: TBD
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Data Internships for Social Good
Course/Section: DSC 495-006
Course/Section: DSC 495-601DE
This course is designed to prepare students who want to make a difference in the world for a data science internship. Students will focus on internships for social good in non-profits, governmental organizations, and community organizations. Students will utilize a data maturity tool for possible preparation for a data science internship. Explorations of these areas include specific emphasis on developing and refining interviewing skills, professional and personal networks, and job application and selection.
Prerequisites: some data science related skills required (such as Python, R, data visualization, Excel for business or government, etc.)