Upcoming DSA Courses

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:

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

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

Course/Section: DSC 495-006

• Day/Time: Wednesday, 10:40am-11:30am
• Course Delivery: In Person

Course/Section: DSC 495-601DE

• Day/Time: N/A
• Course Delivery: Asynchronous

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.)

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

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

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

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

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

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

• 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

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

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

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

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