Upcoming DSA Courses

Course/Section: DSC 595-001

Date/Time: Tuesday, 3 p.m. to 3:50 p.m.

Social network analysis (SNA) refers to the study of connections among and between social units: people, events, organizations, communities, and other groups. This course provides an overview of the primary statistical tools used to analyze network data. This includes the estimation of various network measurements, the identification of cohesive subgroups, and the application of various inferential statistics to network data.

Course Delivery: In Person

Prerequisites: Students should have a basic understanding of probability and statistical inference. Experience working with the R programming environment is beneficial, though not required in advance of the course.

Course/Section: DSC 295-006

Date/Time: Thursday, 11:45 a.m. to 12:35 p.m.

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

Course/Section: DSC 410-001

Date/Time: Wednesday, 10:40 a.m. to 11:30 a.m.

Students will prepare to apply for internships for social impact in nonprofits, governmental organizations, and community organizations. As part of this preparation, students will become familiar with tools (such as a data maturity questionnaire) that can help organizations assess their own use of data, and use assessment results to initiate conversations about the organization’s data practices and goals. Students will learn about the appropriate scope of projects for an internship, and practice some basic data management, analysis, and visualization through a mini-project utilizing data from real organizations with a focus on social impact. Additional emphases include developing and refining interviewing skills, professional and personal networks, job applications, and job selection.

Course Delivery: In Person

Prerequisites: Some elementary data science experience that could be applied in an internship.

Course/Section: DSC 295-604

Date/Time: Tuesday, 6 p.m. to 6:50 p.m.

Data drives decision making. It can be the difference between success and failure for our national security agencies. Our adversaries are collecting data and using that data to influence public debate, target vulnerabilities in our social and physical infrastructure, and to gain competitive advantage in contested environments. For our men and women serving our national security needs, the ability to triage data sources, secure that data, analyze it, and produce actionable products that fuel good decision making, data literacy is an essential skill. Data Literacy means knowing and understanding what data is, why it’s important, how to use it, and how to present it in a way that is useful to decision makers. This project-driven, hands-on course will help students know and understand the core competencies of Data Literacy and start them on their journey to support our national security in a data-driven, probabilistic world.

Course Delivery: Online

Prerequisites: None

Course/Section: DSC 295-602

Date/Time: Monday, 6 p.m. to 6:50 p.m.

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

Course/Section: DSC 295-612

Date/Time: Monday, 4:30 p.m. to 5:20 p.m.

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

Course/Section: DSC 405-001

Date/Time: Thursday, 4:30 p.m. to 5:20 p.m.

Finding, cleaning, and preparing data is often required prior to conducting any data analysis. Data wrangling often accounts 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.

Course Delivery: In person

Prerequisites: Students should enter the course with basic knowledge of a programming language (such as R or Python).

Course/Section: DSC 495-607

Date/Time: Tuesday, 4:30 p.m. to 5:20 p.m.

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 406-601

Date/Time: Wednesday, 4:30 p.m. to 5:20 p.m.

Exploratory data analysis (EDA) focuses on summarizing the main characteristics of data sets, often using visualization methods. The goal is not formal modeling or hypothesis testing, but understanding and exploring data to formulate hypotheses for further investigation. Students will use techniques of EDA and generalize those approaches to large data sets.

Course Delivery: Online

Prerequisites: Students should enter the course with some basic programming experience, such as experience using and familiarity with R or Python.

Course/Section: DSC 412-601

Date/Time: TBD

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, such as logistic regression or K-means, using a projects-centered approach. Students will create projects from successful ML use cases. Each week students will be encouraged to build ML projects tailored to their specific domains of interest.

Course Delivery: Online

Prerequisites: Students should have basic knowledge of a programming language (e.g., R, Python, or others), experience with appropriate use of data structures (e.g., 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/Section: DSC 295-008

Date/Time: Tuesday and Thursday, 10:40 a.m. to 11:30 a.m.

A fusion of aerobics, strength, and flexibility with data analysis. Students will participate in health and fitness activities and collect data on their physiological responses to assess their progress throughout the semester. Parameters include 20 measures such as urinalysis test results, training heart rate, recovery heart rate, temperature, blood pressure, components of the Functional Movement Screening Assessments, site body fat indicators, and more. This data will be collected at regular intervals throughout the semester and used in a project to investigate health trends and communicate the data informed stories of students’ fitness progress.

Course Delivery: In Person

Prerequisites: None

Course/Section: DSC 495-013

Date/Time: Wednesday, 1:55 p.m. to 2:45 p.m.

Students will be introduced to a range of techniques from computational statistics frequently encountered in big data and machine learning. Through case studies students will learn to identify sampling biases commonly encountered in real world data sets. Students will then explore how to overcome these data deficits to extract reliable data inferences through permutation testing, nonparametric imputation, and parametric data simulation and biases correction techniques. Course materials will emphasize a conceptual understanding of these topics. Course content will be delivered in R.

Course Delivery: In Person

Prerequisites: Students should have basic coding proficiency in loops, conditional statements, and matrix data structures.

Course/Section: DSC 202-601

Date/Time: Wednesday, 11:45 p.m. to 12:35 p.m.

Visualizations can be one of the most effective means to communicate quantitative information. Students will cover the principles of effective visualization and how to interpret data displays. Students will evaluate current examples in the media and learn tools for creating static, interactive, and dynamic data displays.

Course Delivery: Online

Prerequisites: None

Course/Section: DSC 201

• DSC 201-001, Wednesday, 9:25 a.m. to 10:25 a.m., In Person
• DSC 201-002, Thursday, 12:50 p.m. to 1:40 p.m., In Person
• DSC 201-003, Tuesday, 11:45 a.m. to 12:35 p.m., In Person
• DSC 201-601, Thursday, 6 p.m. to 6:50 p.m., Online

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 techniques 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.

Prerequisites: None

Course/Section: DSC 295-007

Date/Time: Tuesday, 12:50 p.m. to 1:40 p.m.

Thinking critically about data – where it comes from, how it is interpreted and communicated – is a key skill in virtually every discipline. Students will explore and visualize data using JMP software, with an emphasis on contextualized problem solving using appropriate methodology. Students will investigate the scope of a project and the limits of different approaches in order to effectively convey information to others. Students will apply this knowledge through an individual project using data from an area of interest.

Course Delivery: In Person

Prerequisites: None

Course/Section: DSC 295-009

Date/Time: TBD

KNIME is a Graphical User Interface (GUI)-based workflow platform used for creating Data Science applications and services. KNIME offers easy-to-use drag-and-drop, pre-built Data Processing/Machine Learning modules that do not require writing code. This course is designed for those who are just getting started on their data science journey. Students start with a detailed introduction of the KNIME Analytics Platform, including downloading the software and navigating the workbench. Students will then cover the whole data science cycle from data import, manipulation, and aggregation, to visualization, model training, and deployment using the KNIME Analytics Platform.

Course Delivery: In Person

Prerequisites: None

Course/Section: DSC 495-001

Date/Time: Tuesday, 1:55 p.m. to 2:45 p.m.

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: 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/Section: DSC 495-014

Date/Time: Monday, 12:50 p.m. to 1:40 p.m.

An introduction to network science, including the foundational concepts, the type of data, network structures and algorithms for analysis and optimization. Topics include network algorithms to identify subgraphs like connected components, bi-connected components, communities, cores, reach networks, projections, nodes similarity and pattern match. Students will investigate algorithms to compute network centralities, such as degree, influence, clustering coefficient, closeness, betweenness, eigenvector, PageRank, hub, and authority. Finally, students will consider network optimization algorithms as cycle, linear assignment, minimum-cost network flow, maximum network flow problem, minimum cut, minimum spanning tree, path, shortest path, transitive closure, traveling salesman problem, vehicle routing problem, and topological sort. The course uses a series of case studies to explain the practical outcomes of network algorithms when solving complex problems.

Course Delivery: In Person

Prerequisites: Familiarity with a programming language and knowledge of data structures.

Course/Section: DSC 495

Data/Time: Thursday, 3 p.m. to 3:50 p.m.

For-profit companies, nonprofit service providers and government agencies often use predictive analytics to improve their services. Predictive analytics harnesses historical data and may incorporate machine learning to model future outcomes. Students will explore the value, limitations and ethical considerations of predictive analytics when used to improve services. Using their own dataset or one provided by the instructor, students will learn and apply a practical approach for planning, implementing and assessing a predictive analytics project. The instruction will highlight topics related to data preparation, model training and selection, validation, fairness, transparency and communication of results.

Course Delivery: Online

Prerequisites: Students should have familiarity with R Studio and some experience using R to manipulate data and run basic descriptive statistics.

Course/Section: DSC 295-005

Date/Time: Wednesday, 6 p.m. to 6:50 p.m.

An introduction to network science, including the foundational concepts, the type of data, network structures and algorithms for analysis and optimization. Topics include network algorithms to identify subgraphs like connected components, bi-connected components, communities, cores, reach networks, projections, nodes similarity and pattern match. Students will investigate algorithms to compute network centralities, such as degree, influence, clustering coefficient, closeness, betweenness, eigenvector, PageRank, hub, and authority. Finally, students will consider network optimization algorithms as cycle, linear assignment, minimum-cost network flow, maximum network flow problem, minimum cut, minimum spanning tree, path, shortest path, transitive closure, traveling salesman problem, vehicle routing problem, and topological sort. The course uses a series of case studies to explain the practical outcomes of network algorithms when solving complex problems.

Course Delivery: In Person

Prerequisites: None

Course/Section: DSC 495-020

Date/Time: Monday, 10:40 a.m. to 11:30 a.m.

There are many layers to consider when trying to guarantee reproducibility of a given program (can you still run a program you wrote years ago without errors?). Package managers (apt-get, pip, etc.) solve part of that problem, but the next step is making a portable recreation of (part of) the OS environment. Docker images package software and their dependencies into just such portable containers. Containerization and container orchestration have become critical tools in software engineering, including machine learning engineering, and are heavily used in industry. Students will learn the basics of containerization and gain experience running containers locally and in the cloud.

Course Delivery: In Person

Prerequisites: Familiarity with the Linux command line and bash scripting.

Course/Section: DSC 495-600

Date/Time: TBD

Interested in using data science to study written information? This course will introduce students to a data science technique called topic modeling that is used in many different contexts to analyze data that is composed of words. Students will learn to preprocess words and texts to prepare them for machine learning models by turning words into numbers that an algorithm can take in and process. Then students will learn to use models that can help you decide how many clusters are appropriate for a particular dataset. Students will characterize clusters by identifying the main intents (actions) in each cluster and the most common entities (words) in that cluster. Finally students will use models to make decisions about how to label each cluster.

Course Delivery: Online

Prerequisites: Basic familiarity with Python.