Upcoming Courses

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

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.

Prerequisites: none

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.

Prerequisites: none

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 Nations’ 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, & equity and identify best practices.

Prerequisites: none

The vast majority of individuals in the U.S. have been exposed to cybersecurity threats [cisa.gov]. Personal and economic effects can be devastating. 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.

Prerequisites: none

An introduction to principles of communicating data through dance. Students will break down data from research or an area of interest to focus on the “why” and “so what”, and communicate their findings to an audience through movement and choreographic devices. Students will create and perform a duet or small group movement study based on their data investigation of interest.

Prerequisites: none

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.

Prerequisites: none

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 introduction to the primary tools used to visualize and analyze network data. The course covers network measurement, community detection, and simulation techniques.

Prerequisites: none

A data Physicalization (or simply Physicalization) is a physical artifact whose materiality encodes data. Data Physicalization engages its audience and communicates data using tangible data representations. This course covers topics such as visualization aesthetics, the data-object, data sculptures, critical making, and wearable/art technology. Students will analyze current examples of data Physicalization, discuss visualization in the context of cultural and historical practice, and evaluate scholarship that recognizes intersections among Physicalization, record-keeping, and data literacy.

Prerequisites: none

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.

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

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.

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

Machine learning [ML] is a fundamental component of artificial intelligence. Students will deconstruct the basic 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 tailored to their specific domains of interest.

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.

In this course, students will be introduced to climate data and techniques for analysis and visualization. Students will be equipped with data science best practices, as well as learn how to work with geospatial data. Throughout the course students will work on a team project of their own choosing in which the skills gained in class, combined with their own expertise, will be applied to the project. This course is suitable for all disciplines from business to society.

Prerequisites: Basic understanding of the scientific method, common statistical distributions, and experiences with a programming language such as R or Python. Proficiency in these areas is not required but highly suggested to better engage with the course material.

Students will learn the foundations of the NIST cybersecurity framework (CSF) and AI’s potential impact to each domain. Additionally, students will identify a challenge in AI and cybersecurity, and develop a practical solution and business plan to address that challenge using an entrepreneurial lens. Course topics include cybersecurity measures, design thinking, market analysis, and prototype development.

Prerequisites: Students should have basic/introductory familiarity with data science methodologies applied to cybersecurity such as classification techniques and familiarity with introductory AI concepts such as automation.

Data management has been a critical and common practice employed across industries for many years. Data management incorporates all fields related to managing data as a strategic and valuable resource, including collecting, processing, governing, and analyzing big data. This course is designed to give students the fundamentals of Database management systems including data modeling, relational database concepts, logical and physical database design. Students learn and apply hand-skills for both Structured Query Language, which is a programming language (SQL) and not only SQL (NoSQL). SQL interacts with relational databases, while NoSQL interacts with non-relational databases. Students will also be exposed to core concepts associated with data warehousing and business intelligence.

Prerequisites: Some experience using databases or database driven applications along with knowledge of programming is preferred but not required.

An introduction to the R programming environment geared towards the biological sciences. Topics include installation & software setup, programming, and data exploration & analysis, with heavy focus on data visualization [graphics] within R. Students will learn analyses with a focus on shareability and reproducibility within the context of a project or mini projects. Students may bring their own data and use case datasets will be available as well.

Prerequisites: Familiarity with biology and biological research.

This course offers a hands-on experience with large language models from OpenAI and the Hugging Face library, including models like Llama3 and Mistral. Students will explore various tools and engage in numerous small projects to gain first-hand knowledge and experience in AI and LLMs. By the end of the course, students will have practical skills in building, deploying, and fine-tuning large language models using their own data. 

Prerequisites: Students should have a foundational understanding of a programming language, such as Python or MATLAB. This includes familiarity with basic data types [such as strings, integers, and floats], data structures [such as lists and matrices], and data visualization techniques. While proficiency in these areas is not mandatory, it is recommended to enhance engagement and comprehension of the course material. Additionally, participants are required to have access to a laptop equipped with either a Google account, Google drive, or Python [version 3.71 or newer] pre-installed. This setup will be essential for completing course assignments and participating in interactive components