Graduate School of Arts and Sciences
Data Analytics

Courses

DATA 0200 Foundations of Data Analytics. This course will establish a unifying framework for how data should be used to answer and explore different questions.  It will provide an introduction into the ways that data are used and analyzed across the different disciplines within the arts, humanities, and sciences. The course will provide an understanding of the use and misuse of data, its analysis, and interpretation. (Fall)

DATA 0201 Introduction to Python and Machine Learning. This course introduces the Python programming language and environment for data analysis applications. It includes fundamentals of working with data, statistical methods, and visualization in Python as well as effective trouble-shooting using the Python ecosystem. (Fall)

DATA 0202 Database Design and SQL. Structured Query Language (SQL) is the universal language for interacting with relational databases. This course teaches you SQL, from the rudimentary to the advanced. Concurrently, students learn the art of database design. Upon completion, students should have the confidence to tackle any relational database from any vendor and architect databases to meet the needs of any application. (Spring)

DATA 0220 Communicating with Data. This course focuses on different approaches to the communication of data including visual, oral, and written methods. The focus is on how to create and design in each domain to best communicate information and ideas to a variety of audiences. The course includes a lab where students will learn how to effectively use the data visualization tool Tableau.  The students will apply the skills they learn in the class in giving oral presentations based on various case studies. (Spring)

DATA 0297 Special Topics: Introduction to Natural Language Processing.  NLP is now at the center of AI, data science and data analytics. There is a wealth of textual data online. NLP-enabled products constitute an essential part of everyday life, both in consumer facing products (Siri, Alexa, ChatGPT, Google Translate, etc.) and B2B applications (e.g., NLP for medical and legal domains). However, understanding human languages and extracting structured information from this plethora of unstructured text data is a major challenge for modern computers. The recent advancement in machine learning and deep learning makes NLP one of the fastest growing fields in AI and data science. In this course, we will survey ML-based NLP techniques from statistical ML approaches to the SOTA deep learning models. Students will be able to apply the ML and NLP skills learned in this course to real world problems in a variety of jobs (NLP/ML engineer, NLP data scientist, data scientist, data analyst, etc) and industries/sectors (finance, medical insurance, technology, urban planning, education, etc.). Prerequisites: proficient in python coding; familiarity with probability, linear algebra and calculus (Fall)

DATA 0297 Special Topics: Climate Change and Justice Analytics. In this course the students will complete a full analysis of data sets on the domain of climate change and justice using Python or R to create functional analytical tools, be it an Interactive Dashboard using Tableau/ PowerBI/Plotly and/or a Predictive Model. This is a hands-on course where students might need to collect some data and interact with government or local community sponsors. At the end of the course, students will have a portfolio with two to three projects to showcase in their GitHub/LinkedIn accounts. Prerequisites: proficient in Python or R coding, Tableau or Power BI (Fall)

DATA 0297 Special Topics: Applied Machine Learning with Multimodal Data. Artificial Intelligence (AI) employs a variety of modalities, including image, audio, and text to interact with the world around us. This course is designed to introduce students to the machine learning and deep learning techniques applied to data in multiple modalities. Students will gain a broad understanding of how these techniques are being applied to domain-specific problems ranging from computer vision to natural language processing to audio and music understanding. Students will gain hands-on experience implementing AI algorithms and building AI systems using popular ML tools and frameworks such as PyTorch and Tensorflow/Keras. The skills learned in this course will be valuable for career paths in various industries with a diverse product portfolio. Prerequisites: proficient in python coding; familiarity with probability, linear algebra and calculus (Spring)
 

DATA 0299 Capstone Internship. This course provides students with the opportunity to apply the knowledge and problem-solving skills they've learned over with course of the program with real-world projects or data sets within a professional context. Students must complete a minimum of 100 hours of a professional internship involving data analytics. This is envisioned to be accomplished during either the summer or academic year and is critical to the building of the problem-solving soft skills required by employers.