Virginia Tech
Dr. Nizamani emphasizes hands-on, project-based learning with accessibility prioritized. Her teaching approach focuses on making complex concepts accessible to all students while preparing them for real-world challenges in computer science.
With 17+ years of experience across teaching, research, and academic service, Dr. Nizamani brings a wealth of knowledge to her courses, integrating cutting-edge research with foundational concepts.
Advanced data structures and analysis of data structure and algorithm performance. Sorting, searching, hashing, and advanced tree structures and algorithms. File system organization and access methods.
Prerequisites: CS 2114 (Software Design and Data Structures) with a grade of C or better
Credits: 3
This course develops problem-solving skills using computational thinking. Students learn to analyze problems, design solutions, and implement them effectively.
Credits: 3
Note: This course is part of the "LLM-Ready Graduates" research project, examining responsible integration of AI tools in CS education.
Basic principles and techniques in data analytics including data collection, data preprocessing, predictive modeling, descriptive modeling, and privacy and ethical issues.
Prerequisites: STAT 3006 or STAT 4105 or STAT 4705 or CMDA 2006 and (CS 1064 or CS 1114)
Credits: 3
Overview of database management, database system architecture, and database modeling principles. Logical database design. The relational data model, relational algebra and SQL. Storage and indexing.
Prerequisites: CS 3114 with a grade of C or better
Credits: 3
Note: This course uses DBWorkout, a gamified SQL practice tool developed by the HICE Lab.