Data Sciences

This course covers the mathematical elements of computer science including formal logic, propositional logic, predicate logic, logic in mathematics, sets, functions and relations, recursive thinking, mathematical induction, counting, combinatorics, algorithms, matrices, graphs, trees, and Boolean logic. Students will learn to recognize and express mathematical ideas graphically, numerically, symbolically, and in writing.

This course covers the mathematical elements of computer science including formal logic, propositional logic, predicate logic, logic in mathematics, sets, functions and relations, recursive thinking, mathematical induction, counting, combinatorics, algorithms, matrices, graphs, trees, and Boolean logic. Students will learn to recognize and express mathematical ideas graphically, numerically, symbolically, and in writing.

The course is designed to teach students the basics of probability and statistics as used in engineering and the sciences. The course covers introduction to probability theory, random variables, statistics, and regression.

This course delves into the critical preprocessing steps required to convert raw data into meaningful formats for analysis. Students will learn techniques for handling missing data, detecting outliers, scaling features, and encoding categorical variables. The course also emphasizes feature engineering and selection strategies to improve the performance of machine learning models. Through practical exercise.

This course delves into the critical preprocessing steps required to convert raw data into meaningful formats for analysis. Students will learn techniques for handling missing data, detecting outliers, scaling features, and encoding categorical variables. The course also emphasizes feature engineering and selection strategies to improve the performance of machine learning models. Through practical exercise. 

Data mining focuses on extracting meaningful patterns and knowledge from large datasets. This course covers foundational data mining techniques such as association rule mining, clustering, and classification. Emphasis is placed on understanding the theoretical concepts behind these methods while applying them to practical scenarios. Students will explore tools and libraries used for data mining tasks and tackle projects that simulate real-world applications. 

Big data is transforming industries by enabling the analysis of massive datasets. This course focuses on the architecture, tools, and methodologies used in big data analytics and data warehousing. Students will learn about distributed systems like Hadoop and Spark, as well as the principles of data warehousing design and implementation. 

This course explores the role of cloud computing in data science, including scalable data storage, distributed computing, and cloud-based machine learning. Students will gain hands-on experience with cloud platforms like AWS and Google Cloud to execute data science workflows efficiently. 

Social networks represent complex relationships and interactions. This course introduces students to methods for analysing social networks, including graph theory, community detection, and influence propagation. Applications in marketing, public health, and communication studies are highlighted.

An internship is an important aspect of the DSE curriculum that provides the student with hands-on experience and a good sense of what an actual job in an organization will be like. Students are required to join an IT department in a government or private organization for a summer period of at least 8 weeks in the last summer prior to student graduation. Students should be able to relate the internship experience to the knowledge that he or she has gained through the DSE program courses.  

This course delves into advanced optimization methods used in machine learning, such as convex optimization, stochastic gradient descent, and optimization under constraints. Students will apply these techniques to improve machine learning model performance, focusing on real-world challenges in tuning and scalability. 

This course introduces students to expert systems in general and to rule-based systems in specific. Students learn how to build a rule-based expert system in a variety of application areas. They also learn advanced programming techniques which include topics of inexact reasoning, intelligent database management methods, and how to develop a community of expert systems. Students are also given the opportunity to demonstrate their understanding of the technology by building a rule-based expert system that addresses a real-world problem. 

This course focuses on designing and implementing robust data pipelines to automate the flow of data from diverse sources. Students will learn about ETL (Extract, Transform, Load) processes, real-time data streaming, and frameworks like Apache Airflow and Kafka. Practical projects simulate building scalable and efficient pipelines for enterprise-level applications. 

This course introduces students to expert systems in general and to rule-based systems in specific. Students learn how to build a rule-based expert system in a variety of application areas. They also learn advanced programming techniques which include topics of inexact reasoning, intelligent database management methods, and how to develop a community of expert systems. Students are also given the opportunity to demonstrate their understanding of the technology by building a rule-based expert system that addresses a real-world problem. 

This course focuses on the principles and applications of generative AI, including Generative Adversarial Networks (GANs) and Large Language Models (LLMs). Students will explore cutting edge techniques in generating synthetic data, text, and images. Applications in creative industries and ethical considerations are also discussed. 

Building on the groundwork laid in CSE 495, this course focuses on implementing and completing the capstone project. Students will execute their proposed solutions. Teams will utilize industry standard tools and techniques to develop a functional prototype or system. The course culminates with a comprehensive project report and a formal presentation to faculty and/or industry stakeholders, demonstrating the ability to tackle complex, real-world problems with data-driven strategies. Emphasis is placed on teamwork, project management, and effective communication of findings.