Teaching & Supervision

Principles and tools for visually representing complex data, enabling effective communication of insights and support for decision-making.

In-depth study of advanced machine learning algorithms, deep learning architectures, ensemble methods, and their application to complex, real-world problems.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

In-depth study of advanced machine learning algorithms, deep learning architectures, ensemble methods, and their application to complex, real-world problems.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

Advanced techniques in NLP including transformers, BERT, GPT, and latest state-of-the-art models for language understanding and generation.

Cutting-edge AI topics such as reinforcement learning, generative models, multi-agent systems, and ethical considerations in AI.

End-to-end data science methodology, from data acquisition and cleaning to statistical modeling, machine learning, and actionable insights.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

End-to-end data science methodology, from data acquisition and cleaning to statistical modeling, machine learning, and actionable insights.

Cutting-edge AI topics such as reinforcement learning, generative models, multi-agent systems, and ethical considerations in AI.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Foundational concepts of data science, including exploratory data analysis, statistics, and the data science lifecycle.

Foundational concepts of data science, including exploratory data analysis, statistics, and the data science lifecycle.

Advanced study of concepts in advanced ml and their applications in computer science and engineering.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Hands-on project work integrating knowledge from the curriculum to design, build, and evaluate a significant software or research artifact.

Fundamental concepts of machine learning, covering supervised and unsupervised learning, regression, classification, and basic evaluation metrics.

Fundamental concepts of machine learning, covering supervised and unsupervised learning, regression, classification, and basic evaluation metrics.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

End-to-end data science methodology, from data acquisition and cleaning to statistical modeling, machine learning, and actionable insights.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

End-to-end data science methodology, from data acquisition and cleaning to statistical modeling, machine learning, and actionable insights.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Practical application of data science techniques to solve real-world problems, involving data cleaning, analysis, and visualization.

Principles and tools for visually representing complex data, enabling effective communication of insights and support for decision-making.

Focused study on deep neural networks, including CNNs, RNNs, and modern architectures used in computer vision and sequential data analysis.

Focused study on deep neural networks, including CNNs, RNNs, and modern architectures used in computer vision and sequential data analysis.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Practical application of data science techniques to solve real-world problems, involving data cleaning, analysis, and visualization.

Advanced study of concepts in advanced ml and their applications in computer science and engineering.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Design and implementation of database systems, covering SQL, normalization, data modeling, and relational database theory.

Design and implementation of database systems, covering SQL, normalization, data modeling, and relational database theory.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Advanced study of concepts in distributed systems and their applications in computer science and engineering.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

Fundamental concepts of machine learning, covering supervised and unsupervised learning, regression, classification, and basic evaluation metrics.

In-depth study of advanced machine learning algorithms, deep learning architectures, ensemble methods, and their application to complex, real-world problems.

In-depth study of advanced machine learning algorithms, deep learning architectures, ensemble methods, and their application to complex, real-world problems.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Fundamental concepts of machine learning, covering supervised and unsupervised learning, regression, classification, and basic evaluation metrics.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Cutting-edge AI topics such as reinforcement learning, generative models, multi-agent systems, and ethical considerations in AI.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

Advanced techniques in NLP including transformers, BERT, GPT, and latest state-of-the-art models for language understanding and generation.

Foundational concepts of data science, including exploratory data analysis, statistics, and the data science lifecycle.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

End-to-end data science methodology, from data acquisition and cleaning to statistical modeling, machine learning, and actionable insights.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Advanced study of concepts in advanced algorithms and their applications in computer science and engineering.

End-to-end data science methodology, from data acquisition and cleaning to statistical modeling, machine learning, and actionable insights.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Principles and tools for visually representing complex data, enabling effective communication of insights and support for decision-making.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Advanced study of concepts in advanced artificial inteligence and their applications in computer science and engineering.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Foundational concepts of data science, including exploratory data analysis, statistics, and the data science lifecycle.

Core programming concepts, object-oriented design principles, classes, inheritance, polymorphism, and software design patterns.

Practical application of data science techniques to solve real-world problems, involving data cleaning, analysis, and visualization.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Focused study on deep neural networks, including CNNs, RNNs, and modern architectures used in computer vision and sequential data analysis.

Principles and tools for visually representing complex data, enabling effective communication of insights and support for decision-making.

Broad introduction to artificial intelligence, including search algorithms, knowledge representation, reasoning, and intelligent agents.

Broad introduction to artificial intelligence, including search algorithms, knowledge representation, reasoning, and intelligent agents.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

Focused study on deep neural networks, including CNNs, RNNs, and modern architectures used in computer vision and sequential data analysis.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Foundational concepts of data science, including exploratory data analysis, statistics, and the data science lifecycle.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Cutting-edge AI topics such as reinforcement learning, generative models, multi-agent systems, and ethical considerations in AI.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Foundational concepts of data science, including exploratory data analysis, statistics, and the data science lifecycle.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

End-to-end data science methodology, from data acquisition and cleaning to statistical modeling, machine learning, and actionable insights.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Principles and tools for visually representing complex data, enabling effective communication of insights and support for decision-making.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Practical application of data science techniques to solve real-world problems, involving data cleaning, analysis, and visualization.

Hands-on project work integrating knowledge from the curriculum to design, build, and evaluate a significant software or research artifact.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Hands-on project work integrating knowledge from the curriculum to design, build, and evaluate a significant software or research artifact.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

End-to-end data science methodology, from data acquisition and cleaning to statistical modeling, machine learning, and actionable insights.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Strategies for managing enterprise information assets, business intelligence systems, data governance, and aligning IT with business goals.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

End-to-end data science methodology, from data acquisition and cleaning to statistical modeling, machine learning, and actionable insights.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Advanced study of concepts in distributed systems and their applications in computer science and engineering.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Theory and practice of search engines, indexing, ranking algorithms, and retrieving relevant information from large text collections.

Broad introduction to artificial intelligence, including search algorithms, knowledge representation, reasoning, and intelligent agents.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Fundamental concepts of machine learning, covering supervised and unsupervised learning, regression, classification, and basic evaluation metrics.

Development of essential academic and professional skills, including technical writing, research methodology, and effective presentation techniques.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Focused study on deep neural networks, including CNNs, RNNs, and modern architectures used in computer vision and sequential data analysis.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Practical application of data science techniques to solve real-world problems, involving data cleaning, analysis, and visualization.

Broad introduction to artificial intelligence, including search algorithms, knowledge representation, reasoning, and intelligent agents.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

Algorithms and techniques for recognizing patterns in data, covering statistical pattern recognition and machine learning applications.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Principles of the software development life cycle, including requirements engineering, architectural design, testing strategies, and project management.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

Core programming concepts, object-oriented design principles, classes, inheritance, polymorphism, and software design patterns.

Advanced techniques in NLP including transformers, BERT, GPT, and latest state-of-the-art models for language understanding and generation.

Focused study on deep neural networks, including CNNs, RNNs, and modern architectures used in computer vision and sequential data analysis.

Introduction to logic programming paradigms and languages like Prolog for solving symbolic computation problems.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Principles of the software development life cycle, including requirements engineering, architectural design, testing strategies, and project management.

Design principles of modern computer systems, covering instruction set architectures, processor design, pipelining, memory hierarchies, and I/O.

Study of concurrent execution, threads, synchronization, and parallel programming patterns for multi-core systems.

Core programming concepts, object-oriented design principles, classes, inheritance, polymorphism, and software design patterns.

Design principles of modern computer systems, covering instruction set architectures, processor design, pipelining, memory hierarchies, and I/O.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Broad introduction to artificial intelligence, including search algorithms, knowledge representation, reasoning, and intelligent agents.

Practical application of data science techniques to solve real-world problems, involving data cleaning, analysis, and visualization.

Statistical methods for data analysis, including probability theory, hypothesis testing, regression models, and inferential statistics.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Algorithms and techniques for recognizing patterns in data, covering statistical pattern recognition and machine learning applications.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

Design and implementation of database systems, covering SQL, normalization, data modeling, and relational database theory.

Capstone research project focusing on a specific problem, requiring literature review, methodology design, implementation, and thesis writing.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Principles of the software development life cycle, including requirements engineering, architectural design, testing strategies, and project management.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Foundational concepts of data science, including exploratory data analysis, statistics, and the data science lifecycle.

Comprehensive exploration of machine learning algorithms, neural networks, and their applications in data analysis and prediction.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Core programming concepts, object-oriented design principles, classes, inheritance, polymorphism, and software design patterns.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

Core programming concepts, object-oriented design principles, classes, inheritance, polymorphism, and software design patterns.

Principles of the software development life cycle, including requirements engineering, architectural design, testing strategies, and project management.

Algorithms and techniques for recognizing patterns in data, covering statistical pattern recognition and machine learning applications.

Core programming concepts, object-oriented design principles, classes, inheritance, polymorphism, and software design patterns.

Techniques for discovering patterns and relationships in large datasets, including clustering, association rules, and anomaly detection.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

In-depth study of abstract data types, arrays, linked lists, trees, graphs, sorting, and searching algorithms for efficient software design.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Design principles of modern computer systems, covering instruction set architectures, processor design, pipelining, memory hierarchies, and I/O.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Cutting-edge AI topics such as reinforcement learning, generative models, multi-agent systems, and ethical considerations in AI.

Fundamental concepts of machine learning, covering supervised and unsupervised learning, regression, classification, and basic evaluation metrics.

Broad introduction to artificial intelligence, including search algorithms, knowledge representation, reasoning, and intelligent agents.

Core programming concepts, object-oriented design principles, classes, inheritance, polymorphism, and software design patterns.

Design principles of modern computer systems, covering instruction set architectures, processor design, pipelining, memory hierarchies, and I/O.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Introduction to logic programming paradigms and languages like Prolog for solving symbolic computation problems.

Broad introduction to artificial intelligence, including search algorithms, knowledge representation, reasoning, and intelligent agents.

Study of computational methods for processing and understanding human language, covering syntax, semantics, and sentiment analysis.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Advanced database management concepts, including query optimization, distributed databases, transaction management, and NoSQL systems.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Design and implementation of database systems, covering SQL, normalization, data modeling, and relational database theory.

Core programming concepts, object-oriented design principles, classes, inheritance, polymorphism, and software design patterns.

Design principles of modern computer systems, covering instruction set architectures, processor design, pipelining, memory hierarchies, and I/O.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.

Scalable technologies and frameworks for processing, storing, and analyzing massive datasets, such as Hadoop, Spark, and distributed systems.