tamu comp sci degree plan Overview and Coursework

The Computer Science degree plan at Texas A&M is designed to provide students with a solid foundation in computer science and software engineering.

The degree plan requires a minimum of 128 semester hours of coursework, which typically takes four years to complete.

Students can choose from various concentrations, including algorithms, computer systems, and human-computer interaction.

The core curriculum includes courses in programming, data structures, computer organization, and software engineering.

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The total credit hours for the Bachelor of Science degree in Computer Science at TAMU is 120-124 hours. This is divided among the University Core Curriculum, Major Curriculum, and Electives.

The University Core Curriculum requires 42 credit hours. This includes courses like UNIV 1101 and UNIV 1102, which are first-year seminars that full-time, first-time in college students are required to take.

To fulfill the Major Curriculum, students must take 39 credit hours of computer science and mathematics courses, including COSC 1435, COSC 1436, and COSC 2334.

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Students must also choose one of the four options: Systems Programming, Cyber Security and Infrastructure, Computer Game Programming, or Computer Information Systems. Each option requires 39-40 credit hours of courses specific to that option.

Here is a breakdown of the required courses for all options:

Note that students must also choose one of the following mathematics courses: MATH 3342, MATH 3345, or another course that satisfies the University Core curriculum.

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The Cyber Security Option is a great way to focus on the security side of computer science. This option is designed for those who want to pursue careers in network administration, system administration, web administration, and cyber security expertise.

To earn this option, you'll need to complete a set of required courses, including COSC 2348, Introduction to Scripting, and COSC 2465, Linux Systems. You'll also need to take COSC 2466, Network Systems, and COSC 3351, Internet Programming.

Here's a list of the required courses for the Cyber Security Option:

The Cyber Security Option also requires you to take a set of courses in your junior and senior years, including COSC 3474, Cyber Defense I, and COSC 4310, Digital Forensics. You'll also need to take COSC 4367, Firewall and Intrusion Detection Systems, and COSC 4368, Penetration Testing.

You'll also need to complete a senior capstone project, which will give you hands-on experience in applying the concepts and skills you've learned throughout your degree program.

The Computer Information Systems option at TAMU requires 40 hours of coursework, which includes 12 hours of approved upper-division Computer Science electives and 18 hours of Minor Courses.

The required courses for the Computer Information Systems option are COSC 2470, COBOL Programming (4 hours); COSC 3324, Object-oriented Programming (3 hours); COSC 4342, Computer Networks (3 hours); and 12 hours of approved upper-division Computer Science electives.

Here is a list of the required courses for the Computer Information Systems option:

The student will gain knowledge of the particular application area by choosing a minor in an academic discipline highly related to the application area, and the minor should consist of at least 18 semester hours.

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The Computer Science (CS) program at TAMU is designed to equip students with a strong foundation in computer science concepts and programming principles. This foundation is built through courses such as CSCE 111 Introduction to Computer Science Concepts and Programming, which covers computation to enhance problem-solving abilities and understanding of how people communicate with computers.

To develop problem-solving skills, students take CSCE 430 Problem Solving Programming Strategies, which focuses on methods for analyzing fundamental programming problems and implementing solutions quickly and efficiently. This course emphasizes problem analysis, coding, and testing, and covers a range of algorithmic solutions, including combinatorics, dynamic programming, and graph algorithms.

To prepare students for the program's discipline, the BS CS degree program has been structured to ensure that all students meet the following outcomes by the time of their graduation:

Interactive information structures are a crucial aspect of computer science, allowing users to interact with and navigate complex information systems. This can be achieved through the use of hypermedia, which combines text, images, and other media to create a dynamic and engaging user experience.

In the context of computer science, interactive information structures are often taught in courses such as CSCE 444, which focuses on the programming, design, authoring, and theory of hypermedia. This course covers topics such as object-oriented visual and interactive programming, visual design, and the reference as a metadisciplinary structure.

To create effective interactive information structures, students must have a solid foundation in computer science fundamentals, including programming languages, data structures, and algorithms. This is reflected in the course sequence for computer science majors, which includes courses such as COSC 1435 (Introduction to Problem Solving with Computers I) and COSC 2437 (Data Structures).

Here's a breakdown of the courses that cover interactive information structures:

By mastering the concepts and skills covered in these courses, students can create interactive information structures that are both functional and engaging, and that provide users with a seamless and intuitive experience.

Computers are all around us, but have you ever stopped to think about how they work? A good place to start is with the basics of computer science.

Computational thinking is a fundamental concept in computer science, and it's all about using computers to enhance problem-solving abilities. This is what you'll learn in courses like CSCE 111 Introduction to Computer Science Concepts and Programming.

In these courses, you'll understand how people communicate with computers and how computing affects society. You'll also learn about computational thinking, which is a way of solving problems using computers. Computational thinking is a skill that can be applied to many areas of life, not just computer science.

Understanding how people communicate with computers is crucial in computer science, and it's what you'll learn in courses like CSCE 111 and CSCE 121 Introduction to Program Design and Concepts. These courses will teach you about design and implementation of algorithms, data types, program control, iteration, functions, classes, and exceptions.

As you progress in computer science, you'll learn about system support for application programs, both on single nodes and over networks. This is what you'll learn in courses like CSCE 313 Introduction to Computer Systems. You'll also learn about operating systems, which are the software that manages computer hardware resources and provides common services to computer programs.

Operating systems are a crucial part of computer science, and you'll learn about them in courses like CSCE 410 Operating Systems. This course will teach you about basic operating systems concepts, methods of operating systems design and construction, and case studies of several operating systems.

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Data Structures and Algorithms are the building blocks of computer science. They're essential for solving complex problems efficiently.

In a computer science course like CSCE 221, you'll learn about specification and implementation of basic abstract data types, such as stacks, queues, and lists. These data structures are crucial for organizing and processing large amounts of data.

You'll also study algorithms like sorting and selection, searching, and hashing, which are used to perform tasks like data retrieval and manipulation. Performance tradeoffs and asymptotic analysis will be covered, helping you understand how different implementations affect the efficiency of your code.

To master data structures and algorithms, you'll need a strong foundation in programming, which is why courses like CSCE 121 and CSCE 206 are prerequisites for more advanced courses like CSCE 221.

Practical experience with programming languages like C++ is also essential, as you'll be implementing and executing student programs in these languages.

Languages are a fundamental aspect of computer science, and understanding their design and characteristics is crucial for any aspiring programmer.

Formal languages and automata are basic types of abstract languages and their acceptors, which form the foundation of computer science.

The Chomsky hierarchy is a framework for categorizing languages based on their complexity and expressiveness, with type 0 being the most powerful and type 3 being the most restrictive.

You'll study the solvability and recursive function theory in a course like CSCE 433, which will help you understand the limits of computation and the power of different programming languages.

Programming languages are designed to solve specific problems, and exploring the design space of programming languages is a key part of computer science education.

In a course like CSCE 314, you'll dive deep into the design and characteristics of two programming languages, one functional and one object-oriented, and learn idiomatic uses of each language.

To succeed in programming languages courses, you'll need a solid foundation in computer science, such as the grade of C or better in CSCE 221, which is a prerequisite for many programming languages courses.

Studio 315 is a 3-credit course that requires students to have a solid foundation in Computer Science.

Students can expect to spend 2 hours in lecture and 2 hours in lab each week.

The course focuses on integrating core concepts in Computer Science and familiarizing students with various programming tools and techniques.

By working on 2 or 3 month-long projects, students gain hands-on experience with different specializations within Computer Science.

These projects emphasize different areas of specialization, allowing students to explore various aspects of Computer Science.

To take Studio 315, students must have completed CSCE 312 and CSCE 314, or have taken CSCE 350/ECEN 350 or ECEN 350/CSCE 350.

Concurrent enrollment in CSCE 313 is also a requirement for the course.

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Human interaction with computers is a crucial area of study, as evident in courses like CSCE 436, which focuses on Computer-Human Interaction.

This comprehensive study includes the history and importance of CHI, making it clear that understanding how humans interact with computers is vital for designing effective interfaces.

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CSCE 436 covers various design theories, as well as empirical techniques for task analysis and interface design, showing that a systematic approach is essential for creating user-friendly interfaces.

The course also explores different styles of interaction and future directions of CHI, including hypermedia and computer-supported collaborative work, highlighting the evolving nature of human-computer interaction.

To enroll in courses like CSCE 436, students typically need a grade of C or better in prerequisite courses such as CSCE 315 or CSCE 331, or instructor approval.

Machine learning is a crucial aspect of computer science, and it's essential to understand its theoretical foundations. This includes pattern recognition and generating predictive models and classifiers from data.

Machine learning methods cover a wide range of techniques, such as decision trees, linear discriminants, neural networks, and Gaussian models.

To succeed in machine learning, you'll need a strong foundation in mathematics and statistics, including courses like MATH 304, MATH 311, or MATH 323, and STAT 211 and STAT 404.

The CSCE 421 Machine Learning course is a great place to start, covering topics like supervised and unsupervised learning, optimization procedures, and statistical inference.

A grade of C or better in specific math and statistics courses is required to take CSCE 421, making it a challenging but rewarding experience.

Problem Solving Strategies are a crucial part of computer science.

To develop strong problem-solving skills, it's essential to understand computational thinking, which is taught in courses like CSCE 111 and CSCE 121.

Computational thinking involves breaking down complex problems into smaller, manageable parts, and using algorithms to find solutions. This is a key concept in computer science.

In CSCE 411, Design and Analysis of Algorithms, students learn how to analyze time and space requirements of algorithms, which is critical for developing efficient solutions.

Effective problem-solving also requires understanding abstraction, modularity, and code reuse, which are covered in CSCE 121.

To become proficient in problem-solving, it's essential to practice solving problems from various domains, including combinatorics, dynamic programming, graphs, numerical calculations, string processing, and geometry, as taught in CSCE 430.

Here are some key skills to focus on for effective problem-solving: