Computer Science
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Baylor University’s Master of Computer Science online degree program prepares its graduates for the highest-demand roles in software engineering, data science, and cybersecurity免费代理IP_HTTP代理服务器IP_隐藏IP_QQ代理_国内外代理 ...:110 行 · HTTPS代理IP 更多 国家 代理IP地址 端口 服务器地址 是否匿名 类型 存活时间 验证时间 …
Designed for students with a bachelor’s in computer science or a math-focused undergrad degree, this program will prepare you to become an expert in computing.
- Developed for maximum demand—earn advanced credentials and skills for leading computer science roles.
- Instills innovative thinking—gain comprehensive knowledge and thoughtful skill-building for technology experts.
- Globally connected, ethically centered—consider the human aspects surrounding technology in an increasingly connected and diverse world.
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Opportunities in technology continue to grow for professionals. Baylor University’s Master of Computer Science online program provides an advanced educational experience that emphasizes mastery from both technical and professional perspectives.
- Foundational courses (prerequisite track) prepare you for your master’s degree by teaching key computing fundamentals
- Core computer science concepts build your knowledge of topics, such as algorithms, software languages, databases, and networking
- Specialization tracks let you focus on the role you’re most passionate about—software engineering, data science, or cybersecurity
Prerequisite(s): CSI 4344.
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This course will provide a comprehensive introduction to computer algorithms taken from diverse areas of application. It will concentrate on algorithms of fundamental importance and on analyzing the efficiency of these algorithms.
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Our dedicated enrollment advisors can help you decide which track is best for you. Here’s some information to get the conversation started:
This course will explore the advances in large‐scale data repositories. Students will be exposed to advanced topics in the analytical approaches to handling the five Vs — volume, velocity, variability, veracity, and value — of big data. Parallel programming based on the MapReduce paradigm within the Hadoop Ecosystem is used to address these needs.
Current relational database design concepts, including ER diagrams and normalization. Database access techniques, such as SQL and JDBC. Database issues, including performance and security. Web-database applications. Fee: $50.
Data Science
An introduction to the concepts, techniques, and applications of data warehousing and data mining. Topics include the design and implementation of data warehouse and OLAP operations; data mining concepts and methods, such as association rule mining, pattern mining, classification, and clustering; and the applications of data-mining techniques to complex types of data in various fields. Advanced topics in machine learning and statistics will be covered.
An engineering approach to software development emphasizing design patterns and techniques for enterprise application development. Completing a software project by applying a development process. Fee: $50.
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Prerequisite(s): CSI 3344. Fundamentals of computer networking, including data transmission, communication software, protocols, simple networks, and internetworking.
Defensive programming, secure development operations, secure ecosystem, etc. Networking, database, operating system, and server fundamentals and the application of security principles in authentication, authorization, and design. Cloud/mobile/IoT/security.
Cybersecurity
Prerequisite(s): CSI 3344. This course is an introduction to the theory of computation. It covers the formal models of computation, computability, complexity, and related topics. This course forms a foundation for much of the subsequent work/research you will do in computer science. As you'll see, computation theory is fascinating because there are problems that we can describe simply, which have not yet been solved.
Prerequisite(s): CSI 3344. Operating system design and implementation. Topics include process control and synchronization, memory management, processor scheduling, file systems, and security. Course projects implement parts of an operating system. Fee: $50.
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Prerequisite(s): CSI 3344 or graduate standing. Advanced data structures, algorithm design, and analysis. Topics include common data structures, algorithms, implementation, classes of algorithms, algorithm analysis, computational tradeoffs, and the adaptation of familiar algorithms to new problems.
The objective is to introduce a software-engineering practice. After the course, you will be able to document software systems using known notation. You will learn to analyze, design, and implement information systems using Java EE. You will recognize best practices and good coding styles as well as the development and testing approaches used in the industry for enterprise systems. In the class, we also investigate alternatives to the mainstream and produce system documentation on a thesis level.
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Texas is the second-largest market
for computer-related positions
- Burning Glass Technologies, 2019
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- Earn your Master of Computer Science degree from Baylor University, ranked #79 among national universities by U.S. News & Report.
- 100% online.
- Courses are 15 weeks, with three intakes per year.
- Students learn from nationally recognized faculty.
- Total credits: 33 (48 with foundation courses).
Prerequisite(s): CSI 3334 and 3335. A continuation of database system implementations to include object-oriented and knowledge-based systems. Additional topics covered are the physical-data organization, database integrity, security, transaction management, and distributed database management.
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- 3.0 GPA in Bachelor's degree
- Three letters of recommendation
- Resume
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Prerequisite(s): CSI 4336. An introduction to topics in machine learning, including supervised and unsupervised learning, modeling for regression and classification, Naive Bayes methods, kernel-based learning, support vector machines, statistical and mathematical models for learning, and model assessment and prediction.
Applicants should have a Bachelor of Science (B.S.) degree in computer science or a closely related field. We expect successful applicants to have the equivalent training of a B.S. degree in computer science from Baylor University with knowledge of fundamental theory (math, algorithms, data structures), computer systems (operating systems, network, databases), software engineering, and programming.
For those applying with less than the standard preparation, the quality and adequacy of the admissions record will be evaluated by the Graduate Committee of the Department of Computer Science after reviewing the application for admission. The requirements, which must be met before admission, will be determined by that committee. These requirements will be in addition to the requirements for the M.S. degree.
Prerequisite(s): CSI 3372 or consent of instructor. Advanced topics in software engineering research, including techniques used in software verification and validation with a focus on software specification and testing.
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Designed to provide students with greater skills in software systems, this curriculum focuses on software systems development, data science, and cybersecurity.
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Distributed systems underlie software in multiple domains, including enterprises for stock trading, health care, online shopping, data processing, and transportation management. These systems are frequently designed using the Microservice Architecture (MSA). MSA splits the overall system into independent self‐contained modules managed by distinct development teams. These low-coupled modules interact on a high level (e.g., through REST calls or messaging), which enables development autonomy, individual module upgrades, or selective redeployment.
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Data Science Track
This course focuses on advanced topics in cloud environments (AWS, Google, Azure) and economics, history, differences, and importance of architecture decisions such as the decisions that drive analytics, data lakes, arts, and warehouses. The core concepts of virtual private clouds, instance types, microservices, and storage services will be addressed, in addition to deeper architecture concepts.
Cybersecurity Track
Visualization as a tool for data analysis, recall, inference, and decision‐making. This advanced class will explore high‐dimensional data spaces and tools for visual description and presentation. Principles of effective visualization, including data‐visual mapping, interaction techniques, color theory, cognitive and perceptual psychology, and the human factors of visual depictions of data.
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Introduces topics in cybersecurity, including cryptography.
Advanced topics in cybersecurity, including information security policy, governance, risk management, and strategic decision making.
The application of data principles to anomaly detection, response, etc.
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