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Computer science courses: A comprehensive overview
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The expansive computer science field explores software and computer systems and their problem-solving power. Computer science curriculums vary considerably depending on the school, program, and course type and level, but prospective students should expect to see a healthy dose of math courses, such as statistics, calculus, probabilities, and discrete math.
As a result, the training can challenge students in their pursuit of completing an online computer science degree, certificate, or bootcamp. Yet, this also helps make the training and the acquired skill set specialized and valuable, leading to some very promising computer science jobs.
Guide to bachelor's in computer science courses
The style and structure of a bachelor's degree in computer science vary, but the programs typically feature a blend of introductory courses, intermediate and advanced courses, and complementary courses and electives chosen by the students. Many of the project-based courses feature individual assignments, though students will complete partner- and group-based projects as well.
While the training can be rigorous, many schools help students design a schedule that progresses logically and limits the number of heavy workload courses taken at the same time, such as programming courses. Success students usually need technical skills, determination, and self-motivation, particularly in an online computer science degree.
Check out the details below for some of the more common computer science courses.
Algorithms and data structures
Typical Difficulty Level:
Medium
Typically Includes Project(s)?:
Yes
Quick facts:
- Explores the various algorithms and data structures used to solve the most common computational problems.
- Teaches design, implementation, and analysis of data structures and algorithms.
- Students work with object-oriented programming and design.
- Learn search and sort algorithms.
- Use practical data structures, such as lists and stacks.
Computer architecture
Typical Difficulty Level:
Hard
Typically Includes Project(s)?:
Yes
Quick facts:
- Understand how computer hardware and software interact.
- Learn the architecture systems used to design and build processors and systems.
- Examine the hierarchy of computer functions.
- Study pipelining, drives, addressing, performance enhancement, and memory.
- Analyze CPU and memory performance.
Fundamentals of computer systems
Typical Difficulty Level:
Medium
Typically Includes Project(s)?:
Yes
Quick facts:
- Introduction to assembly programming languages and software design.
- Cover various memory systems, data representation, and machine languages.
- Optimize and analyze code.
- Understand programming and architecture.
- Learn processes and exceptions and how applications interact with systems and hardware.
Introduction to networks
Typical Difficulty Level:
Medium
Typically Includes Project(s)?:
Yes
Quick facts:
- Learn the software and hardware involved in networking.
- Understand network architectures, protocols, client-server models, and reliability.
- Explore the principles, fundamentals, and structures of various networks.
- Build cable networks with configurations and schemes for network devices and IP addresses.
Operating systems
Typical Difficulty Level:
Hard
Typically Includes Project(s)?:
Yes
Quick facts:
- Introduces the various principles and structures of operating systems.
- Teaches students how to write and execute code for efficient programs.
- Covers process and storage management, along with security issues.
- Examines distributed, multimedia, and embedded systems.
- Develop, diagnose, and analyze the functions and components of operating systems.
Programming languages
Typical Difficulty Level:
Hard
Typically Includes Project(s)?:
Yes
Quick facts:
- Learn to design, develop, and document efficient software programs using various programming languages.
- Teaches the basic concepts of programming language syntax and semantics.
- Gain proficiency in one or more languages, such as JavaScript, Python, Ruby, and C++.
- Explore the differences between languages and the justifications for them.
Master's and Doctorate in computer science: Specializations and courses
A master's degree in computer science can lead to advanced professional and educational opportunities, and students can influence their pathway with the type of program they choose. Master's degrees usually take two years to complete and feature course, project, or research-based structures. Course and project-based programs typically feature a capstone project, whereas research-based programs require a thesis.
A doctorate in computer science often leads to careers in academia or research and development. Most doctorates run for 4-7 years and feature a research-based dissertation. Both graduate degree types offer flexibility and allow learners to choose a specialization based on their career and research interests, and the list below explores some of those options.
Artificial intelligence
Description of Specialization:
Artificial intelligence specializations focus on a computer's problem-solving abilities, along with its predictive and recommendation capabilities. The studies cover language processing, interpretation, and pattern recognition. Additional topics may include industry-specific applications, such as trading and healthcare.
Typical Courses:
- Machine learning
- Speech and language processing
Computer-human interaction
Description of Specialization:
This specialization examines the various applications humans have for computers. Students may study the influence of computers on society, along with the reasons for their designs. Some of the training may also cover the evaluation of the systems and what type of interfaces are most effective on consumers and end-users.
Typical Courses:
- Human-computer interactive design
- Emergent interface technology
Cybersecurity
Description of Specialization:
Cybersecurity specializations explore the various methods for protecting computer systems and information. Students may look at the languages and architectures used in the design process and cyber threat detection and investigation. Other topics may include cryptography, ethics, and privacy laws.
Typical Courses:
- Developing secure systems
- Digital forensics and investigations
Data science
Description of Specialization:
Data science specializations examine the many uses for data, the methods for extracting the information, and the techniques and tools for analyzing it. The training looks at diverse math, statistics, and computer science topics, along with industry applications, such as healthcare, business, and sports.
Typical Courses:
- Database systems
- Decision analytics
Game design
Description of Specialization:
Game design specializations focus on the processes used in game production. Students learn how to design and program games, create game engines, and modify gaming projects. They may study 3D and character modeling, computer-user interactions, and art and sound design.
Typical Courses:
- Game architecture and design
- Game production
Mobile and web computing
Description of Specialization:
Mobile and web computing specializations look at the development of applications for the web and mobile devices. The training often covers the entire development lifecycle, considering stakeholder needs, interactivity and user-friendliness. Students may also learn how to develop cross- and multi-platform applications.
Typical Courses:
- Mobile native applications
- User experience design
Networks
Description of Specialization:
A specialization in networks emphasizes the design, development, implementation, and security of various networks in different settings. Students learn to apply the technologies, principles, and protocols of network systems to enhance organizational communication and effectiveness.
Typical Courses:
- Network security
- Advanced wireless networks
Robotics
Description of Specialization:
Robotics specializations focus on the design and development of automated systems for various uses. The training covers the programming and circuit design of intelligent robotic systems. Learners may also work with sensors, signals, digital logic and control systems.
Typical Courses:
- Autonomous robotics
- Artificial intelligence techniques
Software engineering
Description of Specialization:
Software engineering specializations examine the steps and processes involved in software design and development. Students learn engineering principles and approaches, including analyzing the requirements, exploring alternatives, and evaluating the final product. The training also covers testing, safety and production capabilities.
Typical Courses:
- Software tools and programming
- Computability and computational complexity
Theory
Description of Specialization:
Theory specializations teach students how to apply advanced mathematical theories to computer science. Students may examine theories in cryptography, system processing, and computational algebra. Other topics may include programming language theory, software theory, reactive systems and algorithms.
Typical Courses:
Information theory
Computation theory
Coding bootcamp courses
Coding bootcamps offer condensed training packages that equip learners with coding knowledge and skills. Bootcamps may cover introductory, advanced, or specialized material depending on the program. These programs usually focus on up-to-date and practical skills that can be used on the job immediately after completion.
Most coding bootcamps provide applicable programming skills in a particular field, such as JavaScript, Ruby, or SQL. However, some may also lead to industry certification with a specific vendor. Learners may pursue these programs to enter the tech field, advance their career, or refresh or update their skillset.
The style and structure of bootcamps vary, but they typically run between 3-6 months and cost an average of $13,579, according to BestColleges.com. While some organizations offer in-person programs, most coding bootcamps feature online courses for added flexibility.
Compared to a college degree, coding bootcamps run much more focused training, omitting any general education and interdisciplinary courses. The condensed training means significantly shorter completion times, but it also means the course loads can be more intense and rigorous.
The courses included in a coding bootcamp depend entirely on the level and focus of the coding bootcamp chosen, but learners may encounter some of the following courses:
- Web Development
- UX/UI Design
- Front-end Fundamentals with JavaScript
- Data Analytics
- Software Architecture
- SQL Databases
- Python
- Applied Machine Learning
- Digital Marketing
8 tips for success in computer science courses
Students may find computer science difficult, but they can improve their chances of success in a computer science degree by following some basic study principles. The section below underscores some general advice and comp sci tips for better study results.
1. Review course pamphlets.
Students should fully understand their program and course expectations and goals before beginning. They can read through their course pamphlets and identify the types of assignments and project requirements to avoid surprises.
2. Get ahead of your deadlines.
A study schedule can get busy, and projects can pile up if students procrastinate. Learners should consider keeping a calendar to track project deadlines and exam dates to ensure they stay on top of studying and set aside enough time.
3. Pace yourself.
It can be tempting to want to rush through and complete all material as quickly as possible, but students who pace themselves might have more success. Students should take their time and understand each component before moving on.
4. Make your mental health a priority.
Studies can become overwhelming and exhausting for many degree-seekers, so students should prioritize their own mental health. Learners can develop a steady and regular study pace, avoid procrastinating, take breaks, and enjoy the extracurricular activities available at school to improve their mental health.
5. Study smarter, not harder.
While many people cram for tests leading up to exams or rush to complete assignments at the last minute, this strategy does not produce the greatest results. Instead, degree-seekers should study during the times they feel most alert and creative and do so over an extended period of time.
6. Don't do all of your studying alone.
Most students study alone, but studying in groups comes with many advantages. In a study group, learners can pick up information more easily, beat the urge to procrastinate, and identify questions or angles they would have otherwise missed.
7. Have a life outside of school.
Completing the degree is the goal for most students, but colleges and universities offer amazing opportunities to meet new people, find inspiration, and access a wealth of facilities and resources. Learners should try to enjoy life outside their studies whenever they can.
8. Communicate with your professors.
Speaking with professors has many advantages for learners. Students can learn more about a topic or an assignment, consider new approaches or ways of looking at the material or build their network.
In conclusion
The computer science discipline may look and sound daunting from the outside, but the programs and courses cover some of the most interesting material and provide some of the most applicable skills available. The diverse training options offer something for most students, including foundational and comprehensive studies, advanced specialized knowledge, and accelerated bootcamps.
For more information, prospective students can look to apps, blogs, podcasts, and other computer science resources.