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What is Computer Science?

Computer Science is the broad process of working with computers in a practical or theoretical context. It involves the process of building the hardware and software that allows computers to operate and execute the tasks that we rely upon in our daily lives. In most cases, the process involves learning programming languages so that you can write the code that tells the computer what to display or do in response to a given set of circumstances or inputs. This is vital since computers require this programming to operate, and careful and judicious programming will be the difference between a system that works perfectly and one that is in constant danger of breaking down (which can have catastrophic effects given how much we rely upon computers).

Computer science is an incredibly diverse and varied field that encompasses everything from building consumer software to designing the machine learning algorithms that companies and government organizations use to automate complex tasks. Owing to this range of applications, no matter what interests you, there is a good chance that you can find a way to apply that passion to computer science. If you are interested in creative design, you can find work developing user interfaces and other interactive applications for web pages and consumer software. If you are interested in working with cutting-edge technology, you may want to consider pursuing a career in software engineering or machine learning engineering.

Why Learn Computer Science?

Learning computer science is a great way to open the door to a number of high-paying, in-demand career opportunities. Virtually every industry is utilizing computers in significant ways. As a result, computer science skills are employed in almost every facet of our lives, even when they are made invisible to us. By learning computer science, you can have a hand in developing the technologies and applications that make our world run more smoothly and make things that were once imagined as impossible into a reality.

At its core, computer science is essentially problem-solving. So, the lessons learned during computer science training have almost limitless applications. Furthermore, in terms of professional development, computer science is essential in many career paths. That includes many non-computer careers. Economists use models, biologists work with genomes, musicians work with synthesized sounds, etc.

Computer programming teaches lessons about what is possible and what is not possible. Appreciating these conceptual limitations makes learning and life much easier. Programming also means creating, which is a very satisfying endeavor. Additionally, computer science covers topics like ethics, information security, privacy, and other matters affecting us.

Finally, computer science stimulates critical thinking. Steve Jobs once said that computer science is a liberal art. Specific critical thinking skills include the quest for programming improvement, assessing different alternatives, and counting the cost of a particular effort.

Common Types of Computer Science

Computer science is a broad category of skills encompassing virtually every programming project, meaning that there are many different directions to take your computer science training. Regardless of the kinds of things one hopes to do, there is likely a programming language tailored to help facilitate these projects. These are just some of the many subfields within computer science, and each offers unique opportunities and challenges. As technology continues to evolve, new areas of specialization and research emerge, making computer science a dynamic and ever-expanding field.

Web Development

One of the most accessible aspects of computer science is web development, the process of writing programs that inform a web browser how to display a webpage and respond to user interactions. Using programming languages like HTML/CSS and JavaScript, Web Developers will write the code that makes modern web applications function. Given how much communication and commerce occur over web applications, building functioning designs and iterating on new web technologies is vital in almost every aspect of the modern world. Students enrolled in a web development class will learn HTML/CSS and JavaScript (both of which are accessible programming languages for first-time learners), and they will learn how to write a block of code that, when read by a web browser, displays an evocative and interactive webpage.

Front-end development, also known as client-side development, focuses on the user interface and user experience of a website. Front-end developers work on the parts of a website that users interact with directly in their web browsers. Key technologies and skills in front end development include HTML, CSS, JavaScript, Front-end frameworks and libraries, and front end design.

Back-end development, also known as server-side development, deals with the behind-the-scenes functionality of websites. Back-end developers build and maintain the server, databases, and application logic that enable websites to function. Key technologies and skills in back-end development include server-side programming languages, databases, web servers, APIs, security, frameworks, and version control. Some developers specialize in both front end and back-end development, known as full-stack developers. They have expertise in both areas and can work on the entire web application stack.

Data Science

Every time we interact with a web application or other computer program, we create a digital record of our activities, which is collected and organized as data. Every day, we produce an unfathomable amount of data, and it is the job of Data Scientists to build the hardware and software that collects and organizes this data alongside the tools for querying and interpreting it. Data Scientists use languages like Python and SQL to build and query databases. They often use tools like Tableau to convert data into persuasive visualizations that communicate their findings to non-specialist audiences. Data science training involves learning Python (arguably the best programming language for new learners) and learning how Python databases can be applied to different contexts. Python training is also important for working with machine learning and artificial intelligence programming projects. Learning data science can be an important first step in a more specialized computer science training regimen.

Data science skills are in high demand across various industries due to the increasing importance of data-driven decision-making. One of the most prominent roles in this field is that of a Data Scientist. Data scientists are professionals responsible for collecting, cleaning, analyzing, and interpreting data to extract valuable insights. They play a pivotal role in building predictive models, developing algorithms, and effectively communicating their findings to guide crucial business decisions.

Data analysts, on the other hand, are experts who primarily focus on collecting and analyzing data to help organizations make informed decisions. They are instrumental in creating reports, dashboards, and visualizations to convey data-driven insights to non-technical stakeholders. Machine Learning Engineers are a subset of data science professionals who specialize in developing and deploying machine learning models. They work extensively on designing algorithms, training models, and optimizing their performance for real-world applications.

In the financial sector, Quantitative Analysts utilize data to inform investment strategies and risk management. They develop financial models, conduct statistical analyses, and build trading algorithms to make informed decisions in highly volatile markets. Business Analysts leverage data to evaluate and improve business processes. They identify areas that require optimization, make recommendations for efficiency enhancements, and often work closely with stakeholders to define project requirements.

Data engineers are responsible for the design and maintenance of data pipelines and infrastructure. They ensure data availability, quality, and accessibility for data scientists and analysts, making their role essential in the data science ecosystem. These roles represent a diverse range of opportunities within the data science field, and they continue to evolve and expand as organizations increasingly recognize the value of data-driven decision-making.

Software Development

Commercial software development is another vital part of computer science. In our regular lives, we interact with computer software hundreds of times a day, even in ways we don’t notice. Software development tends to fall into a few categories depending on what kind of software is being discussed and how it is accessed. Some common consumer software applications include mobile applications, video games, and desktop software.

Students enrolled in a software engineering course will learn a wide range of programming languages, including HTML/CSS and JavaScript for building the visual and interactive elements of a software application (often called the front end) and Python and its libraries for the database and server-side aspects of the application (often called the back end). These courses are a great way to learn all the essential aspects of computer science and build hands-on, practical applications using this knowledge and training.

Often referred to as software engineering, software development is the process of designing, creating, testing, and maintaining computer programs and applications. It involves a systematic approach to building software to meet specific needs, whether for businesses, individuals, or organizations. Software development encompasses a wide range of activities and stages, including planning and requirements gathering which involves defining the problem that the software will solve and gathering detailed requirements from stakeholders. This step is critical for understanding the scope and objectives of the software project. During the design phase, software architects and designers create a blueprint for the software's structure and functionality. This includes defining the software's architecture, data models, user interfaces, and overall system design.

In the implementation or coding phase, developers write the actual code for the software based on the design specifications. Various programming languages and development tools are used to create the software. Quality assurance and testing are essential to identify and fix bugs, errors, and issues in the software. Different testing methods, such as unit testing, integration testing, and user acceptance testing, are performed to ensure the software functions correctly.

Once the software passes testing and quality checks, it is deployed to the production environment, making it accessible to users or customers. Deployment can vary from traditional on-premises installations to cloud-based solutions. Software development doesn't end with deployment. Continuous maintenance is necessary to address bugs, security vulnerabilities, and user feedback. Developers also release updates and new features to improve the software.

Comprehensive documentation is crucial for understanding how the software works, its features, and how to troubleshoot issues. Documentation helps developers, users, and support teams. Managing software development projects involve coordinating resources, timelines, and budgets. Agile, Scrum, and other project management methodologies are often used to ensure efficient development processes.

Building secure software is a top priority. Developers must follow security best practices to protect data and prevent vulnerabilities that could be exploited by malicious actors. Software development often involves collaboration among teams of developers, designers, testers, and other specialists. Effective communication and teamwork are essential for project success.

Software development is a dynamic field with various approaches and methodologies, including traditional Waterfall, Agile, and DevOps. It plays a crucial role in modern society, powering everything from mobile apps and web applications to operating systems and enterprise software solutions. It's a continually evolving field driven by technological advancements and changing user needs.

Internet of Things

Increasingly, software applications run on devices other than computers and mobile applications. Whether running on a digital assistant, a smartwatch, or a Bluetooth home security system, more and more of our household and personal devices are connected to the internet to facilitate their functions. This is referred to as the Internet of Things (IoT) and is one of computer science's most exciting aspects. IoT technologies are developing rapidly, and what was thought to be the realm of science fiction two decades ago has become incredibly accessible to the general public.

As the technologies become further refined, these technologies become ever more present in everyday lives. Most IoT technologies use JavaScript and its associated libraries to allow devices to access the internet and communicate with one another without the need for human input. For example, this technology lets the digital assistant on a phone access someone’s house’s AC system to turn on the air conditioner when asked to do so (or when it detects that the temperature has reached a point that’s been set to automate). Students enrolled in an IoT training program will learn JavaScript and Python for software development and how to use JavaScript’s libraries to help these applications run on devices other than computers and phones.

Key characteristics and components of the Internet of Things include sensors and actuators which mean that IoT devices are equipped with sensors that can detect changes in their environment, such as temperature, humidity, motion, or light. Actuators allow devices to perform actions in response to data or commands received. Connectivity, where IoT devices are connected to the internet or other networks, allowing them to send and receive data. Common connectivity methods include Wi-Fi, cellular networks, Bluetooth, and low-power wide-area networks (LPWANs). Data Processing, where IoT devices collect data from their sensors, which is then processed locally or in the cloud. Advanced analytics and machine learning algorithms can be applied to make sense of the data and extract valuable insights. Remote Monitoring and Control enables remote monitoring and control of devices and systems. For example, homeowners can remotely adjust their thermostats or security cameras using a smartphone app.

Interoperability devices often use standardized communication protocols to ensure they can work together seamlessly. Common protocols include MQTT, CoAP, and HTTP. IoT has a wide range of applications across various industries, including smart homes, healthcare (remote patient monitoring), agriculture (precision farming), transportation (vehicle tracking), manufacturing (predictive maintenance), and more. IoT security is a critical concern, as these devices can be vulnerable to cyberattacks. Ensuring the confidentiality and integrity of data transmitted by IoT devices is essential.

Examples of IoT devices and applications include smart thermostats, wearable fitness trackers, connected cars, industrial sensors in smart factories, smart appliances, and even smart cities that use IoT for improved urban planning and resource management. The growth of IoT is driven by advancements in sensor technology, wireless communication, cloud computing, and data analytics. As IoT continues to evolve, it has the potential to revolutionize industries, enhance efficiency, reduce costs, and improve our daily lives. However, it also raises important questions about data privacy, security, and the ethical implications of a highly interconnected world.

Machine Learning

Another exciting development in computer science is the rise of machine learning algorithms. Machine learning is a data science process in which computers are built to read and interpret large amounts of data without needing a human operator or interpreter. This allows complicated data processing projects to be completed in seconds and machines to make informed, predictive decisions without human oversight, making it a vital part of emerging artificial intelligence technologies.

Currently, the most common use for machine learning algorithms is in content recommendation algorithms that help direct users to products or content they may be interested in. However, machine learning technology is becoming a wide-open field with the emergence of chatbots. No one can be certain of what the future holds for this exciting technological development. Machine learning relies heavily on Python and other data science languages, so students hoping to learn how to write machine learning algorithms will need to consider learning data science skills.

Machine learning applications are widespread and include natural language processing, image recognition, recommendation systems, autonomous vehicles, medical diagnosis, fraud detection, and many more. Machine learning is a dynamic and rapidly evolving field with ongoing research and developments. It has transformative potential across various industries.

Artificial Intelligence (AI)

AI focuses on creating intelligent agents or systems that can simulate human-like thinking and decision-making. This includes machine learning, natural language processing, computer vision, and robotics. AI has a wide range of applications across various industries, including healthcare (diagnosis and treatment planning), finance (fraud detection and algorithmic trading), transportation (self-driving cars), and entertainment (video game AI and content recommendation). As AI technologies continue to advance, their impact on society and the economy is expected to grow significantly. However, AI also raises important ethical and societal questions related to privacy, bias, job displacement, and more, which require careful consideration and regulation.

AI has made significant strides in understanding, interpreting, and generating human language. Natural Language Processing (NLP) is the branch of AI that enables systems to interact with and comprehend human language. Applications span from chatbots and language translation to sentiment analysis and voice assistants like Siri and Alexa. AI also extends its reach into the visual realm through computer vision. This capability enables machines to interpret and comprehend visual information from the real world, encompassing images and videos. Practical applications include facial recognition, object detection, and the development of autonomous vehicles.

Robotics

AI plays a pivotal role in the realm of robotics, driving the design and control of robots and autonomous systems. AI-equipped robots can perform a wide range of tasks in diverse environments, revolutionizing industries like manufacturing and logistics. Robotics has a wide range of applications across various industries, including manufacturing, healthcare, agriculture, space exploration, search and rescue, and entertainment. Industrial robots, for example, are used in manufacturing processes to automate tasks like welding, painting, and assembly. Medical robots assist surgeons in performing precise procedures, and autonomous drones are used for tasks like aerial surveillance and package delivery.

The field of robotics continues to advance rapidly, driven by developments in artificial intelligence, sensor technology, and materials science. Researchers and engineers are working on creating more capable and versatile robots that can handle increasingly complex tasks and operate safely alongside humans in various settings.

Why Computer Science is Important

Computers and technology are integral to our daily lives. Whether it's smartphones, laptops, or smart appliances, understanding how these devices work and how to use them effectively is crucial. Computer science teaches systematic problem-solving techniques. These skills are valuable not just in computing but in various aspects of life and work. The demand for computer science professionals continues to grow across diverse industries. Learning computer science can lead to a wide range of well-paying and rewarding careers. Computer science drives innovation. Many of the technological advancements that shape our world, from social media platforms to medical breakthroughs, rely on computer science.

Computer science education imparts digital literacy, helping individuals navigate the digital landscape safely and effectively. It encourages critical thinking and analytical skills, which are valuable in many fields, not just computing. Computer science intersects with various disciplines, from healthcare to environmental science, enabling interdisciplinary research and solutions. It can also enhance education through online learning platforms, interactive simulations, and personalized learning experiences.

It facilitates global connectivity and communication, breaking down geographical barriers. Computer science is a foundational skill in the digital age. It offers practical, intellectual, and career benefits, and it connects the modern world together. Whether you aspire to be a software developer, a data scientist, or simply a well-informed digital citizen, learning computer science is valuable and relevant.

What Should You Consider Learning Alongside Computer Science?

Learning computer science can be greatly enhanced by gaining knowledge in complementary areas. Some subjects and skills you might consider learning alongside computer science include:

Mathematics: Computer science and mathematics are closely related. A strong foundation in mathematics, particularly in areas like discrete mathematics, calculus, linear algebra, and probability, will help you understand algorithms and data structures better.
Programming Languages: While you'll learn programming languages as part of computer science, becoming proficient in multiple languages broadens your skill set. Consider languages like Python, JavaScript, C++, and Java, which are widely used in various domains.
Data Structures and Algorithms: Deepening your knowledge of data structures and algorithms will improve your problem-solving abilities and efficiency in coding.
Web Development: If you're interested in web applications, learning HTML, CSS, and JavaScript for front end development, as well as server-side technologies like Node.js, Ruby on Rails, or Django, can be valuable.
Databases: Understanding databases and SQL is crucial for managing data in applications. Learn how to work with relational databases like MySQL or PostgreSQL, and NoSQL databases like MongoDB.
Operating Systems: Familiarize yourself with how operating systems work, including processes, memory management, and file systems.
Networking: Learn about networking protocols, security, and how data is transmitted over the internet.
Cybersecurity: With the increasing importance of cybersecurity, understanding the basics of security, encryption, and ethical hacking can be valuable.
Artificial Intelligence and Machine Learning: These fields are closely related to computer science and are experiencing rapid growth. Learning about AI and ML can open up various career opportunities.
Software Development Practices: Gain knowledge of software development methodologies like Agile or Scrum, version control systems like Git, and tools for continuous integration and continuous deployment (CI/CD).
Soft Skills: Communication, teamwork, problem-solving, and critical thinking are essential skills in any career, including computer science. Develop these skills to work effectively in teams and communicate your ideas clearly.
Domain-Specific Knowledge: Depending on your career goals, consider learning about specific industries or domains where you plan to apply computer science, such as finance, healthcare, or gaming.
Project Management: Understanding project management methodologies like PMBOK or Agile can be beneficial, especially if you're interested in leading software development projects.

The best areas to complement your computer science learning may depend on your career goals and interests. Continuously adapting your skills to align with industry trends and emerging technologies is also essential in the ever-evolving field of computer science.

Careers in Computer Science

Given how common computers are and how they are revolutionizing almost every aspect of the public and commercial sector, computer science careers are in high demand. Since the field is so diverse in terms of job titles, responsibilities, and employment opportunities, it can take time to track all available options. However, per the Bureau of Labor Statistics, computer science-related jobs are expected to grow significantly in the next decade, regardless of the specialization.

For example, Web Developers and Software Engineer positions are expected to grow by 25% over the next decade, significantly more than the national average. Those interested in a career working with data may consider looking for work as a Data Scientist, a job that is expected to grow by about 36% year over year in the next decade. Aspiring Computer Scientists can also find work in more specialized roles, like Machine Learning Engineers or management roles like Software Project Managers. Artificial Intelligence (AI) Researchers work on advancing the field of artificial intelligence. They develop new algorithms, models, and technologies to improve AI systems' capabilities. The salary for all of these positions will vary depending on experience, location, and industry. Still, these are all in-demand career fields that offer competitive salaries for professionals with programming experience.

Computer Science Classes

Students looking to learn computer science will need to start somewhere. A professional training course is a great way to become accustomed to the practices and skills required to succeed as a programmer. When choosing a course, students should consider two important factors. First, they should consider what computer science skills they want to learn and how they can best achieve those goals. Some students will want a robust education covering all programming knowledge, while others will want to learn specific skills, like machine learning. Second, students should consider how much prior knowledge they have. Many classes are designed for students with no prior coding experience. Still, some have prerequisites, so students will need to consider their own level of comfort with computer programming before enrolling.

Noble Desktop offers an array of beginner-friendly, career-focused bootcamps aimed at teaching the ins and outs of computer science. These courses cover several subjects, and students can find classes that teach the specific computer science lessons they are looking for. Noble Desktop offers Full Stack Web Developer Certificate, Data Science Certificate, and Software Engineering Certificate programs for students looking for immersive and comprehensive computer science training. In these classes, students will receive guided instruction in several different programming languages, and they will be trained in the skills they need to find work in these respective fields. These are all career-focused bootcamps, meaning students will receive professional development training aimed at preparing them to start finding a job as soon as they graduate. Some of these professional development services include portfolio-building seminars and one-on-one career mentoring sessions. These courses are ideal for anyone aiming to learn computer science in preparation for a new career.

Noble Desktop also offers many shorter classes for students who aren’t as certain about the direction they want their computer science training to take. For example, students looking to get a feel for their first programming language may want to enroll in a class like the Python for Data Science Bootcamp or the Web Development with HTML & CSS course. These classes provide students with an overview of essential programming languages and teach students the basics of thinking like a programmer. These courses are ideal for students looking to get accustomed to computer science skills but aren’t sure that a career change is right for them. These classes are also taught as part of the larger bootcamp programs, so students will find it easy to transition to more elaborate classes once they complete these shorter courses.

Many service providers offer comprehensive computer science training programs like the UCLA Extension School’s Introduction to Computer Science I course. In this class, students will learn basic computer programming skills pertaining to data management and software engineering. Students will learn basic data types, object-oriented programming skills, and how to apply computer science theory to practical development problems. This course is a solid introduction to the world of computer science and students looking for a broad overview of the field may want to consider courses like this one.

Students may also want to consider enrolling in focused, introductory programming courses, such as ONLC Training Center’s Java Programming Core Skills: Level 1 or General Assembly’s JavaScript Course. These focused classes offer students the chance to design their own computer science curriculum by taking the classes that teach the programming skills they want to learn. There are a ton of different options available and most training providers who teach one programming language will teach several, so students interested in mixing and matching their classes will have a wide range of options on the table.

There are far too many options for computer science training to list, so Noble Desktop has created its Classes Near Me tool to help students with this. Using this tool, students can find and compare in-person and live online computer science classes in their area.

How Long Will It Take to Learn Computer Science

The time it takes to learn computer science varies widely depending on your goals, prior experience, and the depth of knowledge you want to acquire. If you want to learn the basics of computer science, such as programming fundamentals, it can take a few months of consistent effort. Many introductory courses and coding bootcamps offer programs in this range. Noble Desktop offers a wide variety of courses and bootcamps.

To become proficient in a programming language like Python or JavaScript, it may take anywhere from 6 months to a year of dedicated learning and practice. This level of expertise allows you to build basic applications and scripts. To gain a solid foundation in computer science, which includes data structures, algorithms, and problem-solving, it typically takes 1 to 2 years of formal education at the undergraduate level or self-study with online courses and textbooks.

A traditional bachelor's degree in computer science usually takes 3 to 4 years of full-time study. This provides a comprehensive understanding of computer science principles and often includes practical projects. Pursuing a master's degree in computer science typically requires an additional 1 to 2 years of study beyond a bachelor's degree. This can be focused on advanced topics or research. Doctoral programs in computer science can take 3 to 5 years or more to complete, depending on the research area and dissertation work.

Remember that learning computer science is an ongoing process, as technology and the field itself are continually evolving. Many computer scientists continue to learn throughout their careers to stay up-to-date with the latest developments.

The pace of learning also depends on factors like prior knowledge, study habits, available resources, and the complexity of the topics you're tackling. It's important to set realistic goals, stay consistent in your efforts, and continually practice and apply what a student has learned to gain proficiency in computer science.

In-Person or Online

After a student decides on the kind of computer science training they want, they will need to find a course that fits their learning needs as well as their practical needs. Students can take courses either online in live digital classrooms or in person at dedicated training centers. Both options ensure that students will be learning practical skills from experienced professional instructors. However, there are differences between the two learning methods and students will need to consider the differences between them when choosing the delivery method that is right for them.

In-person training offers students the opportunity to work face-to-face with their instructors in a dedicated classroom environment. Many students find that that classroom space is an ideal one to learn in since it is designed to direct students' attention towards their instructor and keep them from getting distracted. In addition, at an in-person training center, students will be able to work alongside a cohort of classmates, providing them with valuable networking opportunities and chances to work on collaborative projects. The drawback to these classes is that students will only be able to enroll in courses they can physically attend which can be fairly limiting for students who don’t live near major cities.

Live online courses are an alternative to in-person classes for students looking to avoid a commute or receive training from a program outside of their local area. These classes are taught over live telecommunication applications like Zoom, meaning that they are still taught by an experienced, live instructor. In these classes, students can receive personalized attention and feedback in real time and they won’t be sacrificing the quality of instruction. Students will be more free to pick and choose the training program they want to enroll in (though they should consider things like time zone differences). The drawback to these classes is that students may feel more isolated and easily distracted working from their home office rather than in a classroom. In addition, students will need to set up and maintain their own technology.

Which is Better: Live Online or Asynchronous Classes?

The decision between live online classes and asynchronous classes depends on an individual’s learning style, schedule, preferences, and individual circumstances. Both formats have their own advantages and disadvantages. An asynchronous class refers to an educational course or learning experience where students do not need to be present in real time for lectures, discussions, or assignments.

In other words, students can access course materials, complete assignments, and engage with content at their own pace and on their own schedule. Asynchronous classes offer a flexible learning environment that accommodates students with varying schedules, time zones, and commitments.

Meanwhile, live online courses are offered over the Internet in which instructors and students interact in real time, just like in traditional classroom settings. These courses replicate the structure and dynamics of a physical classroom by using virtual platforms and tools that allow participants to engage, discuss, and learn together at scheduled times.

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Full-Stack Web Development Certificate Learn The Skills Guarantee™

Learn More About Computer Science Courses

What is Computer Science?

Why Learn Computer Science?

Common Types of Computer Science

Web Development

Data Science

Software Development

Internet of Things

Machine Learning

Artificial Intelligence (AI)

Robotics

Why Computer Science is Important

What Should You Consider Learning Alongside Computer Science?

Careers in Computer Science

Computer Science Classes

How Long Will It Take to Learn Computer Science

In-Person or Online

Which is Better: Live Online or Asynchronous Classes?

Computer Science Courses by School

Python Immersive

Python for Data Science Immersive

Introduction to Python

JavaScript Immersive

Introductory Python

Data Science with Python: Data Analysis and Visualization

Introduction to Computer Science I

Java Programming I

Java Programming II

Java Programming III

Python Programming Short Course (Online)

JavaScript Short Course (Online)

JavaScript Bootcamp (Online) Not currently offered

Python Programming Certificate

Python Bootcamp

Computer Science Summer Program

Java Programming Summer Bootcamp

Full-Stack Developer Summer Program

Python Data Science & Machine Learning Summer Program

Java Programming

Ruby Programming

Ruby on Rails

Introduction to Python Programming

Python 3 Essentials

Python Programming - Advanced

Python Programming - Introduction

Data Wrangling With Python

Computer Science Corporate & Onsite Training Noble Desktop

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