Whether you're a budding programmer or simply curious about the fascinating realms of computer science, free online courses can help you grow in this field with no money you spend — just effort and commitment.
In this compilation of the best accessible computer science courses in 2023, we shed light on the high-quality educational online resources that cater to learners of all levels. So, if you're just about learning to program or want to refresh your existing knowledge, read on and make your choice!
"Introduction to Computer Science" is an entry-level course Harvard University offers. It introduces both majors and non-majors to computer science and programming. The course covers algorithms, data structures, software engineering, web development, and more.
The course is suitable for people with or without prior programming experience. It's taught by David J. Malan, Doug Lloyd, and Brian Yu, who teach students to think algorithmically and solve problems efficiently. The course is self-paced, and students who complete nine problem sets and a final project can earn a certificate. The course lasts 12 weeks (6–18 hours per week).
What you'll learn:
Basics of computer science and programming
How to solve programming problems
Concepts like algorithms, data structures, abstraction, encapsulation, security, resource management, software engineering, and web development
Get familiarity with such programming languages as C, Python, SQL, JavaScript, CSS, and HTML
How to collaborate with like-minded students
How to create and present a final programming project
Like the previous course, "Introduction to Computer Science and Programming Specialization" is a good choice for newbies still hesitating about whether programming is their thing. The course will help you learn fundamental computing principles and how to navigate computational tools. It'll also give you a solid understanding of various computer operations that apply across different software and systems.
The course has a flexible schedule and lasts two months (granted, you devote 10 hours per week per learning). After completing the course, you'll earn a career certificate from the University of London.
The course is divided into such topics:
If you think AI is only for engineers, you can't be further from the truth. And specialists from Stanford University prove this with their course. The "Introduction to Artificial Intelligence" is mainly non-technical and suitable for students of all levels, even with no programming background.
The course is taught by Andrew Ng and consists of 4 modules:
What is AI (9 videos, 1 quiz)
Building AI projects (8 videos, 1 quiz)
Building AI in your company (10 videos, 1 quiz)
AI and society (8 videos, 1 reading, 1 quiz)
The course has a flexible schedule, meaning you can study at your own pace. It requires approximately 10 hours (3 weeks at 3 hours). After finishing the course, you'll earn a shareable career certificate to add to your LinkedIn profile, resume, or CV.
What you'll learn:
Common AI terminology
Ethical and societal discussions surrounding AI and how to navigate them
What AI can and can't do
How to apply AI to problems in your organization
How to build machine learning and data science projects
How to work with an AI team
Algorithms Part 1 and Part 2 by Princeton University
In this course, you'll delve into the fundamental concepts every programmer should master – algorithms and data structures. The emphasis is on practical applications and analyzing Java implementations for scientific performance. Part I covers elementary data structures, sorting, and searching algorithms, while Part II focuses on the graph and string-processing algorithms.
Kevin Wayne and Robert Sedgewick taught the course. It's entirely free, offering all its features without any cost. However, please note that it does not provide a certificate upon completion.
The course is self-paced, and Part 1 lasts approximately 54 hours (3 weeks at 18 hours a week), whereas Part 2 lasts 62 hours (3 weeks at 20 hours a week). Previous experience is optional.
Part 1 includes the following modules: Course Introduction, Union-Find, Analysis of Algorithms, Stacks and Queues, Elementary Sorts, Mergesort, Quicksort, Priority Queues, Elementary Symbol Tables, Balanced Search Trees, Geometric Application of BSTs, Hash Tables, Symbol Table Applications.
Part 2 includes the following modules: Introduction, Undirected Graphs, Directed Graphs, Minimum Spanning Trees, Shortest Paths, Maximum Flow and Minimum Cut, Radix Sorts, Tries, Substring Search, Regular Expressions, Data Compression, Reductions, Linear Programming, Intractability.
You'll like this comprehensive course if you're willing to explore the essentials of machine learning and discover the transformative world of AI. Led by renowned AI expert Andrew Ng, this beginner-friendly program covers the foundational principles of machine learning and can help you create your first real-world AI application.
The course is split into three sections:
All these "sub-courses" provide a broad introduction to modern ML techniques, including supervised learning, unsupervised learning, and some of the best practices used in Silicon Valley for AL and ML. The course targets beginners, though some prior experience will be a bonus. It has a flexible schedule and lasts approximately 2 months (given that you study 10 hours a week). After completing the course, you'll earn a career certificate from Stanford University.
Whether you're entering the world of AI or building a career in machine learning, the Machine Learning Specialization course can be an ideal starting point.
What you'll learn:
How to build ML models with NumPy & scikit-learn
How to apply the best practices for ML development and use unsupervised learning techniques for unsupervised learning
How to build and train a neural network with TensorFlow to perform multi-class classification
How to build and use decision trees
How to create recommender systems with a collaborative filtering approach
"Introduction to Operating Systems" is an advanced graduate-level course designed to introduce you to the fascinating world of operating systems. Throughout this program, you'll delve into the fundamental abstractions, mechanisms, and implementations that form the backbone of modern operating systems.
The course centers around concurrent programming, threading, synchronization, and inter-process communication, offering invaluable insights into how different processes interact.
The course consists of 4 modules:
Introduction
Process and Thread Management
Resource Management and Communication
Distributed Systems
To undertake this course, you should have an intermediate skill level and be familiar with basic hardware and software aspects of computer systems organization. The course is self-paced and will take approximately 2 months.
What you'll learn:
Understand the rationale behind the current design and implementation decisions in modern OS's
Get theoretical knowledge regarding OS's principles and implementation
Gain knowledge via experimenting and evaluating various OS aspects in a practical manner
In our modern interconnected world, cyber-attacks leave nobody untouched. This course may be the perfect solution if you're concerned about safeguarding your or your company's sensitive data. It explores the latest cyber trends and threats, providing relevant and practical knowledge that resonates with nearly every situation.
With a focus on protecting your privacy in the digital realm, this course sheds light on the challenges that companies, governmental entities, educational institutions, and financial services face today.
No prerequisites are required, making this learning course accessible to all. The course lasts 15 hours and offers a mixed learning type: instructor-led and online self-paced. After completing the course, you'll get a badge.
What you'll learn:
What cybersecurity is and what potential impact it may have on you
The most common threats, attacks, risks, and vulnerabilities
Understand how businesses protect their systems
Find more about the latest job trends and why cybersecurity increasingly becomes popular
This foundational program is aimed at intermediate-level students who want to polish their deep learning and AI skills. It'll help you better understand the capabilities and challenges of deep learning and prepare you for participating in the development of AI technology.
The program offers students to build and train several neural network architectures, analyze data, implement optimization techniques, and use algorithms for advanced tasks.
The Deep Learning Specialization program consists of 5 courses:
As you see, the program is quite long, but it's well worth the effort. Plus, when you complete the program, you'll earn a career certificate from DeepLearning.AI and a college credit for participating in some U.S. colleges and universities.
What you'll learn:
How to build and train deep neural networks
How to implement vectorized neural networks
How to apply DL to your applications
How to train and develop test sets and analyze bias/variance for building DL applications
How to implement a neural network in TensorFlow
How to use best strategies for reducing errors in ML systems
How to build a Convolutional Neural Network
How to build and train Recurrent Neural Networks and its variables (GRUs, LSTMs), and many more
Those interested in cloud computing may like the Cloud Computing Specialization created by the University of Illinois. It covers numerous aspects of cloud computing systems, starting with core distributed systems concepts and ending with cloud apps and networking. The program is for intermediate-level students who want to enhance their knowledge in software-defined networking, big data, and distributed computing.
The program includes 6-course series:
Note that the program ends with a real-life project showcasing your practical experience and letting you apply the knowledge you've learned throughout the courses. Upon completing the course, you'll also get a shareable certificate that you'll be able to add to your LinkedIn profile, CV, or resume.
Ultimately, we'd like to present the program to help you enhance your knowledge of Java. The "Java Programming: Solving Problems with Software" course is created to improve your problem-solving skills. Also, you'll recall how to design algorithms and develop/debug programs.
The course consists of 5 modules:
Introduction to the course
Fundamental Java Syntax and Semantics
String in Java
CSV Files and Basic Statistic in Java
MiniProject: Baby Names
As you might have guessed, at the end of the course, you'll build a real-life app to add to your resume. Also, you'll get a career certificate you can add to your resume as well or share it on social media and in your performance review.
The course lasts approximately 17 hours and includes 11 quizzes, 5 assessments. No experience is required.
What you'll learn:
How to edit, compile, and run a Java app
How to use conditionals and loops
How to use Java API documentation
How to debug a Java program using the scientific method
How to develop a set of test cases
How to create a class with multiple methods
Conclusion
This selection of free computer science courses will interest you and help you enhance your Java knowledge with new skills.
These courses are not merely about learning code – they're about fostering problem-solving skills, nurturing creativity, and gaining a deeper understanding of technology's impact on our world. Ready for a new learning journey filled with innovation and discovery?
In this compilation of the best accessible computer science courses in 2023, we shed light on the high-quality educational online resources that cater to learners of all levels. So, if you're just about learning to program or want to refresh your existing knowledge, read on and make your choice!
Part 1 includes the following modules: Course Introduction, Union-Find, Analysis of Algorithms, Stacks and Queues, Elementary Sorts, Mergesort, Quicksort, Priority Queues, Elementary Symbol Tables, Balanced Search Trees, Geometric Application of BSTs, Hash Tables, Symbol Table Applications.
Part 2 includes the following modules: Introduction, Undirected Graphs, Directed Graphs, Minimum Spanning Trees, Shortest Paths, Maximum Flow and Minimum Cut, Radix Sorts, Tries, Substring Search, Regular Expressions, Data Compression, Reductions, Linear Programming, Intractability.
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