Greetings of the day, Readers! I hope you all are safe and cooperating with the administration to fight this pandemic together. Well, this drastic transition from spending a good 9 hours in office meeting the strict deadlines of working from home at our comfort has transformed the work-culture in the IT industry.
This lockdown is certainly more painful for the students and to-be professionals with their stuck-in-middle offer letters and partly confirmed call letters. But, if we look at the brighter side, you've got a great deal of time to upgrade yourself with the technical skills you need to excel in the corporate world.
What could be better than the most esteemed Ivy League institutes across the world? The top Ivy League Schools that we will be considering in this article includes:
- Harvard University
- Princeton University
- Columbia University
- University of Pennsylvania
- Dartmouth
With this, we will further discuss the Top 60 Computer Science Courses/Classes which these world-class institutes offer to quench our thirst for knowledge and excellence.
Harvard University
The best computer science courses offered by Harvard University are;
1. CS50's Introduction to Computer Science
An introduction to the intellectual enterprises of computer science and the art of programming.
Course rating: 1,734,731 Total Enrollments
In this course, you will learn abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development.
Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming.
You can take CS50's Introduction to Computer Science Certificate Course on Edx.
2. CS50's Computer Science for Business Professionals
This is CS50’s introduction to computer science for business professionals.
Course rating: 57,309 Total Enrollments
In this course, you will be emphasizing mastery of high-level concepts and design decisions related thereto.
Through lectures on computational thinking, programming languages, internet technologies, web development, technology stacks, and cloud computing, this course empowers you to make technological decisions even if not a technologist yourself.
3. CS50's AP® Computer Science Principles
This is CS50 AP, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for students in high school, which satisfies the College Board's new AP CS Principles curriculum framework.
4. CS50's Understanding Technology
This is CS50’s introduction to technology for students who don’t (yet!) consider themselves computer persons.
Course rating: 36,519 Total Enrollments
This course fills in the gaps, empowering you to use and troubleshoot technology more effectively. Through lectures on hardware, the Internet, multimedia, security, programming, and web development, this course equips you for today’s technology and prepares you for tomorrow’s as well. internet multimedia security web development programming
5. CS50 for Lawyers
This course is a variant of Harvard University's introduction to computer science, CS50, designed especially for lawyers (and law students).
Course rating: 25,360 Total Enrollments
This course empowers students to be informed contributors to technology-driven conversations, through a mix of technical instruction and discussion of case studies,
In addition, it prepares students to formulate technology-informed legal arguments and opinions. Along the way, it equips students with hands-on experience with Python and SQL, languages via which they can mine data for answers themselves.
6. Statistics and R
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
Course rating: 242,136 Total Enrollments
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. You will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code.
7. Data Science: R Basics
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
Course rating: 368,032
This course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you'll use a real-world dataset about crime in the United States.
8. Data Science: Visualization
Learn basic data visualization principles and how to apply them using ggplot2.
Course rating: 108,128
This course covers the basics of data visualization and exploratory data analysis. You will use three motivating examples and ggplot2, a data visualization package for the statistical programming language R.
9. High-Dimensional Data Analysis
A focus on several techniques that are widely used in the analysis of high-dimensional data.
Course rating: 54,818 Total Enrollments
In this course, you will start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of high-dimensional data sets, and multi-dimensional scaling and its connection to principal component analysis.
10. Data Science: Machine Learning
Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.
Course rating: 136,047 Total Enrollments
In this course, which is a part of the Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, and how to use a set of data to discover potentially predictive relationships.
11. Data Science: Linear Regression
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.
Course rating: 45,515 Total Enrollments
This course, part of our Professional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R.
12. Causal Diagrams: Draw Your Assumptions Before Your Conclusions
Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference.
Course rating: 33,167 Total Enrollments
In this course, you will :
This course introduces you to causal DAGs, a type of causal diagrams, and the rules that govern them and using causal DAGs to represent common forms of bias. Also, the uses causal DAGs to represent time-varying treatments and treatment-confounder feedback, as well as the bias of conventional statistical methods for confounding adjustment.
13. Data Science: Wrangling
Learn to process and convert raw data into formats needed for analysis.
Course rating: 37,094 Total Enrollments
In this course, you will cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining.
14. Data Science: Productivity Tools
Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.
Course rating: 38,066 Total Enrollments
This course explains how to use Unix/Linux as a tool for managing files and directories on your computer and how to keep the file system organized. You will be introduced to the version control systems git.
15. Data Science: Probability
Course rating: 82,472 Total Enrollments
You will be introduced to important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem.
16. Data Science: Inference and Modeling
Learn inference and modeling, two of the most widely used statistical tools in data analysis.
Course rating: 50,478 Total Enrollments
This course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we'll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors.
17. Data Science: Capstone
Show what you've learned from the Professional Certificate Program in Data Science.
Course rating: 31,133 Total Enrollments
By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.
18. Principles, Statistical and Computational Tools for Reproducible Data Science
Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.
Course rating: 22,871 Total Enrollments
This course is really for anyone who is doing any data intensive research. While many of us come from a biomedical background, this course is for a broad audience of data scientists.
19. Using Python for Research
Take your introductory knowledge of Python programming to the next level and learn how to use Python 3 for your research.
Course rating: 164,346 Total Enrollments
This course bridges the gap between introductory and advanced courses in Python. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings.
20. CS50's Introduction to Game Development
Learn about the development of 2D and 3D interactive games in this hands-on course, as you explore the design of games such as Super Mario Bros., Pokémon, Angry Birds, and more.
Course rating: 105,583 Total Enrollments
In this course, you will understand how video games themselves are implemented and explore the design of such childhood games as Super Mario Bros. Pong Flappy Bird Breakout Match 3 Legend of Zelda Angry Birds Pokémon.
21. Quantitative Methods for Biology
Learn introductory programming and data analysis in MATLAB, with applications to biology and medicine.
Course rating: 11,300 Total Enrollments
In this course, you will study alongside students who are also learning to code. For expert programmers, this course has a will help you learn the MATLAB you need without getting slowed down by introductory concepts that you already know.
Princeton University
22. Algorithms, Part I
Learn Algorithms, Part I from Princeton University. This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis.
Course rating: 4.9 out of 5.0 ( 6141 Ratings total)
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
23. Algorithms, Part II
Learn Algorithms, Part II from Princeton University. This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis.
Course rating: 5.0 out of 5.0 ( 1033 Ratings total)
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations.
24. Bitcoin and Cryptocurrency Technologies
Learn Bitcoin and Cryptocurrency Technologies from Princeton University. To really understand what is special about Bitcoin, we need to understand how it works at a technical level.
Course rating: 4.7 out of 5.0 ( 2105 Ratings total)
After this course, you’ll know everything you need to be able to separate fact from fiction when reading claims about Bitcoin and other crypto-currencies. You’ll have the conceptual foundations you need to engineer secure software that interacts with the Bitcoin network.
25. Software Defined Networking
Learn Software Defined Networking from The University of Chicago. In this course, you will learn about software-defined networking and how it is changing the way communications networks are managed, maintained, and secured.
Course rating: 4.7 out of 5.0 ( 27 Ratings total)
In this course, you will learn about software-defined networking and how it is changing the way communications networks are managed, maintained, and secured.
26. Computer Architecture
Learn Computer Architecture from Princeton University. In this course, you will learn to design the computer architecture of complex modern microprocessors.
Course rating: 4.8 out of 5.0 ( 473 Ratings total)
In this course, you will learn to design the computer architecture of complex modern microprocessors. It gives you a description of architecture, micro-architecture and instruction set architectures.
27. Analysis of Algorithms
Learn Analysis of Algorithms from Princeton University. This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures.
Course rating: 4.7 out of 5.0 ( 71 Ratings total)
This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.
28. Networks Illustrated: Principles without Calculus
Learn Networks Illustrated: Principles without Calculus from Princeton University. What makes WiFi faster at home than at a coffee shop? How does Google order its search results from the trillions of webpages on the Internet?
Course rating: 4.4 out of 5.0 ( 49 Ratings total)
This course is about exploring the answers, using a language that anyone can understand. We will focus on fundamental principles like “sharing is hard”, “crowds are wise”, and “network of networks” that have guided the design and sustainability of today’s networks, and summarize the theories behind everything from the social connections we make on platforms like Facebook to the technology upon which these websites run.
29. Networks: Friends, Money, and Bytes
Learn Networks: Friends, Money, and Bytes from Princeton University. You pick up your iPhone while waiting in line at a coffee shop.
Course rating: 4.8 out of 5.0 ( 90 Ratings total)
This course is about formulating and answering these 20 questions. 20 practical questions and their answers, about your networked life. We study cellular network technology, the air interface between end-user devices and base stations, and an important algorithm that has been developed to manage interference between our devices as they share this medium: Distributed Power Control.
30. Computer Science: Algorithms, Theory, and Machines
Learn Computer Science: Algorithms, Theory, and Machines from Princeton University.
Course rating: 4.8 out of 5.0 ( 157 Ratings total)
This course introduces the broader discipline of computer science to people having basic familiarity with Java programming. It covers the second half of the book Computer Science: An Interdisciplinary Approach. The intent is to demystify computation and to build awareness about the substantial intellectual underpinnings and rich history of the field of computer science.
31. Computer Science: Programming with a Purpose
Learn Computer Science: Programming with a Purpose from Princeton University. The basis for education in the last millennium was “reading, writing, and arithmetic;” now it is reading, writing, and computing.
Course rating: 4.8 out of 5.0 ( 151 Ratings total)
This course covers the first half of the book Computer Science: An Interdisciplinary Approach. This course begins by introducing basic programming elements such as variables, conditionals, loops, arrays, and I/O. Next, it turns to functions, introducing key concepts such as recursion, modular programming, and code reuse. Then, we present a modern introduction to object-oriented programming.
Columbia University
32. Machine Learning for Data Science and Analytics
Learn the principles of machine learning and the importance of algorithms.
Course rating: 131,058 enrollments total
This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.
33. Machine Learning
Master the essentials of machine learning and algorithms to help improve learning from data without human intervention.
Course rating: 142,255 enrollments total
In this course, you will learn the models and methods and apply them to real-world situations ranging from identifying trending news topics to building recommendation engines, ranking sports teams and plotting the path of movie zombies. Major perspectives covered include: probabilistic versus non-probabilistic modeling supervised versus unsupervised learning
34. Artificial Intelligence (AI)
Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.
Course rating: 249,728 enrollments total
This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.
35. Enabling Technologies for Data Science and Analytics: The Internet of Things
Discover the relationship between Big Data and the Internet of Things (IoT).
Course rating: 69,444 enrollments total
In this data science course, you will learn about the major components of the Internet of Things and how data is acquired from sensors. You will also examine ways of analyzing event data, sentiment analysis, facial recognition software and how data generated from devices can be used to make decisions.
36. Animation and CGI Motion
Learn the science behind movie animation from the Director of Columbia’s Computer Graphics Group.
Course rating: 33,039 enrollments total
This course will show you how to create lifelike animations focusing on the technical aspects of CGI animation and also give you a glimpse into how studios approach the art of physically-based animation. You will learn the fundamental concepts of physical simulation, including the integration of ordinary differential equations such as those needed to predict the motion of a dress in the wind.
37. HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Social/Peer Perspective)
Learn HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Social/Peer Perspective) from Columbia University. HI-FIVE (Health Informatics For Innovation, Value & Enrichment) Training is an approximately 10-hour online course designed ...
Course rating: 4.6 out of 5.0 ( 18 Ratings total)
HI-FIVE (Health Informatics For Innovation, Value & Enrichment) Training is an approximately 10-hour online course designed by Columbia University in 2016, with sponsorship from the Office of the National Coordinator for Health Information Technology (ONC).
38. Statistical Thinking for Data Science and Analytics
Learn how statistics plays a central role in the data science approach.
Course rating: 189,160 erollments total
This statistics and data analysis course will pave the statistical foundation for our discussion on data science. You will learn how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for data-based decisions and construct models for predicting future trends from data.
39. Big Data and Education
Learn the methods and strategies for using large-scale educational data to improve education and make discoveries about learning.
Course rating: 12,335 enrollments total
In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in the educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities.
40. Data, Models, and Decisions in Business Analytics
Learn fundamental tools and techniques for using data towards making business decisions in the face of uncertainty.
Course rating: 32,322 enrollments total
The main objectives of this course are the following: Introduce fundamental techniques towards a principled approach for data-driven decision-making. Quantitative modeling of the dynamic nature of decision problems using historical data, and Learn various approaches for decision-making in the face of uncertainty.
University of Pennsylvania
41. Robotics: Perception
Learn Robotics: Perception from the University of Pennsylvania. How can robots perceive the world and their own movements so that they accomplish navigation and manipulation tasks?
Course rating: 4.4 out of 5.0 ( 524 Ratings total)
In this course, you will study how images and videos acquired by cameras mounted on robots are transformed into representations like features and optical flow. You will come to understand how grasping objects is facilitated by the computation of 3D posing of objects and navigation can be accomplished by visual odometry and landmark-based localization.
42. Algorithm Design and Analysis
Learn about the core principles of computer science: algorithmic thinking and computational problem-solving.
Course rating: 35,694 enrollments total
This course, part of the Computer Science Essentials for Software Development Professional Certificate program, is an introduction to the design and analysis of algorithms and answers along the way these and many other interesting computational questions. You will learn about algorithms that operate on common data structures, for instance, sorting and searching; advanced design and analysis techniques such as dynamic programming and greedy algorithms.
43. Robotics: Vision Intelligence and Machine Learning
Learn how to design robot vision systems that avoid collisions, safely work with humans and understand their environment.
Course rating: 32,663 enrollments total
In this course, you will understand how Machine Learning extracts statistically meaningful patterns in data that support classification, regression and clustering. Then by studying Computer Vision and Machine Learning together, you will be able to build recognition algorithms that can learn from data and adapt to new environments.
44. Cryptocurrency and Blockchain: An Introduction to Digital Currencies
Learn Cryptocurrency and Blockchain: An Introduction to Digital Currencies from the University of Pennsylvania.
Course rating: 6,983 enrollments total)
This course was designed for individuals and organizations who want to learn how to navigate investment in cryptocurrencies. You’ll learn how to define a currency, analyze the foundations of digital signatures and blockchain technology in cryptocurrency, and accurately assess the risks of cryptocurrency in a modern investment portfolio.
45. Data Structures and Software Design
Learn how to select, apply, and analyze the most appropriate data representations in your code and design high-quality software that is easy to understand and modify.
Course rating: 25,501 enrollments total
This course, part of the CS Essentials for Software Development Professional Certificate program, will take your skills to the next level by teaching you how to write “good” software that appropriately represents and organizes data, is easy to maintain, and is of high quality. In this course, you will learn about important core data structures such as arrays, lists, stacks, queues, sets, maps, trees, and graphs, and learn how to evaluate them and reason about their behavior and efficiency.
46. Computational Thinking for Problem Solving
Learn Computational Thinking for Problem Solving from the University of Pennsylvania. Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution.
Course rating: 27,197 enrollments total
In this course, you will learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language. By the end of the course, you will be able to develop an algorithm and express it to the computer by writing a simple Python program.
47. People Analytics
Learn People Analytics from the University of Pennsylvania. People analytics is a data-driven approach to managing people at work.
Course rating: 79,323 enrollments total)
In this course, you will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies. This course is an introduction to the theory of people analytics and is not intended to prepare learners to perform complex talent management data analysis.
48. Programming for the Web with JavaScript
Learn how to develop dynamic, interactive, and data-driven web apps using JavaScript.
Course rating: 72,740 enrollments total
This course, part of the CS Essentials for Software Development Professional Certificate program, provides an introduction to modern web development using JavaScript. In addition to exploring the basics of web page creation using HTML and CSS, you will learn advanced web page layout and responsive design tools such as Bootstrap.
49. Software Development Fundamentals
Learn the fundamentals of object-oriented programming in Java, as well as the best practices of modern software development.
Course rating: 45,176 enrollments total
This course, part of the CS Essentials for Software Development Professional Certificate program, will quickly cover Java syntax and keywords and then explore features of object-oriented programming including encapsulation, inheritance, and polymorphism.
Dartmouth
50. Linux Basics: The Command Line Interface
Learn the Linux Command Line interface and become a skilled user of this powerful operating system.
Course rating: 27,368 enrollments total
In this course, you will :
This course will introduce you to Linux, a powerful operating system used by most professional developers! In this course, you will learn the history of Linux and how its open source community was able to create today’s most advanced operating system. You will navigate the file system, use fundamental Linux commands and master the Linux command-line interface.
51. C Programming: Modular Programming and Memory Management
Enhance your coding skills along your path to becoming a proficient C programmer with the essential concepts of functions and pointers. Receive instant feedback on your code right within your browser.
Course rating: 15,037 enrollments total
In this course, part of the C Programming with Linux Professional Certificate program, you will be introduced to the concept of modular programming: that is, dividing up more complex tasks into manageable pieces. Within moments you will be coding hands-on in a new browser tool developed for this course, receiving instant feedback on your code.
52. C Programming: Pointers and Memory Management
Continue building your coding skills in your path to becoming a proficient C programmer by mastering the concept of pointers and memory management. Receive instant feedback on your code right within your browser.
Course rating: 15,988 enrollments total
In this course, we will examine a key concept, foundational to any programming language: the usage of memory. This course builds upon the basic concept of pointers, discussed in C Programming: Modular Programming and Memory Management, and introduces the more advanced usage of pointers and pointer arithmetic.
53. C Programming: Using Linux Tools and Libraries
Learn how to use professional tools and libraries to write and build C programs within the Linux operating system. Receive instant feedback on your code right within your browser.
Course rating: 11,728 enrollments total
This course in the C Programming with Linux Professional Certificate program will allow you to develop and use your C code within the Linux operating system. Using libraries in C is a fundamental concept when it comes to sharing code with others. In addition to compiling and linking, you will also learn how to pass arguments to an executable program.
54. C Programming: Language Foundations
Master foundational concepts in the C programming language such as logical statements and arrays.
Course rating: 36,402 enrollments total
In this course, part of the C Programming with Linux Professional Certificate program, you will learn to use logical statements and arrays in C. Logical statements are used for decision-making with follow-up instructions, based on conditions you define.
55. C Programming: Getting Started
Start learning one of the most powerful and widely used programming languages: C.
Course rating: 84,993 enrollments total
In this course, you will learn the principles of C programming and start coding hands-on in a browser tool that will provide instant feedback on your code. The C programming language is one of the most stable and popular programming languages in the world.
Summing-Up
Thus, we conclude our listing of 55 best Ivy League courses which you must consider taking to utilize your quarantine days in upskilling yourselves. In case you want me to write articles on any specific technology, do comment below and tell me how it was!
Glad to see, that you have made it till the end. If this article added some value to your learning or if you liked it then like, upvote and share it in your network.
Also, I would love to hear any feedback and review from you. Please tell me what you liked in the comment section below. Happy Learning!✨
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