Top 7 Free Best Courses For Stanford University

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If you are a college student, you want to do a free course from a big university, then Stanford University has released some courses, so let’s see what are the courses, so let’s start

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1. Computer Science

CS101 is a self-paced course that teaches the essential ideas of Computer Science to a zero-prior-experience audience. Computers can appear very complicated, but in reality, computers work within just a few, simple patterns. CS101 demystifies and brings those patterns to life, which is useful for anyone using computers today.

In CS101, participants play and experiment with short bits of “computer code” to bring to life the power and limitations of computers. Everything works within the browser, so there is no extra software to download or install. CS101 also provides general background on computers today: what is a computer, what is hardware, what is software, and what is the internet. Anyone who has the ability to use a web browser may be successful in this course

2. Designing Your Career

This online course uses a design thinking approach to help people of any age and academic background develop a constructive and effective approach to designing their vocation. This course is primarily comprised of 5 career-oriented vocational wayfinding concepts, illustrated through videos and expanded through personal reflections and exercises.

3. Introduction to Statistics

Stanford’s “Introduction to Statistics” teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning

4. Algorithms: Design and Analysis – Part 1

In this course, you will learn several fundamental principles of algorithm design. You’ll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You’ll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we’ll study how allowing the computer to “flip coins” can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?

5. Algorithms: Design and Analysis – Part 2

In this course, you will learn several fundamental principles of advanced algorithm design. You’ll learn the greedy algorithm design paradigm, with applications to computing good network backbones (i.e., spanning trees) and good codes for data compression. You’ll learn the tricky yet widely applicable dynamic programming algorithm design paradigm, with applications to routing in the Internet and sequencing genome fragments. You’ll learn what NP-completeness and the famous “P vs. NP” problem mean for the algorithm designer.  Finally, we’ll study several strategies for dealing with hard (i.e., NP-complete problems), including the design and analysis of heuristics.  Learn how shortest-path algorithms from the 1950s (i.e., pre-ARPANET!) govern the way that your Internet traffic gets routed today; why efficient algorithms are fundamental to modern genomics; and how to make a million bucks in prize money by “just” solving a math problem!

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6. R Programming Fundamentals

This course covers the basics of R: a free programming language and software environment used for statistical computing and graphics. R is widely used by data analysts, statisticians, and data scientists around the world. This course covers an introduction to R, from installation to basic statistical functions. You will learn to work with variable and external data sets, write functions, and hear from one of the co-creators of the R language, Robert Gentleman.

7. Cryptography

Cryptography is an indispensable tool for protecting information in computer systems. This course explains the inner workings of cryptographic primitives and how to correctly use them. Students will learn how to reason about the security of cryptographic constructions and how to apply this knowledge to real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two or more parties generate a shared secret key. We will cover the relevant number theory and discuss public-key encryption and basic key exchange. Throughout the course, students will be exposed to many exciting open problems in the field.

Today’s Thought

Your time is limited, so don’t waste it living someone else’s life. Don’t be trapped by dogma – which is living with the results of other people’s thinking.” – Steve Jobs
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