Ever dreamed of learning from Stanford University professors? In this post, we’ll explore six of the most exciting free courses Stanford has to offer. Whether you’re a tech enthusiast, a career-minded individual, or simply someone curious to expand your knowledge, there’s something here for everyone.
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Table of Contents
About the Stanford University
Stanford University (officially Leland Stanford Junior University) is a private research university in Stanford, California. It was founded in 1885 by Leland Stanford—a railroad magnate who served as the eighth governor of and then-incumbent senator from California—and his wife, Jane, in memory of their only child, Leland. Stanford has an 8,180-acre (3,310-hectare) campus, among the largest in the nation.
The university admitted its first students in 1891, opening as a coeducational and non-denominational institution. It struggled financially after Leland died in 1893 and again after much of the campus was damaged by the 1906 San Francisco earthquake. Following World War II, Frederick Terman, the university’s provost, inspired and supported faculty and graduates’ entrepreneurialism to build a self-sufficient local industry, which would later be known as Silicon Valley.
The university is organized around seven schools on the same campus. It also houses the Hoover Institution, a public policy think-tank. Students compete in 36 varsity sports, and the university is one of two private institutions in the Pac-12 Conference. Stanford has won 131 NCAA team championships, more than any other university, and was awarded the NACDA Directors’ Cup for 25 consecutive years, beginning in 1994. Stanford students and alumni have won at least 296 Olympic medals (including 150 gold)
Here Are 6 Top Free Courses Offered By Stanford University
1. Algorithms: Design and Analysis
Welcome to the self-paced course, Algorithms: Design and Analysis! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth.
This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.
Specific topics in the course include: “Big-oh” notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), randomized algorithms (QuickSort, contraction algorithm for min cuts), data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of BFS and DFS, connectivity, shortest paths).
Learners will practice and master the fundamentals of algorithms through several types of assessments. There are 6 multiple choice quizzes to test your understanding of the most important concepts. There are also 6 programming assignments, where you implement one of the algorithms covered in lecture in a programming language of your choosing. The course concludes with a multiple-choice final. There are no assignment due dates and you can work through the course materials and assignments at your own pace.
Course Link Click Here
2. Communicating with Presence
Internet Enduring Material sponsored by Stanford University School of Medicine. Presented by Primary Care and Population Health at Stanford University School of Medicine.
Modern society has provided us with technological advantages but also barriers to connect with our patients in a meaningful way. This online module highlights the Stanford Presence 5 model, which is a framework for integrating humanism and effective communication as you interact with your patients.
Intended Audience
Specialties – All Specialties
Professions – Non-Physician, Physician
Accreditation
In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.
Credit Designation
American Medical Association (AMA)
Stanford Medicine designates this Enduring Material for a maximum of 0.25 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Course Link Click Here
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3. Databases: Advanced Topics in SQL
Databases” was one of Stanford’s three inaugural massive open online courses in the fall of 2011. It has been offered in synchronous and then in self-paced versions on a variety of platforms continuously since 2011. The material is now being offered as a set of five self-paced courses, which can be taken in a variety of ways to learn about different aspects of databases.
Relational Databases and SQLÂ is the most popular course in the Databases series. It is applicable to learners seeking to gain a strong understanding of relational databases and to master SQL, the long-accepted standard query language for relational database systems. Additional courses focus on advanced concepts in relational databases and SQL, formal foundations and database design methodologies, and semistructured data.
All of the courses are based on video lectures and demos. Many of them include quizzes between video segments to check understanding, in-depth standalone quizzes, and/or a variety of automatically checked interactive exercises. Each course also includes an unmoderated discussion forum and pointers to readings and resources. The courses are described briefly below. Taught by Professor Jennifer Widom, the overall curriculum draws from Stanford’s popular longstanding Databases course.
Course Link Click Here
4. Statistical Learning with R
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. We also offer a separate version of the course called Statistical Learning with Python – the chapter lectures are the same, but the lab lectures and computing are done using Python.
Course Link Click Here
5. 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.
What you’ll learn?
We will cover:
1. How to download and install R.
2. How to use R in an interactive and easy-to-understand environment.
3. What the key objects are and how we manipulate them in R.
4. Where the objects are stored and how to save our work.
5. All the important data structures: data frames, lists, matrices.
6. How to import data into R and how to save your work.
7. How to manipulate and preprocess data and work with missing values.
8. How to plot your data – an introduction to ggplot2.
9. How to use the wealth of contributed packages to achieve a specific task.
Course Link Click Here
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6. Databases: Relational Databases and SQL
Databases” was one of Stanford’s three inaugural massive open online courses in the fall of 2011. It has been offered in synchronous and then in self-paced versions on a variety of platforms continuously since 2011. The material is now being offered as a set of five self-paced courses, which can be taken in a variety of ways to learn about different aspects of databases.
Relational Databases and SQLÂ is the most popular course in the Databases series. It is applicable to learners seeking to gain a strong understanding of relational databases and to master SQL, the long-accepted standard query language for relational database systems. Additional courses focus on advanced concepts in relational databases and SQL, formal foundations and database design methodologies, and semistructured data.
All of the courses are based on video lectures and demos. Many of them include quizzes between video segments to check understanding, in-depth standalone quizzes, and/or a variety of automatically checked interactive exercises. Each course also includes an unmoderated discussion forum and pointers to readings and resources. The courses are described briefly below. Taught by Professor Jennifer Widom, the overall curriculum draws from Stanford’s popular longstanding Databases course.
Why Learn About Databases
Databases are incredibly prevalent — they underlie technology used by most people every day if not every hour. Databases reside behind a huge number of websites; they’re a crucial component of telecommunications systems, banking systems, video games, and just about any other software system or electronic device that maintains some amount of persistent information. In addition to persistence, database systems provide a number of other properties that make them exceptionally useful and convenient: reliability, efficiency, scalability, concurrency control, data abstractions, and high-level query languages. Databases are so ubiquitous and important that computer science graduates frequently cite their database class as the one most useful to them in their industry or graduate-school careers.
Course Link Click Here
