Stanford University, a world leader in technological innovation, is offering a fantastic opportunity for you to upskill for free with their free IT courses IT students. This exciting program provides a chance to learn from renowned Stanford faculty and gain valuable knowledge in the ever-evolving field of IT – all without breaking the bank. In this blog post, we’ll delve into the details of these free IT courses, including the application process and the potential benefits for your IT career.
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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 Jr.[2] 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’s death 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).
Eligibility Criteria Of free IT courses IT students
These free IT courses are for IT students and CS graduates.
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Here Are Stanford University’s free IT courses IT students
1. Databases: Modeling and Theory
This course is one of five self-paced courses on the topic of Databases, originating as one of Stanford’s three inaugural massive open online courses released in the fall of 2011. The original “Databases” courses are now all available on edx.org.
This course covers underlying principles and design considerations related to databases; it can be taken either before or after taking other courses in the Databases series.
- The Relational Algebra section of this course teaches the algebraic query language that provides the formal foundations of SQL.
- The Relational Design Theory section of the course provides comprehensive coverage of dependency theory and normal forms in relational databases, a well-accepted theoretical framework for developing good relational database schemas.
- The Unified Modeling Language section of this course introduces the data-modeling component of UML, and describes how UML diagrams are translated to relational database schemas.
The introductory videos in this course are the same as the introductory videos in Databases: Relational Databases and SQL ; they are included for the benefit of learners who have not taken Databases: Relational Databases and SQL.
Course Link Click Here
2. Databases: Advanced Topics in SQL
This course is one of five self-paced courses on the topic of Databases, originating as one of Stanford’s three inaugural massive open online courses released in the fall of 2011. The original “Databases” courses are now all available on edx.org.
This course is broad and practical, covering indexes, transactions, constraints, triggers, views, and authorization, all in the context of relational database systems and the SQL language. This course builds on concepts introduced in Databases: Relational Databases and SQL and is recommended for learners seeking to advance their understanding and use of relational databases.
The Indexes and Transactions section of this course covers two important features of database systems from the application-builder’s perspective: indexing for increased performance, and transactions for concurrency control and failure recovery.
The Constraints and Triggers section of this course explains key, referential integrity, and “check” constraints, followed by comprehensive coverage of database triggers.
The Views and Authorization section of this course provides extensive coverage of how database views can be created, used, and updated, and introduces standard techniques for authorization in relational databases.
Course Link Click Here
3. Statistical Learning with Python
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 in this course is done in Python.
There are lectures devoted to Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chatper. We also offer the separate and original version of this course called Statistical Learning with R – the chapter lectures are the same, but the lab lectures and computing are done using R.
The lectures cover all the material in An Introduction to Statistical Learning, with Applications in Python by James, Witten, Hastie, Tibshirani, and Taylor (Springer, 2023.
Course Link Click Here
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4. Machine Learning Full Course
This course provides an introduction to the field of machine learning, covering basic models such as linear regression and logistic regression, as well as more advanced techniques such as support vector machines (SVMs) and neural networks.
Topics also include decision trees, ensemble methods, expectation-maximization algorithms, independent component analysis, reinforcement learning and linear dynamical systems. It also covers topics such as data splits, model selection and cross-validation, debugging, and diagnostics. Finally, the lectures further explore the fundamentals of machine learning, such as gradient descent, moments, and optimization, in order to lay the foundations for advanced machine learning topics.
Course Link Click Here
