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Hello, I'm Sergei. I'm a Data Scientist with a broad professional background, master degrees in Physics and Economics and a professional certificate in accountancy from ACCA. For a number of years I was developing management reporting and controlling systems in corporations (including at C-level), have experience in team leading and project management and continue professional development in Data Science and Machine Learning with ambition to build AI systems that could change the way we do business.

I've created this web page to keep a detailed log of my progress in gaining a solid knowledge in CS and DS initially guided by the OSSU CS Curriculum, but subsequently expanded to other extra curriculum activities. It may serve as example for other people looking for an online equvalent of a degree in CS and DS.

The OSSU describes its Curriculum as:

... a complete education in computer science using online materials. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines. It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc.

The OSSU CS Curriculum is designed into the following sections (with a quick ref link to each of them):

Intro CS - for students to try out CS and see if it's right for them
Core CS - corresponds roughly to the first three years of a computer science curriculum, taking classes that all majors would be required to take
Advanced CS - corresponds roughly to the final year of a computer science curriculum, taking electives according to the student's interests
Projects - to validate, consolidate, and display the knowledge

With my progress in CS, I've got a particular interest in Data Science and Machine Learning and complemented the original CS Curriculum with some of the best available online and offline DS courses. Therefore, I've added the whole new section on Data Science and Machine Learning, which comes the first below.

Topics covered: imperative programming procedural programming C manual memory management basic data structures and algorithms Python SQL basic HTML, CSS, JavaScript and more

Topics covered: functional programming design for testing program requirements common design patterns unit testing object-oriented design Java static typing dynamic typing ML-family languages (via Standard ML) Lisp-family languages (via Racket) Ruby and more

Note: I stick to Python and its ecosystem so far in my current projects. In CS50, I also learned basics of C and Web stack (HTML/CSS/JS/jQuery+Python/Flask/Jinja+SQLite). Courses in this section teach programming concepts with such languages as Lisp-family, Java, ML, Ruby, Haskell, Prolog. I prioritize them as 'nice to have', but after I've covered other topics of the Curriculum.

Topics covered: linear transformations matrices vectors mathematical proofs number theory differential calculus integral calculus sequences and series discrete mathematics basic statistics O-notation graph theory vector calculus discrete probability and more

Note: I decided to opt out from courses 012-014 on Culculus and Linear Algebra with credits from my Master degree in Physics. MIT's materials from 015 "Math for CS" I use for reference purposes. I also added refresher courses in Statistics - the more Statistics, the better :). E.g. an excellent 'Statistical Reasoning' course by Stanford Online from the OSSU Data Science Curriculum.

Topics covered: boolean algebra gate logic memory computer architecture assembly machine language virtual machines high-level languages compilers operating systems network protocols and more

Topics covered: divide and conquer sorting and searching randomized algorithms graph search shortest paths data structures greedy algorithms minimum spanning trees dynamic programming NP-completeness and more

Note: (4 Algorithms courses by Stanford on Coursera == 2 Algorithms courses by Stanford on Lagunita)

Topics covered: Agile methodology REST software specifications refactoring relational databases transaction processing data modeling neural networks supervised learning unsupervised learning OpenGL raytracing block ciphers authentication public key encryption and more