Sitting at home in quarantine? Want to learn something new?
We hope all of you are well, and that the time leading up to the exam term hasn't been
too stressful. We extend our well wishes for everyone affected by the pandemic, and a
thank you to everyone who has contributed in one way or another.While there might be
disruptions to Summer plans, now is a prime opportunity for learning and building on
your skills! At DSS, we've compiled a list of websites and courses that we think would be
beneficial for all of you.
NOTE: We are working actively with our science team and external education platforms
to secure an exclusive deals for DSS members, so please look out for further updates on
Data Science Resources
The following courses and resources can provide you with a solid knowledge
foundation in machine learning with theories and praticals.
Coursera - Machine Learning by Stanford
This is a great introductory course by the top lecturer at Stanford University and Coursera
Andrew Ng. It provides a rigorous approach towards the mathematical intuition behind
machine learning algorithms, with hand-on implementation.
Coursera - Data Science Specialisation by Johns Hopkins University
GitHub - inst0060-notes by Tony Wu
This is the course notes for INST0060: Foundation of Machine Learning and Data
Science module taught at the Department of Information Studies at the UCL, with
rigorous proof sketches to many math intuitions in Supervised Learning, Reinforment
Learning, Unsupervised Learning and Neuro Networks algorithms.
Mathematics, Statistics and Computer Science Resources
Data Science is a product of mathematics, statistics, computer science and
programming. These resources provide you with the essential toolsets for discovering
the mechanisms behind each algorithms from a mathematician, statistician and
computer scientist's perspective
MIT - MIT Open Course Ware (OCW)
A collection of the open courses provided at one of the best STEM-related universities:
MIT. The 18.0x series of introductory courses by the Mathematics Department and the
6.0x series of introductory courses by the Computer Science Department are highly
recommended. The courses cover a wider and deeper range of contents than some
similar courses taught at the UCL with courseworks, examinations, projects and sample
Some recommended courses:
18.02 Multivariable Calculus
18.03 Differential Equations
18.05 Introduction to Probability and Statistics
18.06 Linear Algebra
18.062J Mathematics for Computer Science
18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine
6.041SC Probabilistic Systems Analysis and Applied Probability
6.005 Software Construction
6.087 Practical Programming in C
6.088 Introduction to C Memory Management and C++ Object-Oriented
Professor Strang's lectures on 18.06 and 18.065 are frequently regarded as the
"best Linear Algebra course". His textbooks are used as the standard textbooks for
many univerisities around the world. He published also a new book in 2019 on the
linear algebra for machine learning. Machine Learning is highly related linear
algebra so these are definitely worth checking out
Coursera - Algorithms by Princeton University
This is a 2-part course on algorithms by Sedgwick and Wayne, professors at Princeton.
The supplementary reading Algorithms, 4th Ed is regarded as the Algorithms Bible by
many. The course provides you with a thorough introduction to many algorithms and the
mathematical proof sketches of the running time and memory usage efficiencies behind
each algorithm. The Java implementation of data structure and Sedgewick's interfacr-
orientated approach for Java Implementation is absolutely inspiring. Upon the
completion of the course, you will not only be able to implement a range of data
structures and algorithms on your own but also become a master in Java and the
mechanism behind it.
It's an interactive way to learning to coding. It provides live screencast so that you can
pause at anytime to play around with the freshly-written codes by the lecturer in real-
time in the browser with live rendering, compiling and interpreting enabled, which frees
you from setting up your local environment. Courses on front-end engineering, data
structure, and programming are avaible.