Tutor information
Vasos Pavlika
Dr Vasos Pavlika is Associate Professor (Education) at University College London, he also teaches Mathematics at the LSE (University of London), as well as Online at: SOAS, University of London (Mathematical Economics), Goldsmiths College (Computing and Data Science), University of London and the Open University (Applied Mathematics). He has been a lecturer in the Department for Continuing Education, Oxford since 2004.
Courses
C++ will be used to introduce object-oriented programming, commencing at an introductory level. We will move onto encapsulation, inheritance, polymorphism, software engineering, dynamic data allocation, recursion as well as the standard template library.
This course will introduce statistics to the beginner, covering measures of central tendency, dispersion, probability theory and inferential statistics.
A celebration of the latest advances in astronomy, with some of the best images from satellites, space probes and ground-based observatories. With world-leading experts, all are welcome at this popular and richly illustrated event.
We will look at the epoch-making work of Galileo and how he influenced Newton as well as the toppling of the Aristotelian world view. After Newton, we will look at more classical physicists and conclude with Relativity, Particle and Quantum Physics.
C++ will be used to introduce object-oriented programming, commencing at an introductory level. We will move onto encapsulation, inheritance, polymorphism, software engineering, dynamic data allocation, recursion as well as the standard template library.
Calculus is a 'sine qua non' for studying more advanced mathematics, physics, statistics, machine learning and data science. This course will introduce you to the vocabulary and techniques that open the panorama of science and engineering.
Linear algebra and its matrices appear throughout the sciences and in the mathematical parts of the social sciences. This basic course is a prerequisite to understanding advanced mathematics and myriad closely and distantly related quantitative fields.