Oxford Study Abroad Programme
Online Courses Summer 2022
1 August - 19 August 2022
The Online Courses are the transition in light of the COVID-19 pandemic. The goal of these changes is to minimise the need to gather in large groups and spend prolonged time in close proximity with each other in spaces such as classrooms, dining halls, and residential buildings. Our actions are consistent with the recommendations of leading health officials on how to limit the spread of COVID-19 and are also consistent with similar decisions made by a number of our peer institutions.
Course 1 Artificial Intelligence and Machine Learning
This course provides an overview of machine learning techniques to explore, analyse, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. Our goal is to help you master all the core concepts of both technologies, such as Deep Learning, regression techniques, Machine Learning algorithms, etc. Moreover, during this online course, you will work on a number of real-time, industry-based projects, which will enhance your learning experience and provide you with hands-on experience.
All sessions start with a look at the wider implications of artificial intelligence on society, the economy and the world; and are followed with the technical sessions on the content below,
*Beyond linear models: polynomial and logit fits
*Classic ML algorithms
*Gaussian mixture models
*Natural language processing
Students should develop some skills and familiarity with the mathematical topics below. Knowledge of these topics may be acquired by the students before the course (as part of their previous studies).
Whilst this content is not essential for completing the current course, it should be regarded as core learning for students by the time they complete the course.
- What a matrix is: Matrix representation of data-sets
- Matrix operations: Addition (+), Subtraction (-), Multiplication (.), Transpose (T)
- The link between algebra and matrices: Expressing systems of algebraic equations in matrix form
- What is a ‘probability’?
- Different views of what a probability represents: Bayesian Vs. Frequentist view
- Operations on probabilities:’ AND’ and ‘OR’
- Definitions: ‘Statistical distribution’, ‘Sample Space’, ‘Random Variable’
- Discreet Vs. Continuous Random Variables and the relationship between them
- Expectation: Definition and use in valuing options
2.The course programming language - Python
Python Programming Language
- You do not need to be highly skilled at Python before starting the course.
- The majority of activities will require you to read and replicate existing code, but not to write new programmes.
We will be using the ‘Jupyter Notebook’ environment to write Python programmes.
- You will need access to your own Jupyter Notebook environment in order to complete the hands-on and practical workshop element of this course.
- Jupyter is free and widely available. But you will need to either install it on your own machine or otherwise access a public Jupyter environment.
Course 2 Data Science
This course introduces the students to the mathematical foundations of data analysis, as well as to visualising and analysing data with Python. There are three main parts to the course. The first third of the course introduces a statistical basis for data analysis, including means and variance of random variables, Bayes’ formula, the central limit theorem, linear regression, confidence intervals and hypothesis testing (z-test and t-test). The second third is about data cleaning and visualisation (including creating plots of various types), with many examples in Python. The final third introduces the basics of machine learning in data analysis, covering the k nearest neighbours algorithm, regression using machine learning and principal component analysis, with an emphasis both on mathematical understanding and the ability to utilise these methods in Python.
Familiarity with the basics of Python
Python 3.7 or 3.8 installed as a part of the Anaconda Python distribution of Data Science, or equivalent.
2.Mathematics and statistics:
Working knowledge of linear algebra, calculus, basic probability, and statistics
Course 3 Business and Management - Entrepreneurship and Innovation
This course introduces students to relevant concepts to provide an understanding of business environments and the issues affecting contemporary business. It focuses on the link between entrepreneurial theory and practice. It offers an introduction to some of the key areas of entrepreneurship research, and illustrates these theoretical insights with cases from a wide range of industries, sectors and countries.
Students will learn to reflect on the multifaceted nature of entrepreneurship and strengthen their critical thinking skills through discussion and practical exercises, including the development of a start-up business model. The combination of theory, methodology and practice will help students understand the challenges of entrepreneurship in the real world, achieving a complete view of this phenomenon and its different facets. This course sets the scene and provides a platform for future study as well as helps students to appreciate the interconnected nature of business organisations, the environment in which they operate, and the people involved. It offers undergraduate students and new graduates the opportunity to acquire key skills in management and finance.
Course 4 Future Cities and Public Policy
As the world becomes increasingly urbanized, societies face new and complex challenges arising from intense economic pressure, increased inequality, and environmental degradation. Beyond a traditional role of guiding land use and development projects, contemporary urban planners are responsible for promoting more competitive, inclusive, and ecological cities.
In this course, you will study cities through the lens of economic, social, and environmental sustainability. The course’s world-class faculty delivers state-of-the-art courses, personalized mentoring, and firsthand insight on projects they have conducted in the public, private, and non-profit sectors. By working with them and your peers, you’ll learn how to leverage knowledge of cities into forward-looking policy and action for advancing the goals of societies across the globe. Issues such as poverty, inequality, production and consumption in the urban communities of the future are key in the development of sustainable cities.
* Please note that the course description is indicative and may be subject to change.
First-year undergraduate students and above or equivalent
Standard Entry Requirements
At the time of joining the OSAP programme, you will normally be able to demonstrate an average grade, or equivalent academic experience, of:
• 2.8/4.0 GPA (US scale) or 70/100 Percentage Grade Level or 2:2 (UK scale)
* 1st-year undergraduates from some partner universities do not need to submit academic performance proof. For more details, please contact your home institution.
* Please note that for some of the courses, there are additional prerequisites. For further details, please contact us at email@example.com.
2.English language requirement
This requirement for proof of English proficiency is not required for applicants whose first language is English, those whose first language is not English but have been involved in a full-time degree-level academic programme at a university where English is the language of instruction, or those who have extensive experience working in a professional English-speaking environment. Otherwise, you will need to demonstrate proficiency by providing us with a recognised qualification. The majority of modules normally require a level of minimum IELTS 5.5 or equivalent. Please find more details below:
(1) IELTS: minimum 5.5 for an overall average
(2) TOEFL: minimum 85 for the overall score
(3) College English Test (CET)-4: minimum 425 (applicable to Chinese university applicants only.)
(4) College English Test (CET)-6: minimum 500 (applicable to Chinese university applicants only)
* For those applicants who have not taken the above tests by the time of application or have not been in a professional English-speaking environment for years, their English proficiency must be assessed through the virtual interview by the programme officer.
* The selection panel of the programme will consider the overall qualifications of each applicant.
We mainly work with our partner universities on the application work. To apply, please complete and submit the application form to your home institution, or click and complete the online application form here.
Students are advised to apply as early as possible due to competition for places.
Upon admission, students will be issued a Letter of Acceptance. The letter is commonly sent via email. Application processing time is 1-2 weeks.
An invoice for the programme fees will be attached to the acceptance letter, listing payment and bank details.
Your place on the summer school is confirmed as soon as your payment is received, and you will receive a receipt for your payment via email from the Programme Administrator.
For further information, please email firstname.lastname@example.org or call our direct line at +44(0)1865 512959. Our administrative staff will reply to you as soon as possible.
A limited number of scholarships (normally for university students) are available for those participants who have a competitive performance during the programme and in the home institution.
Oxford Study Abroad Programme has contracts with the Colleges of Oxford University for the use of facilities and also contracts with lectures and professors from Oxford University on our courses. OSAP is not affiliated with the University of Oxford in any way; it is funded by Oxford Study Abroad Foundation which is a non-for-profit organisation registered in England and Wales.