An Overview of Data Science
Data is of huge significance as it forms the quintessential building block of the Information age. In some circles Data is already becoming an alternative global currency. Data Science is an increasing important skill. A recent study by the McKinsey Global Institute concludes, "a shortage of the analytical and managerial talent necessary to make the most of Big Data is a significant and pressing challenge”.
Data science is an interdisciplinary field. It enables one to analyse, communicate and reformulate raw Data in order to extract conclusions about that information. Data Science ultimately allows us to use and work with Data in creative ways to generate value and give us a better and more profound understanding of the underlying context.
Data Science is already used in all sectors of industry and society to allow government and organisations to make better and more informed decisions and to verify or disprove existing models, processes and theories.
The course aims to introduce students to a blend of Data Science concepts and technologies in order to enable them to understand the everyday Data related issues, covering the Data Science Supply Chain from Data collection, to processing, analysis and visualisation.
Term Starts: 2nd October
Background Reading List
Cathy O'Neil and Rachel Schutt., Doing Data Science, O'Reilly, 2014
Russell Jurney., Agile Data Science, O'Reilly, 2013
Edward Tufte., The Visual Display of Quantitative Information, Graphics Press, 2013 (2nd ed)
Andy Kirk., Data Visualisation: A Handbook for Data Driven Design, Saga 2016
If you are planning to purchase books, remember that courses with too few students enrolled will be cancelled. The Department accepts no responsibility for books bought in anticipation of a course.
If you have enrolled on a course starting in the autumn, you can become a borrowing member of the Rewley House library from 1st September and we will try to ensure that as many titles as possible are available in the Library by the start of each term. If you are enrolled on a course starting in other terms, you can become a borrowing member once the previous term has ended.
All weekly class students may become borrowing members of the Rewley House Continuing Education Library for the duration of their course. Prospective students whose courses have not yet started are welcome to use the Library for reference. More information can be found on the Library website.
There is a Guide for Weekly Class students which will give you further information.
Availability of titles on the reading list (below) can be checked on SOLO, the library catalogue.
Students who register for CATS points will receive a Record of CATS points on successful completion of their course assessment.
To earn credit (CATS points) you will need to register and pay an additional £10 fee per course. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online.
Coursework is an integral part of all weekly classes and everyone enrolled will be expected to do coursework in order to benefit fully from the course. Only those who have registered for credit will be awarded CATS points for completing work at the required standard.
Students who do not register for CATS points during the enrolment process can either register for CATS points prior to the start of their course or retrospectively from between January 1st and July 31st after the current academic year has been completed. If you are enrolled on the Certificate of Higher Education you need to indicate this on the enrolment form but there is no additional registration fee.
Course Fee: £225.00
Take this course for CATS points: £10.00
Dr Sepi Chakaveh is Senior Associate Tutor (Data Science) at the Oxford University Department for Continuing Education. She has a degree in Electronic Engineering & a PhD in Experimental Astrophysics & Space Sciences. Sepi has been teaching & researching Dynamic Objects & Data convergence. She is co-founder & the former director of Southampton Data Science Academy.
The course will include a mix of lectures, and hands-on exercises which will allow students to gain experience using the theory and techniques delivered in the lectures in the field of Data Science at large.
• Learning about the Nature of Data & Data Science enabling technologies
• Introduction the Applied Data Science Pipeline Process
• Expolring Data Visualisation & its application is Data Science Usecase Scenarios
The course will be mostly theory based, with a few hands-on introductory practical sessions in Data Wrangling & Data visuliastions tools.
By the end of this course students will be expected to:
The course aims to provide an introduction to various topics such as Big Data, data visualisation, advanced databases and cloud computing, along with a toolkit to use with data.
The students will need to complete a written assessment on the last session of the course.
Students must submit a completed Declaration of Authorship form at the end of term when submitting your final piece of work. CATS points cannot be awarded without the aforementioned form.
To earn credit (CATS points) for your course you will need to register and pay an additional £10 fee for each course you enrol on. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online.
Please use the 'Book' or 'Apply' button on this page. Alternatively, please complete an application form.
Level and demands
No or very little background knowledge or experience is required for this course.
Most of the Department's weekly classes have 10 or 20 CATS points assigned to them. 10 CATS points at FHEQ Level 4 usually consist of ten 2-hour sessions. 20 CATS points at FHEQ Level 4 usually consist of twenty 2-hour sessions. It is expected that, for every 2 hours of tuition you are given, you will engage in eight hours of private study.
Terms and conditions
Terms and conditions for applicants and students on this course
Sources of funding
Information on financial support