Applied Data Science
Data is of huge significance as it forms the quintessential building block of the Information age. Many believe that Data will be the next global currency. Data Science is the knowledge which will be the most demanding sector in the 21st century. 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 society to allow government and organisations to make better and more informed decisions and to verify or disprove existing models, processes and theories.
The Applied Data Science course is aimed to introduce students to sufficient blend of Data Science concepts and technologies in order to enable them to work with everyday Data related issues analytically. It will cover the technical supply chain of Data Science from Data collection, to processing, analysis and visualisation through innovative hands on practical practices.
Term Starts: 23rd January
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).
Paul Teetor, R Cookbook, O'Reilly, 2011
Morgan Kaufmann, Data Mining: Practical Machine Learning Tools and Techniques. 3 edition, 2011
Matthew Russell, Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. O'Reilly, 2013
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. 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.
Coursework is an integral part of all weekly classes and everyone enrolled will be expected to do coursework, but only those who have registered for credit will be awarded CATS points for completing work at the required standard. 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.
If you do not register when you enrol, you have up until the course start date to do so.
Course Fee: £215.00
Take this course for CATS points: £10.00
Dr Sepi Chakaveh 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.
To gain an understanding of:
• Big Data Phenomena
• Data Analytics
• Data visualisation
• Practicing user case scenarios in Big Data Analytics
The course will include a mix of lectures, and hands-on exercises and invited talks from expert data science practitioners.
By the end of the 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 (including D3, Google Refine and Hadoop/TensorFlow).
The students will need to complete a project which include understanding & applying all the above knowledge & techniques.
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
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