Data analytics (Big Data) is a major phenomenon in the 21st century, rendering traditional data processing applications inadequate and increasing the demand for data analysts trained in this area who can collate, interpret and draw value from complex data sets. This programme brings together a range of techniques that the modern data analyst needs. You will study blocks in mathematics, statistics, data analysis and computing, and tackle a variety of interesting and engaging problems from business and industry. A good grounding in all these subjects is essential for creating and using algorithms and systems that identify patterns and extract value from masses of data. The course will also develop key graduate skills such as problem-solving and communication, with a third of the credits at each level based on project-oriented work where students will develop their knowledge, professionalism and creativity in a supporting environment. As an example, in your second year you will be introduced to neural networks and deep learning. This important topic is at heart a powerful blend of linear algebra, nonlinear activation functions, vector calculus chain rule for gradients, and steepest descent optimisation with sampling. These fundamental building blocks will be brought together in theory and in software so that you will be able to build your own deep learning neural net, and be able to explain the function of every part of the algorithm. This last aspect of being able to explain the software’s function is key to the role of a mathematician as an understander as well as a user of methods, as opposed to just a consumer of software. The emphasis throughout will be on the practical rigour associated with getting deep learning to work. Follow the four-year ‘Professional Placement’ degree programme and you‘ll benefit from our extensive experience in helping students to find well-paid work placements with blue-chip companies. Our sandwich students find that their mathematical and transferable skills are in demand in many sectors. This programme strives to meet the educational requirements of the Chartered Mathematician designation, it is seeking an award by the Institute of Mathematics and its Applications. Once granted, when this course is followed by subsequent training and experience in employment to obtain equivalent competences to those specified by the Quality Assurance Agency (QAA) for taught master's degrees.

Year 1 Calculus - Elements of Applied Mathematics- Fundamentals of Mathematics - Linear Algebra - Probability and Statistics I - Programming and Mathematical Projects Year 2 Discrete Mathematics and Operational Research - Mathematics of Machine Learning - Multivariable Calculus - Probability and Statistics II - Professional Development and Project Work - Statistical Programming for Data Analytics Work placement year in industry Year 3 Compulsory modules : Experimental Design and Regression - Statistical Data Science and Machine Learning - Data Science Project Optional modules: Decision Making in the Face of Risk - Mathematical Finance - Stochastic Models

The Mathematics for Data Science BSc programme uses elements of formative and summative assessment. Although both forms of assessment will be graded, only the summative assessment will count for progression for your final degree. Our academics use formative assessment as a fundamental component in the learning process, including; class tests (both in paper and electronic format), electronic quizzes, and short written exercises. Summative assessments throughout this course consist of coursework and examinations. We base your final degree class on your performance in second and final year. Final year carries twice the weight of second year.

Professional Bodies are not listed for this Course.

26 January This is the deadline for applications to be completed and sent for this course. If the university or college still has places available you can apply after this date, but your application is not guaranteed to be considered.

Course code: G1NK

Institution code: B84

Campus Name: Main Site

Campus code:

The following entry points are available for this course:

Year 1

Entry Requirements for Advanced Entry are not listed for this Course.

Please click the following link to find out more about qualification requirements for this course

Minimum Further Information are not listed for this Course.

Test	Grade	AdditionalDetails
IELTS (Academic)	6.0	with no less than 5.5 in each subsection
Institution's Own Test		with no less than 55% in each subsection
TOEFL (iBT)	79.0	with a minimum of: Reading - 18 Listening - 17 Speaking - 20 Writing - 17
PTE Academic	51.0	with a minimum of 51 in all subscores

Brunel University London - English Language Requirements

https://www.brunel.ac.uk/international/English-Language-Requirements

Student satisfaction : 59%

Employment after 15 months (Most common jobs): 60%

Go onto work and study: 85%

Fees quoted are per year and may be subject to an annual increase. Home/EU undergraduate student fees are regulated and are currently capped at £9,250 per year; any changes will be subject to changes in government policy. International fees will increase annually, by no more than 5% or RPI (Retail Price Index), whichever is the greater.

Kingston Lane
Address2 are not listed for this Course.
Address3 are not listed for this Course.
Uxbridge
UB8 3PH