Part time Course in Data Science in England in United Kingdom

Compare Part time Course Studies in Data Science in England United Kingdom 2017

Data Science

Courses are academic classes taught by qualified instructors that are intended to enhance participant’s knowledge of a given area or training in a particular discipline. Courses vary broadly in terms of length, size, content and duration.

Education in the United Kingdom is a devolved matter with each of the countries of the United Kingdom having separate systems under different governments: the UK Government is responsible for England, and the Scottish Government, the Welsh Government and the Northern Ireland Executive are responsible for Scotland, Wales and Northern Ireland, respectively.

England is the largest of the four "home nations" that make up the United Kingdom. It is also the most populous of the four with almost 52 million inhabitants (roughly 84% of the total population of the UK).

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Course in Functional Programming in Haskell: Supercharge Your Coding

FutureLearn
Online Part time 6 weeks September 2017 United Kingdom London

Get an introduction to Haskell, the increasingly popular functional programming language, with this University of Glasgow course. [+]

Part time Course Studies in Data Science in England in United Kingdom. Course in Functional Programming in Haskell: Supercharge Your Coding Do you want to develop software using the latest programming language paradigm? Haskell is a functional programming language, based on formal mathematical principles. As such, it is easy to reason about and develop, and it executes efficiently on modern multicore machines. From investment banks to social networks, everyone is adopting Haskell. Get an introduction to functional programming in Haskell On this introductory course, you will discover the power, elegance and simplicity of functional programming in Haskell. By the end, you will be able to: characterise the differences between imperative and functional programming paradigms; implement small-scale functional programs in elementary Haskell; apply standard combinators for operating on lists; create new algebraic data types and use recursion to define functions that traverse recursive types; and reason in a mathematical manner about data types, functions, recursion and similar functional constructs. Learn with developers from the birthplace of Haskell This course has been created by the School of Computing Science at the University of Glasgow – the virtual birthplace of the Haskell language, where many of its original developers worked. It will give you the opportunity to learn with these experts and join the growing, global community of Haskell programmers. Requirements This course is intended for learners who already have experience of at least one programming language, such as Python or Java. You might be a computer science student, a software developer who wants to learn a new programming style, or somebody considering university study in computer science or information technology. [-]

Course in Learn to Code for Data Analysis

FutureLearn
Online Part time 4 weeks October 2017 United Kingdom London

This hands-on course will teach you how to write your own computer programs, one line of code at a time. You’ll learn how to access open data, clean it and analyse it and to produce visualisations. You will also learn how to write up and share your analyses, privately or publicly. [+]

Course in Learn to Code for Data Analysis This hands-on course will teach you how to write your own computer programs, one line of code at a time. You’ll learn how to access open data, clean it and analyse it and to produce visualisations. You will also learn how to write up and share your analyses, privately or publicly. You will install free software (see Requirements below) to learn to code in Python, a widely used programming language across all disciplines, due to its support for scientific and engineering libraries and visualisation tools, and wide range of development tools. You will write up analyses and do coding exercises using the popular Jupyter Notebooks platform, which allows you to see immediately the result of running your code and helps you identify – and fix – any errors more easily. You will look at real data from the World Health Organisation, the World Bank and other organisations. You’ll be encouraged to discuss the data and your analyses with your fellow learners, and to build a community of researchers around these and other datasets. Requirements The course does not assume prior experience in programming or data analysis. Basic familiarity with a spreadsheet application will be an advantage. The course does not require any knowledge of statistics, but you need to have basic numeracy skills, like writing arithmetic expressions, using percentages and understanding scientific notation. If you wish to brush up on your numeracy skills, we recommend the FutureLearn course Basic Science: Understanding Numbers from The Open University. To study this course you will use specialist software. You can use the software online, via a free account on a website, or offline, by downloading and installing a free software package. You will receive instructions about both options via email before the course starts. The online solution requires a good internet connection and has some limitations. The offline software has no limitations and is the recommended option. However, you will need access to a desktop or laptop computer on which you can install software. The software is free and there are versions available for Windows, Mac and Linux platforms. You will need about 3 GB of free disk space to download and install the software, and to store datasets that will be provided in the course. Whether you choose the online or offline software option, you will need to be proficient in basic computer tasks, like creating folders, downloading files and copying them to specific folders, etc. In terms of accessibility, you will be asked to use your web browser and to type code. [-]

Course in Big Data: Mathematical Modelling

FutureLearn
Online Part time 2 weeks October 2017 United Kingdom London

Learn how to apply selected mathematical modelling methods to analyse big data in this free online course. [+]

Part time Course Studies in Data Science in England in United Kingdom. Course in Big Data: Mathematical Modelling This course is part of the Big Data Analytics program, which will enable you to gain the big data analytics skills that are in demand today. Have you ever wondered how mathematics can be used to solve big data problems? This course will show you how. Mathematics is everywhere, and with the rise of big data it becomes a useful tool when extracting information and analysing large datasets. Learn how maths underpins big data analysis We will begin by explaining how maths underpins many of the tools that are used to manage and analyse big data. We will show you how very different applied problems can have common mathematical aims, and therefore can be addressed using similar mathematical tools. We will then proceed to introduce three such tools, based on a linear algebra framework. These tools and the problems that they address are: eigenvalues and eigenvectors for ranking graph Laplacian for clustering singular value decomposition for data compression. Develop your analysis skills with prototypical case studies In this course, we have chosen a number of prototypical problems in data analytics to demonstrate the main concepts. These algorithms can be extended to facilitate their use in big data problems. Our hands-on approach will allow you to develop your analytic skills using self-contained datasets, deepen your understanding of the underlying mathematical methods, and explore how these methods can be applied to big data in your area. Continue learning with the Big Data Analytics program This course is one of four in the Big Data Analytics program on FutureLearn from the ARC Centre of Excellence for Mathematical and Statistical Frontiers at Queensland University of Technology (QUT). The program enables you to understand how big data is collected and managed, before exploring statistical inference, machine learning, mathematical modelling and data visualisation. When you complete all four courses and buy a Certificate of Achievement for each, you will earn a FutureLearn Award as proof of completing the program of study. Acknowledgements QUT would like to thank the following content contributors: Kevin Burrage Giuseppe De Martino Steve Psaltis Ian Turner Requirements This course is designed for anyone looking to add mathematical methods for data analytics to their skill set. To get the most out of this course, we recommend that you have studied linear algebra at a university/college level. We would encourage you to refresh your knowledge of vector and matrix algebra before engaging with the course material. We will assume basic MATLAB (or other) programming skills for some of the practical exercises. MathWorks will provide you with free access to MATLAB Online for the duration of the course so you can complete the programming exercises. Visit the MATLAB Online website to ensure your system meets the minimum requirements. [-]

Course in Big Data: Statistical Inference and Machine Learning

FutureLearn
Online Part time 2 weeks September 2017 United Kingdom London

This free online course equips you for working with these solutions by introducing you to selected statistical and machine learning techniques used for analysing large datasets and extracting information. [+]

Course in Big Data: Statistical Inference and Machine Learning Everyone has heard of big data. Many people have big data. But only some people know what to do with big data when they have it. So what’s the problem? Well, the big problem is that the data is big—the size, complexity and diversity of datasets increases every day. This means that we need new technological or methodological solutions for analysing data. There is a great demand for people with the skills and know-how to do big data analytics. Extract information from large datasets This free online course equips you for working with these solutions by introducing you to selected statistical and machine learning techniques used for analysing large datasets and extracting information. Of course, we can’t teach everything in one course, so we have focused on giving an overview of a selection of common methods. You will become familiar with predictive analysis, dimension reduction, machine learning and clustering techniques. You will also discover how simple decision trees can help us make informed decisions and you can dive into statistical learning theory. Explore real-world big data problems These methods will be described through case studies that explain how each is applied to solve real-world problems. You can also develop your coding skills by applying the techniques you’ve just learnt to complete hands-on tasks and obtain results. Just as there are many statistical and machine learning methods for big data analytics, there are also many software packages (see ‘Requirements’ below) that can be used for this purpose. In this course, we will expose you to three such packages, so that you can start to become familiar with using different tools, and can gain confidence in going further with these packages or using others that may come your way. Continue learning with the Big Data Analytics program This course is one of four in the Big Data Analytics program on FutureLearn from the ARC Centre of Excellence for Mathematical and Statistical Frontiers at Queensland University of Technology (QUT). The program enables you to understand how big data is collected and managed, before exploring statistical inference, machine learning, mathematical modelling and data visualisation. When you complete all four courses and buy a Certificate of Achievement for each, you will earn a FutureLearn Award as proof of completing the program of study. Acknowledgements QUT would like to thank the following content contributors: Tomasz Bednarz Amy Cook Miles McBain Kerrie Mengersen Sam Rathmanner Nan Ye Requirements You will enjoy this course most and benefit from the learning experience if you have a basic understanding of statistics and mathematics at an undergraduate level. In this course you will be using the following free tools. Please review the product websites below to ensure your system meets the minimum requirements: R and R Studio Desktop (open source edition) You will complete practical exercises using R Studio, so you’ll need to be familiar enough with R to: install a package import data read and run starter code develop a solution or read through a solution and gain understanding from it. NOTE: You must first have a working installation of R to use R Studio. H2O Flow H2O Flow can be used as a stand-alone package for big data analytics or can be used in conjunction with R. This package will allow you to tackle larger problems that you might encounter in your own work. WEKA WEKA is a popular workbench for machine learning and statistical analysis. It comprises a very wide range of tools that are suitable for big data analysis. Knowing R, H2O Flow and WEKA will give you a powerful, flexible and scalable set of tools to manipulate and analyse big data. [-]

Mastering Mathematical Finance Online Courses - Numerical Methods in Finance with C++

Department of Mathematics University of York - Online Programs
Online Part time 4 - 8 months August 2017 United Kingdom York

Driven by concrete computational problems in quantitative finance, this book provides aspiring quant developers with the numerical techniques and programming skills they need. The authors start from scratch, so the reader does not need any previous experience of C++. [+]

Part time Course Studies in Data Science in England in United Kingdom. The courses are based on 8 books from the "Mastering Mathematical Finance" (MMF) series published by Cambridge University Press. There are 8 individual courses - each covering the contents of one of the books. Delivery is by means of one-to-one tutorials conducted via Skype by the authors and editors of the series, and regular coursework. Who are the courses aimed at? The courses are designed to meet the continuing professional development and training needs of: Finance or IT professionals working in quantitative finance and risk management Individuals seeking a career change, managers who need to keep abreast with progress in these fields Prospective students who would like to prepare for entry to relevant postgraduate degree programmes Pre-sessional course (Pre-sessional course "Mathematics for Quantitative Finance" - This course is suitable for candidates who need to consilidate their mathematics background before embarking on some or all of the 8 courses. Cost - £1500) Method of Delivery List of Courses Each online course to be based on a book from the MMF series, with an additional set of exercises, and involves 10 rounds of activities culminating in 10 one-to-one online sessions. Each course takes aproximately 4 - 8 months to complete. Each of the 10 rounds consists of: self-study based on the book, problem solving: solutions submitted and marked electronically, model solutions to the problems attempted, written feedback on the work submitted, one-hour one-to-one online session via Skype with screen sharing, conducted by one of the authors of the MMF series, tailor-made for individual requirements, a combination of lectures and tutorials. Additionally, each module to provide: an online discussion forum, email support, final test. Induction meeting via Skype to cover technical matters before the start of the first module (including help in using the software needed for online delivery). Each student will need a decent internet connection (broadband standard), a Windows or Mac computer and a Skype account. There is some additional free software to install such as the LyX mathematical editor. Additional pre-sessional course available for delegates who need to revise or acquire relevant mathematical background. About the Numerical Methods in Finance with C++ Driven by concrete computational problems in quantitative finance, this book provides aspiring quant developers with the numerical techniques and programming skills they need. The authors start from scratch, so the reader does not need any previous experience of C++. Beginning with straightforward option pricing on binomial trees, the book gradually progresses towards more advanced topics, including nonlinear solvers, Monte Carlo techniques for path-dependent derivative securities, finite difference methods for partial differential equations, and American option pricing by solving a linear complementarity problem. Further material, including solutions to all exercises and C++ code, is available online. The book is ideal preparation for work as an entry-level quant programmer and it gives readers the confidence to progress to more advanced skill sets involving C++ design patterns as applied in finance. Written specifically at the Master's level by experienced lecturers, so readers can dive in directly The mathematics is rigorous but also motivated, so readers see how to apply what they learn Online material includes solutions to exercises and C++ code [-]