Middlesex University

      Data Science MSc

      Middlesex University
      In London

      Price on request
      Prefer to call the centre directly?
      (0) 2... More
      Want to speak to an Advisor about this course?

      Important information

      Typology Master
      Location London
      Duration 1 Year
      Start Different dates available
      • Master
      • London
      • Duration:
        1 Year
      • Start:
        Different dates available
      Description

      Overview
      All industries now utilise data and Data-Science and Data-Analytics are increasingly identified as key industrial activities. The position of Data Scientist is rapidly becoming a required post for any company that wishes to take full advantage of the data that they collect. This course is designed to give you the skills to step into a career as a Data Scientist in a wide range of industries and companies.
      Why study MSc Data Science* at Middlesex University?
      This masters has been designed to offer those with a familiarity in maths, science or computing an opportunity to develop a key set of skills for future employment in a way that builds on your existing knowledge and skill base. Upon completing the course, you will be ready to fulfil the requirements of a Data Scientist.
      You will focus on the intertwining areas of machine learning, visual analytics and data governance, and be able to strike a balance between theoretical underpinnings, practical hands-on experience, and acquisition of industrially-relevant languages and packages. You will also be exposed to cutting-edge contemporary research activity within data science that will equip you with the potential to pursue a research-based career, and, in particular, further PhD study at Middlesex.
      Course highlights
      Explore theoretical and practical aspects with industry-recognised skills
      Study a course that is unique in its fusion of machine-learning, visual analytics and corporate data governance
      Equip yourself to apply machine learning and visual analytics to any data source

      Facilities (1)
      Where and when
      Starts Location
      Different dates available
      London
      The Burroughs, NW4 4BT, London, England
      See map
      Starts Different dates available
      Location
      London
      The Burroughs, NW4 4BT, London, England
      See map

      To take into account

      ¬∑ Requirements

      Entry requirements UK & EU International How to apply Qualifications A 2:2 honours degree in a related subject, such as those providing a significant exposure to information technology Applicants with degrees in other fields who can demonstrate relevant industrial experience may also be considered. Eligibility UK/EU and international students are eligible to apply for this course. Academic credit for previous study or experience. If you have relevant qualifications or work experience, academic credit may be awarded towards your Middlesex University programme of study

      Questions & Answers

      Ask a question and other users will answer you

      Achievements for this centre

      2018
      2017

      How do you get the CUM LAUDE seal?

      All courses are up to date

      The average rating is higher than 3.7

      More than 50 reviews in the last 12 months

      This centre has featured on Emagister for 9 years

      What you'll learn on the course

      Governance
      Computing
      Data analysis
      Algorithms
      Project
      systems

      Course programme

      Course content

      What will you study on MSc Data Science?

      Your studies will focus on the intertwining areas of machine learning, visual analytics and data governance. You will investigate theoretical underpinnings while gaining practical hands-on experience. You will build on your existing knowledge and skill base to gain key understanding that will be readily applicable for a career in data science.

      • Modules
        • Modelling, Regression and Machine Learning (30 credits) - Compulsory

          This course will equip you with the theoretical and algorithmic basis for understanding learning systems and the associated issues with very large datasets/data dimensionalities. You will be introduced to algorithmic approaches to learning from exemplar data and will learn the process of representing training data within appropriate feature spaces for the purposes of classification. You will also focus on basic data structures and algorithms for efficient data storage and manipulation. The major classifier types are taught before introducing the specific instances of classifiers along with appropriate training protocols. You will explore where classifiers have a relationship to statistical theory as well as notions of structural risk with respect to model fitting. You will be equipped with techniques for managing this in practical contexts.

        • Visual Data Analysis (30 credits) - Compulsory

          This module provides an understanding of the methods, theories and techniques relevant to interactive visual data analysis. You will learn relevant principles and practices in visual data analysis design, implementation, and evaluation. You will gain experience in researching, designing, implementing, and evaluating your own visual analysis solutions, using both off-the-shelf tool-kits and data visualisation programming libraries. You will gain the knowledge to support your future employment or research in the fast-developing areas of data science, particularly visual analytics.

        • Applied Data Analytics: Tools, Practical Big Data Handling, Cloud Distribution (30 credits) - Compulsory

          This course will provide an in-depth of the tools and systems used for mining massive dataset and, more in general, an introduction to the fascinating emerging field of Data Science. The module is divided in three parts. The first part focuses on the language R, a statistical learning language used to learn from data. This part provides an overview of the most common data mining and machine learning algorithms and every discussed concept is accompanied by illustrative examples written in R language. The second part of the module introduces the participant to MapReduce, a programming model used to process big data sets. We will teach how to design good MapReduce algorithms to process massive datasets. The third, and last, part of the module takes a tour through cloud computing systems and teaches the participant how to effectively use them.

        • Legal, Ethical and Security Aspects of Data Management (30 credits) - Compulsory

          This module focuses on legal, ethical and security requirements that underpin the technical processes and practice of data science (the collection, preparation, management, analysis and interpreting of large amounts of data called big data). Data science leads to predictive analyses and insights into big data for businesses, healthcare organisations, governments and security services among others. The volume of data collected, stored and processed brings many concerns especially related to privacy, data protection, liability, ownership and licensing of intellectual property rights and information security. This module will explore how data can be fairly and lawfully processed and protected by legal and technical means. It will give students a comprehensive understanding of important legal domains/regulatory issues, relevant ethical theories/guidance and important information security management policies that impact on the practice of data science. Further it will equip student with the necessary foundations to develop high professional standards when working as data scientists.

        • Individual Data Science Project (60 credits) - Compulsory

          This project module would give the student the opportunity to use a combination of general research methods and project planning, execution, management, evaluation, reporting knowledge and skills and specialist computer science and data science knowledge and understanding to apply an existing or emerging technology to the solution of a practical problem, or to contribute and extend the theoretical understanding of new and advancing technology and its application. The project will also give students the opportunity to demonstrate a personal commitment to legal, ethical, professional standards, recognising obligations to society, the profession and the environment. You will develop your communication skills to competently communicate your findings in written and oral form.

      You can find more information about this course in the programme specification. Module and programme information is indicative and may be subject to change.