Data Science MSc with Integrated Placement (15 months/24 months) | Middlesex University London
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Data Science MSc with Integrated Placement (15 months/24 months)

Learn about the course below
Code
PGI100A (15 months)
PGI100B (24 months)
Start
October 2018
Duration
15 months full-time (3 months placement)
2 years full-time (12 months placement)
Attendance
Full-time
Fees
£8,800 (UK/EU)*
£13,500 (INT)*
Course leader
David Windridge

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 the MSc Data Science with Integrated Placement (15 months/24 months) 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.

As part of this course, you can do an optional three month or one year industry placement.  Programmes with integral placements give you the opportunity to apply the skills you have learned throughout your studies in a practical environment. You will be earning a full time salary and will learn skills that can't be taught in a classroom at University. During the placement, you will be able to gain further insight into industrial practice that you can take forward into your individual project and into your future career.

Although the placement is not guaranteed, the University maintains links with a wide network of organisations who offer placement opportunities. The University will also provide you with full support to help you secure a placement, from job application to the interview.

In order to qualify for the placement period you must have passed all modules in the semesters preceding the placement. The placement will take place after you have completed all your modules and undertaken your final applied project.

What will you study on the MSc Data Science with Integrated Placement (15 months/24 months)?

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 module will give you an in-depth understanding of the tools and systems used for mining massive data-sets. It also serves as an introduction to the fascinating and emerging field of Data Science. You will focus on the language R, a statistical learning language used to learn from data, which will provide an overview of the most common data mining and machine learning algorithms. Each concept discussed is also accompanied by illustrative examples written in R language. You will be introduced to MapReduce, a programming model used to process big data sets and you will learn how to design good MapReduce algorithms to process massive datasets. You will also explore cloud computing systems and learn to use them effectively.

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

      Data science leads to predictive analyses and insights into big data for businesses, healthcare organisations, governments and security services, amongst 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. As such, this module will focus on legal, ethical and security requirements that underpin the technical processes and practice of data science including the collection, preparation, management, analysis and interpreting of large amounts of data. You will explore how data can be fairly and lawfully processed and protected by legal and technical means. You will gain a comprehensive understanding of important legal domains/regulatory issues, relevant ethical theories/guidance and security management policies that impact on the practice of data science. You will also be equipped with the necessary foundations to develop high professional standards when working as data scientists.

    • Individual Data Science Project (60 credits) - Compulsory

      This module aims to develop your knowledge and skills required for planning and executing data science research projects, which can include proof of concept projects or empirical studies related to data obtained from industrial or academic sources. You will plan and carry out your project by applying theories, methods and techniques previously learned and critically analyse and evaluate your research results. You will develop your communication skills to competently communicate your findings in written and oral form.

    • Placement (0 credits)

      As part of this course, you can do an optional three month or one year industry placement.  Programmes with integral placements give you the opportunity to apply the skills you have learned throughout your studies in a practical environment. You will be earning a full time salary and will learn skills that can't be taught in a classroom at University. During the placement, you will be able to gain further insight into industrial practice that you can take forward into your individual project and into your future career.

      You will undertake your placement after completing your taught modules and project. During the placement, you will be assigned an academic supervisor, who will maintain direct contact with you. You will be able to access online materials at the University as well as other physical resources as appropriate. You will maintain a placement log for the day-to-day activities and submit a final report once the placement comes to an end.

      Although the placement is not guaranteed, the University maintains links with a wide network of organisations who offer placement opportunities. The University will also provide you with full support to help you secure a placement, from job application to the interview.

      In order to qualify for the placement period you must have passed all modules in the semesters preceding the placement.

You can find more information about this course in the programme specification. Please note that optional modules may not run, due to student numbers or staff availabilty. If an optional module will not run, we will advise you after the module selection period when numbers are confirmed, or at the earliest time that the programme team make the decision not to run the module, and help you choose an alternative module.

How is the MSc Data Science with Integrated Placement (15 months/24 months) taught?

You will gain knowledge and understanding through a combination of traditional lecture delivery, small group discussions, small group and individual exercises, lab sessions and the individual project. Throughout your studies, you are encouraged to undertake independent study both to supplement and consolidate what is being learned, and to broaden your individual knowledge and understanding of the subject. Critical evaluation and selection of techniques and solutions will engage you in relating theory to practice.

Assessment

Your technical skills will be assessed throughout the year in a series of formative and summative coursework. Every week, you will be given lab tasks designed to match the content covered in the lecture. These tasks are expected to be completed during the lab and you will receive timely feedback assessment.

Summative coursework is planned for every two months, after the completion of each major module component. The type of the work will depend on the module finished so it could range from development work after a technical component or a research/report after a non-technical component (such as design and evaluation). These works require considerably more effort than the formative coursework and can give you a clear indication of your performance on each major module component.

Coursework breakdown

  • Quiz: data visualisation foundations (10%)
  • Coursework 1: Visual data analysis with Tableau (20%)
  • Coursework 2: Interactive data visualisation with JavaScript and D3.js (20%)
  • Coursework 3: mini project to develop a visual analytics workflow for analysing real world data, using KNIME (25%)
  • Coursework 4: mini-project to develop a software for image analysis (25%)
  1. UK & EU
  2. International
  3. How to apply
  1. UK & EU
  2. International
  3. Additional costs

How can the MSc Data Science with Integrated Placement (15 months/24 months) support your career?

Upon completing the course, you will be well placed to step into a career as a data scientist in a wide range of industries and companies.

You could also consider continuing your studies to PhD level.

Dr David Windridge
Associate Professor in Computer Science

Dr Windridge heads the University's Data Science activities. His research interests centre on the related fields of machine-learning, cognitive systems and computer vision. He also has a former research interest in Astrophysics. He has played a leading role on a number of large-scale machine-learning projects in academic and industrial research and has won a number of interdisciplinary data science research grants areas such as psychological modelling and proteomics. He has authored more than 100 peer-reviewed publications (including best paper awards), with over 1000 citations collectively.

Other courses

Data Science MSc by Research

Start: January 2017, September 2017

Duration: 1 year full-time, Usually 2 years part-time

Code: PGY000

Computer Science MSc with Integrated Placement (15 months/24 months)

Start: October 2018, September 2018 (EU/INT induction)

Duration: 15 months full-time (3 months placement), 2 years full-time (12 months placement)

Code: PG404A (15 months), PG404B (24 months)

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