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Data Science (Part-Time)
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MASTERS

Data Science (Part-Time)

Middlesex University
London, Great Britain
Part-time
€81.53/Credit
239 Points
Duration
2 Years
Language
English

Program Description

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.

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.

Entry Requirements

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Education
Bachelor's Degree
6
or
Degree from State Technical Insitute (TEI)
6
English Level
Michigan Proficiency (ECPE)
C
or
Cambridge Proficiency (CPE)
B
or
Cambridge Advanced (CAE)
A
or
International Baccalaureate
5.0
or
IGCSE
C
or
Pearson Test of Academic Engl.
58
or
IELTS
6.5
or
TOEFL (internet based)
87
or
PTE
58
Required Documents
CV
Certified Copy of the Degree Certificate/Diploma
Certified Copy of the English Certification
ID Certified Copy
Passport Certified Copy
Personal Statement - Motivation Letter
Detailed Transcript
Academic Reference Letter (1st)
Student Visa
Academic Reference Letter (2nd)
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Notes:

Information about qualifications:

  • A degree in a mathematically or computationally literate degree area

Other accepted qualifications:

  • Applicants with degrees in other fields who can demonstrate relevant industrial experience may also be considered.

Alternative English language requirements:

  • ESB ESOL International: Level 2‐Step 1
  • European Baccalaureate Language 1: Grade 7.0
  • European Baccalaureate Language 2: Grade 7.0
  • Language Cert International ESOL B2 SELT (SELT version ONLY): B2 High Pass overall and min 33/50 in each skill
  • Duolingo: Overall 110

More information:

  • If you have relevant qualifications or work experience, academic credit may be awarded towards your Middlesex University programme of study.

Curriculum

Modelling, Regression and Machine Learning

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. - (30 credits) Compulsory

Visual Data Analysis

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. - (30 credits) Compulsory

Applied Data Analytics: Tools, Practical Big Data Handling, Cloud Distribution

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 two 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 takes a tour through cloud computing and big data systems and teaches the participant how to effectively use them. Specifically, platforms and systems like OpenStack, Hadoop, MapReduce, MongoDB, Spark and NoSQL databases are introduced and every concept is accompanied by a number of illustrative examples. - (30 credits) - Compulsory

Legal, Ethical and Security Aspects of Data Management

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. - (30 credits) Compulsory

Individual Data Science Project (60 credits) - Compulsory

The project module aims to develop your knowledge and skills required for planning and executing research projects such as proof of concept projects or empirical studies related to data science. To plan and carry out your projects you will have to:

Apply theories, methods and techniques previously learned.

Critically analyse and evaluate research results drawing on knowledge from other modules.

Develop your communication skills to enable you to communicate your findings competently in written and oral form.

Careers

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.

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