ATH/TECH | MSc in Big Data Engineering & Data Science
Master's

MSc in Big Data Engineering & Data Science

The skills of collecting, storing, transforming, mining and analysing data are essential for a professional working in the modern labour market of technology and innovation. This programme’s goal is to educate the engineers and scientists who will lead the ever growing and evolving industry, in which new job openings in the data fields (i.e. data engineers, data scientists, ML engineers, AI engineers, ETL developers, Business Intelligence experts, Big data experts, data analysts and others) are constantly available.

MSc in Big Data Engineering & Data Science

WHY CHOOSE THIS COURSE

Because the degree received is issued by the the University of York, one of the world’s premier institutions for inspirational teaching and life-changing research and a member of the prestigious Russell Group in UK.

Because this is the ideal course for software engineers and developers who wish to gain from emerging technologies and succeed in creating strategic growth, competitiveness, profit and value for the companies they work for.

Because it is the only program that combines software engineering aspects with data related fields and provides extended hands-on experience to students in fields like artificial intelligence, machine learning, business intelligence, data visualization, data integration, distributed systems, ETL and others.

Because it focuses on creating and developing engineers, scientists, experts and professionals who are ready to deal with hands-on cases and actual issues companies face in the complicated field of data.

Because it provides a variety of employment options due to the great demand for graduates who combine ICT knowledge and skills in data related fields. Graduates can start their own business or work in various fields (i.e. data engineers, data scientists, ML engineers, AI engineers, ETL developers, Business Intelligence experts, Big data experts, data analysts and others) or even begin an academic career.

 

MSc in Big Data Engineering & Data Science

GOALS OF THIS COURSE
  • To teach the necessary techniques and skills on how to process massive datasets and turn them into valuable information that will lead to the accomplishment of business objectives.
  • To provide specialisation for sectors that are in constant need of professionals with skills in big data engineering and data sciences.
  • To focus on industrial models approved and needed by businesses so that students will be efficiently prepared for a career in every field relating to IT.
  • To cοnduct research and compose a thesis under the supervision of the faculty.
  • To create the leaders of the business world.
  • To train students to conceive, develop and create complex software systems.
  • To employ scientific personnel of experienced lecturers and researchers. The professors of CITY College, University of York Europe Campus have been long teaching in academic environments and have been involved in research groups and studies. Frequent seminars with guest speakers from the tech industry and the academic world complete the learning process.

MSc in Big Data Engineering & Data Science

CURRICULUM
Full - Time Mode

Deep Learning

Deep learning is a hot topic that has found multiple areas of application in the industry and business. Deep learning is the extension of Neural Networks (NN) that includes some new developments in training algorithms and uses the versatility of the computing power and data storage of the cloud. The module briefly introduces neural networks, explains how they work, how they are trained, and how they are deployed. Furthermore, it discusses the recent developments in training algorithms, NN structures, and cloud deployment, to conclude with the practical application of Artificial Intelligence solutions that we now call Deep Learning.

Continuous and Agile Software Engineering

The aim of this unit is to present contemporary topics in Software Engineering. It starts with a brief overview of software engineering principles and then focuses on modern approaches to software development and management, software architecture, and advanced techniques as formal methods. Students will actively contribute to the lectures, by reviewing and presenting material on contemporary software engineering topics.

Industrial Group Project

The purpose of this unit is to provide students with the opportunity to integrate and apply the skills and the knowledge they have acquired so far in their studies to a realistic problem. Students are exposed to the processes involved in the team-based development of software through real projects that are provided by companies from the software industry.

Research Skills and Dissertation Preparation

This unit intends to introduce students to the research topics and techniques that are commonly employed in software engineering and telecommunication science. Students will be exposed to the principles of report writing, literature reviewing, and research designs and approaches. These research approaches will include the design of data collection and analysis methods, as well as representation and interpretation of the results. An introduction to tools facilitating quantitative and qualitative analysis will also be provided. The unit is enriched with a number of exercises, case studies and discussions and concludes with practical guidelines of how to write a successful dissertation.

Business Intelligence

Business intelligence (BI) systems are applications and technologies for gathering, storing, analyzing, and accessing information for better business decision-making. The application areas of such systems include measuring and monitoring key performance indicators, benchmarking and forecasting sales, performing data mining and analysis of customer information.
This course provides a comprehensive introduction to the concepts, components, techniques and applications of BI and related areas. Students will gain awareness, in a gradual manner, of the complete life cycle of implementing and managing BI systems.

Big Data Engineering

This unit explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. Main aspects and challenges of Big Data will be addressed by introducing relevant algorithms, among others, in the areas of MapReduce, data stream mining and recommendation systems.
Additionally, this course provides a detailed description and hands-on experience to the cutting-edge open-source software Apache Hadoop and Apache Kafka.
Students will be introduced and gain awareness, in a granual manner, to the concepts, algorithms and techniques that cover key Big Data topics.

Data Mining and Machine Learning

This unit addresses the contemporary aspects, algorithms and challenges of mining big data by introducing data mining and machine learning algorithms. The unit aims to provide students with the necessary skills, knowledge and hands-on experience regarding data exploration, data preprocessing, visualization, unsupervised machine learning algorithms, clustering and other contemporary topics like dimensionality reduction, anomaly detection, pattern recognition, association rules and recommendation systems.

Advanced Artificial Intelligence Techniques

This unit starts with an in-depth introduction to Artificial Intelligence problem solving techniques and covers contemporary and advanced AI algorithms and application area. Main aspects and challenges that will be addressed by introducing relevant theory and hands-on practical sessions include search algorithms (brute-force, heuristic), supervised Learning Algorithms, Neural Networks, Deep Learning, Natural Language Processing and image classification.

Cloud Engineering

The unit aims at introducing students to a range of foundational –as well as more advanced– topics pertaining to cloud engineering, including:

  • Fundamental concepts: foundations of cloud computing, principles of cloud computing, benefits, cloud service models, cloud deployment models, virtualisation technologies.
  • Cloud platforms: AWS EC2, AWS Lambda, Heroku.
  • Cloud-native applications: SOA, Microservices, Docker, Kubernetes, DevOps, challenges involved.
  • Quality of provisioned services: scaling, IoT, Fog Computing, Mobile Edge Computing, SLAs.
  • Security issues: access control, colocation attacks, side-channel attacks.

Dissertation

Part - Time Mode

Year 1

Deep Learning

Deep learning is a hot topic that has found multiple areas of application in the industry and business. Deep learning is the extension of Neural Networks (NN) that includes some new developments in training algorithms and uses the versatility of the computing power and data storage of the cloud. The module briefly introduces neural networks, explains how they work, how they are trained, and how they are deployed. Furthermore, it discusses the recent developments in training algorithms, NN structures, and cloud deployment, to conclude with the practical application of Artificial Intelligence solutions that we now call Deep Learning.

Big Data Engineering

This unit explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. Main aspects and challenges of Big Data will be addressed by introducing relevant algorithms, among others, in the areas of MapReduce, data stream mining and recommendation systems.
Additionally, this course provides a detailed description and hands-on experience to the cutting-edge open-source software Apache Hadoop and Apache Kafka.
Students will be introduced and gain awareness, in a granual manner, to the concepts, algorithms and techniques that cover key Big Data topics.

Continuous and Agile Software Engineering

The aim of this unit is to present contemporary topics in Software Engineering. It starts with a brief overview of software engineering principles and then focuses on modern approaches to software development and management, software architecture, and advanced techniques as formal methods. Students will actively contribute to the lectures, by reviewing and presenting material on contemporary software engineering topics.

Business Intelligence

Business intelligence (BI) systems are applications and technologies for gathering, storing, analyzing, and accessing information for better business decision-making. The application areas of such systems include measuring and monitoring key performance indicators, benchmarking and forecasting sales, performing data mining and analysis of customer information.
This course provides a comprehensive introduction to the concepts, components, techniques and applications of BI and related areas. Students will gain awareness, in a gradual manner, of the complete life cycle of implementing and managing BI systems.

Year 2

Industrial Group Project

The purpose of this unit is to provide students with the opportunity to integrate and apply the skills and the knowledge they have acquired so far in their studies to a realistic problem. Students are exposed to the processes involved in the team-based development of software through real projects that are provided by companies from the software industry.

Research Skills and Dissertation Preparation

This unit intends to introduce students to the research topics and techniques that are commonly employed in software engineering and telecommunication science. Students will be exposed to the principles of report writing, literature reviewing, and research designs and approaches. These research approaches will include the design of data collection and analysis methods, as well as representation and interpretation of the results. An introduction to tools facilitating quantitative and qualitative analysis will also be provided. The unit is enriched with a number of exercises, case studies and discussions and concludes with practical guidelines of how to write a successful dissertation.

Data Mining and Machine Learning

This unit addresses the contemporary aspects, algorithms and challenges of mining big data by introducing data mining and machine learning algorithms. The unit aims to provide students with the necessary skills, knowledge and hands-on experience regarding data exploration, data preprocessing, visualization, unsupervised machine learning algorithms, clustering and other contemporary topics like dimensionality reduction, anomaly detection, pattern recognition, association rules and recommendation systems.

Advanced Artificial Intelligence Techniques

This unit starts with an in-depth introduction to Artificial Intelligence problem solving techniques and covers contemporary and advanced AI algorithms and application area. Main aspects and challenges that will be addressed by introducing relevant theory and hands-on practical sessions include search algorithms (brute-force, heuristic), supervised Learning Algorithms, Neural Networks, Deep Learning, Natural Language Processing and image classification.

Cloud Engineering

The unit aims at introducing students to a range of foundational –as well as more advanced– topics pertaining to cloud engineering, including:

  • Fundamental concepts: foundations of cloud computing, principles of cloud computing, benefits, cloud service models, cloud deployment models, virtualisation technologies.
  • Cloud platforms: AWS EC2, AWS Lambda, Heroku.
  • Cloud-native applications: SOA, Microservices, Docker, Kubernetes, DevOps, challenges involved.
  • Quality of provisioned services: scaling, IoT, Fog Computing, Mobile Edge Computing, SLAs.
  • Security issues: access control, colocation attacks, side-channel attacks.

Summer of Year 2

Dissertation

MSc in Big Data Engineering & Data Science

INFORMATION + PREREQUISITES
STARTING DATES

1st semester (October)
2st semester (February)

DURATION

1 Year (Full Time)

2 Years (Part Time)

LANGUAGE OF INSTRUCTION

English

SCHEDULE

Part Time : Four weekday evenings per week

Part Time : Two weekday evenings per week

PREREQUISITES FOR ENROLLING

Candidates should hold an undergraduate degree in Computer Science, Computer Engineering, or any other related ICT discipline.

Postgraduate applicants must have a fluent command of the English language, certified by one of the following:

      • International English Language Testing System (IELTS Academic) with overall score 6.5 or above, with at least 5.5 in each component, or
      • Pearson – PTE Academic with overall score 61 or above, with no less than 51 in each component.
      • Cambridge Certificate in Advanced English (CAE) with overall score of 176 or above, with no less than 162 in each component.
      • Cambridge Certificate of Proficiency in English (CPE) with an overall score of 176 or above, with no less than 162 in each component.
      • Test of English as a Foreign Language (TOEFL) internet-based test (iBT) and special home edition, with overall score 87 or above, with a minimum of 17 in Listening, 18 in Reading, 20 in Speaking and 17 in Writing, or
      •  Michigan State University – Certificate of English Language Proficiency (MSU – CELP): CEFR C2
      • GCSE English Language with minimum Grade C / Grade 4.
      • iGCSE English Language with minimum Grade C.
      • Trinity ISE Level 3 with Pass in all components.
      • Duolingo with overall score 110, minimum 90 in all other components.
      • Examination for the Certificate of Proficiency in English (ECPE), or
      • Michigan English Test (MET) with overall score 230 and above, with a minimum of 53 in each component.

 

The certification has to have been issued within the last three years.

Candidates who have completed their Bachelor’s studies or their higher secondary education entirely through the medium of the English Language are not required to hold an English language qualification.

If a candidate does not fulfill the English Language requirement and achieves a mark higher than 6.5 in the ATH/TECH placement test, they will be allowed to proceed with their enrollment on the condition that the required certificate will be submitted by the end of the first semester (full-time attendance) or by the end of the first year of studies (part-time attendance).

DOCUMENTATION

In the Rules & Regulations section, you may find all the files, applications and entry requirements needed.

MSc in Big Data Engineering & Data Science

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