This comprehensive micro-credential covers advanced use of R and Rstudio, following on from the Data Programming with R module.Topics are subject to change each year as the software progresses but are likely to include: professional graphics through ggplot2; advanced function behaviour; advanced data processing with R through tidyverse packages; creation of interactive documents and presentations with Rstudio, markdown and knitr; creation of interactive applications through shiny; step-by-step guidance on creating R packages.You will learn through Lectures, tutorials, enquiry and problem-based learning. This micro-credential is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

University College Dublin

Data Science 

IT and Computer Science

This comprehensive micro-credential covers advanced use of R and Rstudio, following on from the Data Programming with R module.Topics are subject to change each year as the software progresses but

ADVANCED DATA PROGRAMMING WITH R

The module covers what is meant by learning from data and using patterns in data to learn from it as long as we have enough data from which to learn. The module will look at how to learn from data, using basic techniques and trialling them on data sets, and will look at algorithms to make machine learning better.

University of Limerick

The module covers what is meant by learning from data and using patterns in data to learn from it as long as we have enough data from which to learn.

Artificial Intelligence And Machine Learning

Connected embedded systems are the vital building blocks of the Internet of Things (IoT). Connected implies that these systems are linked to the physical world through sensors and actuators. Additionally, embedded systems can communicate with each other and with cloud-based Platform-as-a-Service (PaaS) solutions. This microcredential exposes students to state-of-art research and solutions for such embedded systems, including: embedded Linux, multi-platform SoC solutions, real-time interfacing, telemetry protocols for IoT, and messaging.

Dublin City University

Connected embedded systems are the vital building blocks of the Internet of Things (IoT). Connected implies that these systems are linked to the physical world through sensors and actuators. Additionally,

Connected Embedded Systems (EEN1071)

This micro-credential examines the legal and regulatory context of cybersecurity, considering both Irish law and the wider international framework. It looks at the development of criminal offences in relation to cybercrime and the way in which international cooperation in this area has developed. It considers the cybersecurity obligations which are imposed by a range of laws (such as the GDPR, ePrivacy Directive, NIS Directive, and so on), and assesses reporting and incident response mechanisms which must be in place to comply with these. It identifies and discusses particular legal issues around responding to cybercrime such as the sharing of intelligence, network surveillance and monitoring, and the implications of gathering data for forensic use. It also outlines the civil obligations and liability in damages which may result from cybersecurity incidents and issues such as whether and how these may be covered by insurance.

This micro-credential examines the legal and regulatory context of cybersecurity, considering both Irish law and the wider international framework. It looks at the development of criminal offences in relation to

CYBERSECURITY LAW & REGULATION

This module introduces the elements of the data analytics workflow, including data cleaning, feature extraction and feature selection, predictive and descriptive modelling, and deployment of models. The role of data visualisation in the data analytics process is discussed as well. The module involves practice with a state-of-the-art data analytics software platform.

This module introduces the elements of the data analytics workflow, including data cleaning, feature extraction and feature selection, predictive and descriptive modelling, and deployment of models. The role of data

Data Analytics

100% online - Data Analytics with R This module will cover an introduction to programming in R and RStudio for data analytics assuming no prior experience. The module will cover data wrangling, data visualisation, summarising data, and simple statistical modelling in R. Students will learn about reproducible research, coding and analytics practices, and be given an introductory overview of RShiny dashboards.

100% online - Data Analytics with R This module will cover an introduction to programming in R and RStudio for data analytics assuming no prior experience. The module will cover

Data Analytics with R

The micro-credential covers the essence of programming with data using the languages C and C++, with a particular focus on incorporating such code into the R statistical environment. You will learn the structure of both languages and how commands can be called from R via the Rcpp and inline packages. This enables a very large speed gain over traditional R commands and is especially useful for large data sets. You will learn through Lectures, tutorials, enquiry and problem-based learning. This micro-credential is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

The micro-credential covers the essence of programming with data using the languages C and C++, with a particular focus on incorporating such code into the R statistical environment. You will

DATA PROGRAMMING WITH C

This comprehensive micro-credential will provide you with a deep understanding of the principles and practices of in the range of topics, including (but not limited to):<ul><li> structure of the Python language</li><li>data manipulation</li> <li> data visualisation</li><li>statistical analysis</li> <li> regression and classification</li><li>machine learning and clustering algorithms</li> <li>APIs and web scraping</li><li>string manipulation and regular expressions</li></ul>You will learn how to manipulate data and perform statistical analysis using Python. All content is delivered asynchronously. There are no face-to-face lectures or tutorials. This micro-credential is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

This comprehensive micro-credential will provide you with a deep understanding of the principles and practices of in the range of topics, including (but not limited to): structure of the Python

DATA PROGRAMMING WITH PYTHON

This micro-credential covers the principles behind, and key characteristics, of stochastic models in real-world applications. Topics include: introduction to stochastic modelling with applications; non-parametric regression; Survival analysis, Markov chains, continuous time Markov processes, Poisson processes; mixture models. The lectures are recorded as videos. The notes include worked examples and computer code. In this micro-credential you will have a chance to be involved in mini-projects in statistical modelling. This micro-credential is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

This micro-credential covers the principles behind, and key characteristics, of stochastic models in real-world applications. Topics include: introduction to stochastic modelling with applications; non-parametric regression; Survival analysis, Markov chai

STOCHASTIC MODELS

The ability to predict how a data communications network will perform in terms of delay, throughput or packet loss is an important aspect of the engineering practice of computer and telecommunications network design. Given the complexity of operation of communications protocols, coupled with the randomness of data traffic transported by a network, gaining a reliable estimate of system performance requires careful analysis with appropriate modelling techniques. The aim of this micro-credential is to firstly review the operating principles of data communications protocols and then to develop the basic theory and practice required for evaluating the performance of communications systems and data networks, using discrete-state mathematical and computer simulation modelling methods.

The ability to predict how a data communications network will perform in terms of delay, throughput or packet loss is an important aspect of the engineering practice of computer and

Data Network Protocol Analysis & Simulation

This microcredential will introduce students to a set of intelligent algorithms applied in modern computing. Students will also develop a theoretical and mathematical foundation of these intelligent algorithms, and show how these algorithms can be used in problem solving environments and understand their properties and limitations. Through this, they will be able to gain experience with working with these advanced algorithms.

This microcredential will introduce students to a set of intelligent algorithms applied in modern computing. Students will also develop a theoretical and mathematical foundation of these intelligent algorithms, and show

Machine Learning (CSC1145)

This microcredential provides a postgraduate-level comprehensive introduction to cloud systems with an emphasis on in-demand and advanced topics such as big data computing, cloud resource virtualization and scheduling, and cloud management systems. It is designed in five courses with four focusing on key topics. Each course includes four topics corresponding to the main theme. The first course focuses on the concepts, architecture, infrastructure, technological foundations, and technology challenges of cloud systems. The second course targets big data processing and analytics in cloud centres. The third course concentrates on virtualisation technology in cloud systems.The fourth course covers the techniques on resource provisioning and task scheduling in cloud. The final course introduces the mechanisms and practical tools on cloud system management and operations. From the micro-credential's outcomes, the student will have a deep understanding of the architecture and the core techniques used in the current cloud systems. Moreover, they will be able to develop their own ideas on innovation applications using cloud and/or new approaches on optimising cloud systems.

This microcredential provides a postgraduate-level comprehensive introduction to cloud systems with an emphasis on in-demand and advanced topics such as big data computing, cloud resource virtualization and scheduling, and cloud

Cloud Systems (CSC1160)

This microcredential provides a postgraduate-level comprehensive introduction to Blockchain technology and how it can be used effectively in applications. It is designed in five courses and each course includes four topics corresponding to the main theme. The first course primarily focuses on the origin of blockchain and how blockchain enables the internet of value. The second course covers the challenges associated with implementing a real blockchain solution. The third course concentrates on Bitcoin including mining of Bitcoins, Bitcoin transaction workflows, and Bitcoin payment verifications and forks. The fourth course covers smart contracts including its key components and explanations on how they work. The final course introduces Blockchain vulnerabilities and security threats, which is vital information in todays world.

This microcredential provides a postgraduate-level comprehensive introduction to Blockchain technology and how it can be used effectively in applications. It is designed in five courses and each course includes four

Blockchain: Basics & Applications (CSC1148)

Lawyers and technologists are now using software tools as a means of analysing legal information, including case law, legislation, and practice data. This module explores the use of analytics for and by lawyers. Incorporating insights from the use of analytics in other disciplines, and decades of experience in the development of business information systems, it provides learners with an opportunity to develop critical perspectives on the appropriateness of legal analytics in different contexts while also building their practical data management and analysis skills through hands-on projects.

Digital, Data and Analytics 

Business and Management

Lawyers and technologists are now using software tools as a means of analysing legal information, including case law, legislation, and practice data. This module explores the use of analytics for

Law and Analytics

Topics covered in this short course include:Introducing data communications; Defining key terms and concepts 	What is data and datafication? Examining the landscape of open data, public data and critically the process of datafication. Show how datasets and databases are built.	Data modelling and algorithms. How data is classified, categorised, and modelled; the technical process of algorithmic decision-making and developing students' ability to analyse and evaluate potential limitations in the process.	Understanding data-driven methodologies: Exploring the most common ways data is used and developed in research and highlighting means of evaluating the quality and limitations of data and different data-driven methodologies.	Data and disciplines: Different approaches to data in science, social science, politics, business and more.	Data and the media: Developing a critical awareness of good and bad practices regarding the mediation of data-driven information	Data and regulation: Exploring the landscape of Irish and EU regulation around data and considering specific implications for data communications.

Topics covered in this short course include:Introducing data communications; Defining key terms and concepts	What is data and datafication? Examining the landscape of open data, public data and critically the

Introduction to Data Communications

Topics covered on this microcred include:Principles of data communication. Develop a better understanding of the core principles around communicating data-driven information to the general public. Additionally, we consider implications for communicating with different types of audiences.	Data gathering and analysis. Introduction to open and public data; Overview of approaches to sourcing and reviewing datasets to establish what stories emerge. 	Establish connections within and between datasets. Understanding relationships between different types of data-driven research, integrating complex data into stories, and communicating conflicting data. 	How to build narratives or stories from data. Applying knowledge gained to develop a range of narratives or stories about data on smaller or larger scales, small to large scale.	Collaborative data project based on our learner's disciplines, areas of work or interest.This module builds on from module An Introduction to Data Communication by further exploring how to prepare and extract key features of large datasets and use them to tell stories for different types of audiences. Students will also learn key concepts and techniques behind data perception and visualisation, as well as attaining basic proficiency in some associated software tools.

Topics covered on this microcred include:Principles of data communication. Develop a better understanding of the core principles around communicating data-driven information to the general public. Additionally, we consider implications for

Advanced Data Storytelling

The micro-credential focuses on the analysis of relational data, and reviews the available methodologies and algorithms that can be employed to model network interactions. You will learn through weekly video lectures and computer lab sheets. This micro-credential is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

The micro-credential focuses on the analysis of relational data, and reviews the available methodologies and algorithms that can be employed to model network interactions. You will learn through weekly video

STATISTICAL NETWORK ANALYSIS

This micro-credential will discuss several computational methods that rely on repeated random sampling to approximate numerical quantities of interest. In particular, the methods explored will concern strategies to simulate probability distributions in order to numerically compute different estimates of interest and to carry out inferential statements. It will make use of the free statistical software package R (www.r-project.org). You will learn through video lectures, computer code walkthroughs, and problem-based learning. This micro-credential is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

This micro-credential will discuss several computational methods that rely on repeated random sampling to approximate numerical quantities of interest. In particular, the methods explored will concern strategies to simulate probability

MONTE CARLO

This micro-credential provides an opportunity to learn some basic techniques in Time Series Analysis. A Time Series is a set of measurements taken at regular intervals over a period of time. Time series pervade the worlds of economics and finance e.g. equity prices, (CPI) inflation, GNP, GDP, derivative prices, oil prices, etc. Although the techniques of Time Series Analysis are used by many people, from meteorologists to astronomers, this course will mainly focus on examples in the world of economics and finance. This micro-credential will show how to model such data and how forecasts and predictions can be made using those models. Upon completion of this micro-credential you should have mastered techniques that are extremely valuable for careers in the analysis of economic and financial data. Topics covered include, among others: Stationarity, ARIMA models, Parameter Estimation, Forecasting, and Cointegration. Additional topics may be included which may vary from year to year.

This micro-credential provides an opportunity to learn some basic techniques in Time Series Analysis. A Time Series is a set of measurements taken at regular intervals over a period of

TIME SERIES

This micro-credential encompasses a collection of techniques for discovering patterns in data and making predictions, involving models and methods at the intersection of Machine Learning and Statistics. It will aim to introduce a set of techniques for the analysis of complex data. This micro-credential provides an overview of a variety of statistical learning methods for unsupervised and supervised learning. Focus will be placed on the understanding, the critical evaluation and the appropriate application of the different techniques in different data analysis scenarios. It will also cover how to implement these statistical learning methods using the statistical software R. You will learn through video lectures posted each week that walk-through module content, blending theory with examples and applications. Practice problem and coding-based problem sheets will enable self-assessment of learning outcomes. All content is delivered using the VLE, which includes a monitored discussion forum with topics created for each week's lecture material. It is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

This micro-credential encompasses a collection of techniques for discovering patterns in data and making predictions, involving models and methods at the intersection of Machine Learning and Statistics. It will aim

STATISTICAL MACHINE LEARNING

This micro-credential will introduce Bayesian analysis with an emphasis on concepts in Bayesian theory and practice. A focus throughout will be on statistical programming via the Stan probabilistic programming language. You will learn through video lectures posted each week that walk through module content, blending theory with applications. Each chapter in the micro-credential will be accompanied by Stan code which will allow you to implement the material developed in the video lectures. This micro-credential is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

This micro-credential will introduce Bayesian analysis with an emphasis on concepts in Bayesian theory and practice. A focus throughout will be on statistical programming via the Stan probabilistic programming language.

BAYESIAN ANALYSIS

This micro-credential will cover many common statistical techniques used to analyse high-dimensional data. Topics include clustering techniques; classification techniques; ordination techniques such as principal components analysis; and graphical techniques such as multidimensional scaling. All analyses will be conducted using the R software. You will learn through lectures, tutorials, enquiry and problem-based learning. This micro-credential is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

This micro-credential will cover many common statistical techniques used to analyse high-dimensional data. Topics include clustering techniques; classification techniques; ordination techniques such as principal components analysis; and graphical techniqu

MULTIVARIATE ANALYSIS

This module introduces students to simple statistical models with covariates, commonly known as regression models. Students will learn about the structure of such models, how to fit them using least squares, likelihood, and Bayesian methods, how to draw inferences, and how to check model fit. The module will use a variety of real-world examples to illustrate the use of regression models and cover both theoretical and practical considerations. Computer labs will make use of the R statistical package.

This module introduces students to simple statistical models with covariates, commonly known as regression models. Students will learn about the structure of such models, how to fit them using least

Predictive Analytics

In this comprehensive micro-credential, you will learn how to manipulate data and perform statistical calculations using SAS. You will study through video lectures posted each week that walk though module content, blending theory with example exercises. In addition you will be provided with practice problem sheets to enable self-assessment of learning outcomes. Sample solutions for these will be posted after each problem set. All content will be delivered using the VLE which includes a monitored discussion forum with topics created for each weeks lecture material and each problem set. This micro-credential is suitable for people already working in a role focused on data analytics, data science, programming or those interested in moving into the field.

In this comprehensive micro-credential, you will learn how to manipulate data and perform statistical calculations using SAS. You will study through video lectures posted each week that walk though module

DATA PROGRAMMING WITH SAS

This module will provide learners with an understanding and practical ability to correctly classify, value and determine origin for materials being imported or exported by their organisations, the legislative basis and the mechanisms required for import and export compliance. Participants will learn to understand their role in compliance with global trade regulations and to apply practical export knowledge within their organisation.

Information and Communication Technology 

This module will provide learners with an understanding and practical ability to correctly classify, value and determine origin for materials being imported or exported by their organisations, the legislative basis

Advanced Practices of Classification

This module will investigate the increasing digitalisation of business processes, from designÂ through to process execution. Process modelling will be introduced and the use ofÂ visualisation techniques in supporting process improvement will be established. The module will establish a robust process for understanding the digitisation requirements, paths to digitising the process and methods of process visualisation andÂ analysis using platforms like SAS JMP Pro Software. Robotic Process Automation (RPA) will also be considered with exposure to Software platforms such as Blue Prism.

This module will investigate the increasing digitalisation of business processes, from designÂ through to process execution. Process modelling will be introduced and the use ofÂ visualisation techniques in supporting process

Digitalisation Of Process

This module adopts an applied learning approach to understanding embedded systems, theÃ¢Â€Â Internet-of-things (IoT) and the cyber-physical systems (sensors, control boards) necessary forÃ¢Â€Â data acquisition in business environments. The aim of this module is to enable the learner to programme standard ICT Boards, I/O, sensorsÃ¢Â€Â and gateways in order to collect time-series data streams. Furthermore, the application of dataÃ¢Â€Â stream analysis at the Board/Gateway level (edge computing) will be explored. Robotic ProcessÃ¢Â€Â Automation (RPA) will also be considered with exposure to Software platforms such as BlueÃ¢Â€Â Prism

This module adopts an applied learning approach to understanding embedded systems, theÃ¢Â€Â Internet-of-things (IoT) and the cyber-physical systems (sensors, control boards) necessary forÃ¢Â€Â data acquisition in business environments. The a

Introduction To Cyber-Physical Systems & Internet of Things

This module will cover an introduction to programming in R and RStudio for professionals working in the wind energy sector, assuming no prior coding experience. The module will cover data wrangling, data visualisation, summarising data, and simple statistical modelling in R. Students will learn about reproducible research, coding and analytics practices, and be given an introductory overview of RShiny dashboards for data generated in wind energy applications.

This module will cover an introduction to programming in R and RStudio for professionals working in the wind energy sector, assuming no prior coding experience.   The module will cover data

Introduction to Data Analytics with R for Wind Energy

This module adopts an applied learning approach to identify opportunities and work with dataÃ‚Â through the lens of the relational database model. The aim of this module is to introduce the learner to standard business systems to collect andÃ‚Â interpret datasets for data-driven intelligence. Therefore, learners will develop an understandingÃ‚Â of the need to design and develop database systems, collect, clean, visualise and interpret dataÃ‚Â rooted in best data analysis practice.

This module adopts an applied learning approach to identify opportunities and work with dataÃ‚Â through the lens of the relational database model. The aim of this module is to introduce

Introduction To Data Analytics/Visualisation & Machine Learning

This module will provide an introduction to the core mathematics and core programming skills required in machine learning. On the successful completion of this module participants will be able to:Use Python to implement standard programming constructs. 	Understand the use of Calculus, Linear Algebra and Probability Theory in machine learning applications	Apply Python to simple machine learning models and data visualisations.

This module will provide an introduction to the core mathematics and core programming skills required in machine learning. On the successful completion of this module participants will be able to:Use

Introduction to Scientific Computing in Artificial Intelligence

This microcred will introduce you to machine vision and image processing principles. Key topics such as linear image processing, feature detection and essential object detection are introduced.Practical examples of these techniques are included in the laboratories for this module to increase student engagement with this material. This short course is a precursor to advanced vision modules.

This microcred will introduce you to machine vision and image processing principles. Key topics such as linear image processing, feature detection and essential object detection are introduced.Practical examples of these

Machine Vision and Image Processing

Topics covered include:Basic Text Processing: Regular Expressions, Word Tokenization, Normalization, Stemming and Lemmatization, and Sentence Segmentation. 	String Similarity: Minimum Edit Distance, Backtrace and Alignment, Weighted Minimum Edit Distance, Phonetic Matching, Real-world applications (record de-duplication).	N-gram Language Models: Introduction to N-grams, Estimating N-gram Probabilities, N-grams Evaluation and Perplexity, Generalization and Zeros, Add-One (Laplace) Smoothing, (Interpolation, Good Turing Smoothing, Kneser Ney Smoothing), Google Books N-gram Corpus, Zipf's law.	Spelling Correction: Introduction to the task of Spelling Correction, The Noisy Channel Model of Spelling, Real Word Spelling Correction, Peter Norvig's Spell Checker, State of the Art Systems.	Text Classification: Introduction to the task of text classification, Introduction to Naive Bayes, Formalizing the Naive Bayes Classifier, Naive Bayes Learning, Naive Bayes Relationship to Language Modeling, Precision, Recall, and the F measure, Text Classification Evaluation (micro; macro averaging), Practical Issues in Text Classification, Manual labelling tools and techniques (Amazon Mechanical Turk, brat, doccano, inception).	Sentiment Analysis: Introduction to the task of Sentiment Analysis, the baseline algorithm for Sentiment Analysis (tokenization, feature extraction, classification), Sentiment Lexicons, Learning Sentiment Lexicons, and Other Sentiment Tasks (aspects, attributes, targets).

Topics covered include:Basic Text Processing: Regular Expressions, Word Tokenization, Normalization, Stemming and Lemmatization, and Sentence Segmentation.	String Similarity: Minimum Edit Distance, Backtrace and Alignment, Weighted Minimum Edit Distance, 

Natural Language Processing: An Introduction

This course is designed and delivered by the Innovation Value Institute, Maynooth University's Digital Transformation and Innovation Research Centre. The micro-credential provides participants with the knowledge, skills, and confidence to help build a sustainable and successful organisational digital transformation programme. The course explores a range of topics such as: <ul><li>the fundamentals of digital transformation;</li><li>approaches to change leadership, effective change management, and organisation capability improvement;</li><li>cultural, structural, and technology dynamics of digital transition;</li><li>improving the business value outcomes of transformation in organisations;</li><li>major benefits and challenges associated with the management and execution of digital transformation initiatives.</li></ul>This course is suitable for professionals across multiple industries and at various career stages: <ul><li>new career entrants wishing to develop skills and knowledge;</li><li>mid-career professionals seeking to enhance their digital leadership capabilities;</li><li>senior managers wishing to use digital to build operational excellence. </li></ul>

Automation and AI 

This course is designed and delivered by the Innovation Value Institute, Maynooth University's Digital Transformation and Innovation Research Centre. The micro-credential provides participants with the knowledge, skills, and confidence to

LEADING DIGITAL TRANSFORMATION IN ORGANISATIONS

This micro-credential introduces students with no previous programming experience to the open-source statistical programming language R. Topics include: manipulating vectors, matrices, arrays and lists; basic programming constructs and programme flow; graphical methods; dealing with large data sets; simple statistical methods. This micro-credential is suitable for people already working in a role focused on data analytics, or those interested in moving into the field. You will learn through weekly video lecture and screencast material, non-assessed lab sheets.

This micro-credential introduces students with no previous programming experience to the open-source statistical programming language R. Topics include: manipulating vectors, matrices, arrays and lists; basic programming constructs and programme flow;

DATA PROGRAMMING WITH R

This micro-credentials covers introductory probability and statistical inference, focusing on understanding concepts and methodologies. Topics include: descriptive statistics, probability, sampling distributions, confidence interval estimation, hypothesis testing and regression.

This micro-credentials covers introductory probability and statistical inference, focusing on understanding concepts and methodologies. Topics include: descriptive statistics, probability, sampling distributions, confidence interval estimation, hypothesis

Introduction to data analytics

The aim of the micro-credential is to introduce students to the design of wireless networks and in particular to illustrate the differences between fixed and mobile voice and data networks. Students will gain in-depth knowledge of the underlying architectures of wireless communication systems and standards, physical layer, MAC, and network layer. The design and planning of a wireless network considering its critical limitations and performance issues. The design of cellular networks will be motivated in terms of the properties of wireless signal transmission and the evolution of cellular network technologies. Understanding of various wireless networks standard including LAN, MAN, short and long-range networks, and a combination of networks. Introduction of wireless sensor networks and their application for the Internet of Things.

The aim of the micro-credential is to introduce students to the design of wireless networks and in particular to illustrate the differences between fixed and mobile voice and data networks.

Wireless/Mobile Communications (EEN1043)

The aim of this microcredential is to present the student with the technical background necessary to understand how to effectively develop information retrieval systems and forward-thinking strategies. The student will learn to use these valuable skills to analyse personal and online data to develop enhanced information access, support and communication strategies. The micro-credential will firstly provide a thorough grounding in the mechanics of how information access and retrieval systems operate. The student will be presented with the main theoretical models behind search engines to understand their main limitations and strengths. The micro-credential will then explore how to extract knowledge from multimedia and individual data to support next-generation content search, recommendation and personalisation. New devices and methods will be considered here also. The final section of the micro-credential explores new directions and challenges in the search and information access landscape. The student will finish the course with a solid understanding of search and personal data, and utilising this information to communicate with the individual.

The aim of this microcredential is to present the student with the technical background necessary to understand how to effectively develop information retrieval systems and forward-thinking strategies. The student will

Mechanics of Search (CSC1121)

The aim of this microcredential is to introduce students to the software embedded in accredited network devices such as routers to implement network protocols. Whenever feasible, we will delve into the study of open-source protocol implementations used in live networks. In addition to that, both the data plane and the control plane will be studied, including data-link layer protocols, network layer protocols and transport layer protocols. The sought-after skills of hardware acceleration, optimisation techniques and other approaches to achieving "wire speed" operation will be investigated. Lastly, protocols appropriate to the Internet of Things, to data centres, and to the future Internet will be looked into.

The aim of this microcredential is to introduce students to the software embedded in accredited network devices such as routers to implement network protocols. Whenever feasible, we will delve into

Network Stack Implementation (EEN1004)

This micro-credential introduces students to advanced network performance concepts. The students will be reminded of the OSI and the TCP/IP models and basic network-related protocol aspects. They will be introduced to Quality of Service and Network Performance metrics as well as to Quality of Experience and Quality of Perception as metrics to assess the quality of data delivery. Various novel and existing network protocols at network and transport network layers will be introduced and their performance-related characteristics will be discussed. These protocols will be classified based on the layers of the OSI stack they operate at and include: IPv4, IPv6, TCP, SCTP, DCCP, RTP, RTCP, etc. At application layer, multimedia and gaming applications will be discussed in the context of real-time content delivery, along with other application types. Adaptive and non-adaptive content delivery solutions will be presented along with significant issues such as end-user perceived quality. Modeling and simulation with the goal of assessing network performance will be introduced with focus on the application layer. Java development with focus on performance of transport and application layer network protocols will be performed. Novel performance-related issues in the context of mobility, power consumption and security will be discussed.

This micro-credential introduces students to advanced network performance concepts. The students will be reminded of the OSI and the TCP/IP models and basic network-related protocol aspects. They will be introduced

Network Performance (EEN1058)

This microcredential introduces students to the well-regarded realm of cybersecurity technologies, algorithms and protocols for securing IoT and Edge Networks. It introduces symmetric cryptography algorithms for data confidentiality, asymmetric cryptography algorithms for key distribution and identity authentication, and hash functions for message authentication. In addition, the microcredential describes different types of security attacks and countermeasures, which prepares the student for potential real-world issues. The microcredential also covers the mathematics fundamental to cryptographic algorithms and other technologies, including privacy protection, public key infrastructure (PKI), PKI-based authentication, programmable logic, encryption/decryption, and trusted hardware.

This microcredential introduces students to the well-regarded realm of cybersecurity technologies, algorithms and protocols for securing IoT and Edge Networks. It introduces symmetric cryptography algorithms for data confidentiality, asymmetric cryptograp

Security for IoT and Edge Networks (EEN1059)

The overall aim of this microcredential is to introduce the high-value theory and practice of mathematical network analysis and optimisation methods. They apply to the problems of performance analysis of communications protocols, network dimensioning and capacity planning, network architecture design and traffic analysis in modern large-scale data networks. Examples of such include optically switched metro and access networks, datacenter and high performance computing interconnects, and femto-macro cell wireless network architectures. Network analysis is essential to understanding and evaluating the fundamental performance properties (e.g. latency, jitter, throughput, packet-drop rate) of complex network architectures and communications protocols. Network dimensioning methods are a necessity in the planning and deploying of large-scale networks under given capacity and cost constraints. This microcredential will cover fundamental theory in probability, stochastic processes, queuing theory, graph theory and optimisation methods and apply them to solving various data network design and performance management problems.

The overall aim of this microcredential is to introduce the high-value theory and practice of mathematical network analysis and optimisation methods. They apply to the problems of performance analysis of

Network Analysis and Dimensioning (EEN1075)

In today's ever-evolving digital landscape, it is crucial for students to stay ahead of the curve by acquiring the skills and knowledge necessary to operate and design future data networks with aggregate capacities in the petabit/s region. By enrolling in our Future Data Network micro-credential, students will gain a comprehensive understanding of the principles of network architecture design that are essential in achieving the necessary scale and efficiency of operation in data networks of the future. This micro-credential offers a unique opportunity for students to explore the range of acceleration techniques required to inject and extract data at tomorrow's wire speeds. Furthermore, students will delve into the orchestration of resources and network reconfiguration in data centre networks and at the network edge. This will allow them to examine the use of these techniques in deploying virtual networks and network slices, a skill that is in high demand in the ICT industry

In today's ever-evolving digital landscape, it is crucial for students to stay ahead of the curve by acquiring the skills and knowledge necessary to operate and design future data networks

Data Science

Microcreds