### Overview

This is a a four year honours degree programme delivered jointly by the School of Computer Science and the School of Mathematical Sciences. This programme includes a six-month work placement/project (CS3220 Work Placement DSA) in Third Year.

### First Year - Data Science and Analytics

To be admitted to the First University Examination in Data Science and Analytics a student must have satisfactorily attended modules amounting to **60** credits comprising core modules to the value of **55** credits, and elective modules to the value of **5** credits.

### Second Year - Data Science and Analytics

To be admitted to the Second University Examination in Data Science and Analytics a student must have satisfactorily attended modules amounting to **60** credits comprising core modules to the value of **55** credits, and elective modules to the value of **5** credits.

### Third Year - Data Science and Analytics

To be admitted to the Third University Examination in Data Science and Analytics a student must have satisfactorily attended modules amounting to **60** credits.

### Fourth Year - Data Science and Analytics

To be admitted to the Fourth University Examination in Data Science and Analytics a student must have satisfactorily attended modules to the value of **60** credits comprising core modules to the value of **45** credits, and elective modules to the value of **15** credits.

### Programme Requirements

For information about modules, module choice, options and credit weightings, please go to Programme Requirements.

### Programme Requirements

Module List
Code |
Title |
Credits |

| |

CS1106 | Introduction to Relational Databases | 5 |

CS1112 | Foundations of Computer Science I | 5 |

CS1113 | Foundations of Computer Science II | 5 |

CS1117 | Introduction to Programming | 15 |

AM1054 | Mathematical Software | 5 |

MA1058 | Introduction to Linear Algebra | 5 |

MA1059 | Calculus | 5 |

ST1050 | Statistical Programming in R | 5 |

ST1051 | Introduction to Probability and Statistics | 5 |

| |

AM1053 | Introduction to Mathematical Modelling | 5 |

or ST1402 | Modelling and Systems for Decision Making |

| |

CS2208 | Information Storage and Management I | 5 |

CS2209 | Information Storage and Management II | 5 |

CS2513 | Intermediate Programming | 5 |

CS2514 | Introduction to Java | 5 |

CS2515 | Algorithms and Data Structures I | 5 |

CS2516 | Algorithms and Data Structures II | 5 |

MA2055 | Linear Algebra | 5 |

MA2071 | Multivariable Calculus | 5 |

ST2053 | Introduction to Regression Analysis | 5 |

ST2054 | Probability and Mathematical Statistics | 10 |

| |

AM2052 | Mathematical Modelling | 5 |

or ST2403 | Time-to-Event Analysis |

| |

CS3204 | Cloud Infrastructure and Services | 5 |

CS3205 | Data Visualization for Analytics Applications | 5 |

CS3220 | Work Placement DSA | 10 |

CS3306 | Workplace Technology and Skills | 10 |

CS3318 | Advanced Programming with Java | 5 |

CS3509 | Theory of Computation | 5 |

ST3053 | Stochastic Modelling I | 5 |

ST3061 | Statistical Theory of Estimation | 5 |

ST3069 | Generalised Linear Models | 5 |

ST3071 | Risk Prediction Modelling | 5 |

| |

CS4701 | Analytics Project for Computer Science | 15 |

or ST4092 | Data Analytics Project |

CS4704 | Algorithms and Data Structures for Analytics | 5 |

CS4705 | Computational Machine Learning | 5 |

ST4060 | Statistical Methods for Machine Learning I | 5 |

ST4061 | Statistical Methods for Machine Learning II | 5 |

ST4069 | Multivariate Methods for Data Analysis | 10 |

| 15 |

| Computer Modelling and Numerical Techniques (5) | |

| Topics in Applied Mathematics (5) | |

| Principles of Compilation (5) | |

| Multimedia Compression and Delivery (5) | |

| Introductory Network Security (5) | |

| Functional Programming I (5) | |

| Constraint Programming and Optimisation (5) | |

| Survival Analysis (5) | |

| Time Series (5) | |

Total Credits | 240 |

### Examinations

Full details and regulations governing Examinations for each programme will be contained in the *Marks and Standards Book* and for each module in the *Book of Modules*.

### Programme Learning Outcomes

**Programme Learning Outcomes for BSc (Hons) (Data Science and Analytics) (NFQ Level 8, Major Award)**

On successful completion of this programme, students should be able to:

- Analyse problems of a computational and/or quantitative nature, encountered in a range of types of large-scale data, and construct solutions to such problems using the tools and skills of modern data analytics, including the use of machine learning, statistical and mathematical computer packages, and the use of database programmes;

- Describe the fundamental theories, models and principles of statistical methods, and carry out a wide range of calculations involved in statistical decision making, modelling, hypothesis generation and inference;

- Describe the fundamental theories, models and principles of computational methods for storing, processing and performing inference on large data sets; examples include machine learning, data mining and probabilistic methods;

- Manage large amounts of data using modern database tools, and understand the management implications of hardware, software and bandwidth constraints;

- Apply data management tools to data sets from a range of application domains, such as biology, business, and science, in order to gain exposure to working with different types of data;

- Analyse data selected from a range of domains such as insurance, bio-informatics, marketing, social networking, finance, fraud detection, and drug discovery;

- Perform computational/statistical analyses and create visualizations to aid in understanding heterogeneous data;

- Summarize and communicate computational and statistical models and techniques, and be able to visualise this information in order to best present such summaries to technical and non-technical audiences;

- Apply visualisation and summarization techniques to application domains, to demonstrate ability to highlight outcomes from different types of data with respect to different objectives (e.g., profit-making vs. health-outcomes);

- Develop skills in analytical fields, with the ability to significantly contribute in a broad range of industries (and moreover to society as a whole) in using skills and education to identify, assess, manage and quantify key findings (e.g., trends, risk, uncertainty) in various situations;

- Work independently on a research project, collating, analysing and reporting on the findings with the capacity to present the results to a broad audience.