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) in Third Year.

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.

Core Modules

CS1106 Introduction to Relational Databases (5 credits)
CS1112 Foundations of Computer Science I (5 credits)
CS1113 Foundations of Computer Science II (5 credits)
CS1117 Introduction to Programming (15 credits)
AM1054 Mathematical Software (5 credits)
MA1058 Introduction to Linear Algebra (5 credits)
MA1059 Calculus (5 credits)
ST1050 Statistical Programming in R (5 credits)
ST1051 Introduction to Probability and Statistics (5 credits)

and modules to the value of 5 credits to be chosen from the following:

Elective Modules
AM1053 Introduction to Mathematical Modelling (5 credits)
ST1401 Introduction to Operations Research (5 credits)

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

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.

Core Modules
CS2208 Information Storage and Management I (5 credits)
CS2209 Information Storage and Management II (5 credits)
CS2513 Intermediate Programming (5 credits)
CS2514 Introduction to Java (5 credits)
CS2515 Algorithms and Data Structures I (5 credits)
CS2516 Algorithms and Data Structures II (5 credits)
MA2055 Linear Algebra (5 credits)
MA2071 Multivariable Calculus (5 credits)
ST2053 Introduction to Regression Analysis (5 credits)
ST2054 Probability and Mathematical Statistics (10 credits)

and modules to the value of 5 credits to be chosen from the following:

Elective Modules
AM2052 Mathematical Modelling (5 credits)
ST2402 Modelling and Systems for Decision Making (5 credits)

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

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

Core Modules
CS3204 Cloud Infrastructure and Services (5 credits)
CS3205 Data Visualization for Analytics Applications (5 credits)
CS3220 Work Placement DSA (10 credits)
CS3306 Workplace Technology and Skills (10 credits)
CS3318 Advanced Programming with Java (5 credits)
CS3509 Theory of Computation (5 credits)
ST3053 Stochastic Modelling I (5 credits)
ST3061 Statistical Theory of Estimation (5 credits)
ST3069 Generalised Linear Models (5 credits)
ST3070 Statistical Theory of Hypothesis Testing (5 credits)

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

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.

Core Modules
CS4701 Analytics Project for Computer Science (15 credits) or
ST4092 Data Analytics Project (15 credits)
and
CS4704 Algorithms and Data Structures for Analytics (5 credits)
CS4705 Computational Machine Learning (5 credits)
ST4060 Statistical Methods for Machine Learning I (5 credits)
ST4061 Statistical Methods for Machine Learning II (5 credits)
ST4069 Multivariate Methods for Data Analysis (10 credits)

and modules to the value of 15 credits to be chosen from the following:

Elective Modules
AM2061 Computer Modelling and Numerical Techniques (5 credits)
AM3064 Computational Techniques (5 credits)
AM4006 Mathematical Modelling of Biological Systems with Differential Equations (5 credits)
AM4010 Topics in Applied Mathematical Modelling (5 credits)
CS4150 Principles of Compilation (5 credits)
CS4405 Multimedia Compression and Delivery (5 credits)
CS4407 Algorithm Analysis (5 credits)
CS4413 Future and Emerging Technologies (5 credits)
CS4614 Introductory Network Security (5 credits)
CS4615 Computer Systems Security (5 credits)
CS4616 Distributed Algorithms (5 credits)
CS4620 Functional Programming I (5 credits)
CS4626 Constraint Programming and Optimisation (5 credits)
CS4710 Programming Paradigms for Big Data (5 credits)
ST3054 Survival Analysis (5 credits)
ST4064 Time Series (5 credits)

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

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.