Higher Diploma in Data Analytics and Visualisation (Higher Diploma in Data Analytics and Visualisation)

National University of Ireland, Galway (1DAV1)
Key Programme Details
Award

Higher Diploma in Data Analytics and Visualisation

NFQ Level

Level 8 About NFQ

Delivery Method

Classroom, Blended

Mode

Full Time

ECTS Credits

60

Department

Computer Science

General Information
Contact

Dr. Josephine Griffith

Email

josephine.griffith@nuigalway.ie

Phone

+353-91-493717

Address

School of Computer Science
National University of Ireland,
Galway

Role

Programme Director

Important Dates
Application Deadline

26/08/2021

Start Date

06/09/2021

End Date

31/08/2022

About this Course

The course builds on existing strengths in Data Science and Analytics in the School of Computer Science and the Data Science Institute, and our experience in running a successful Masters in Data Analytics.

The goal of this programme is to provide a conversion route for graduates from a variety of disciplines to become highly competent professionals in the field of data analytics and visualisation. Upon completion, graduates will be able to:

- Demonstrate expert knowledge of data manipulation and analysis using leading tools and techniques.
- Assess business requirements in terms of data analytics solutions and apply the appropriate statistical methods and analysis strategies to develop high value business analytics solutions.
- Critically assess and evaluate appropriate strategies for adding business value to data using business intelligence tools alongside leading edge technologies.
- Create high value business dashboards and applications using web development and data visualisation techniques.

The programme has a number of core elements:

- Immersion in fundamental database, programming and software development techniques.
- A solid foundation in statistical and analysis methods.
- Proficiency in theory and application of data analysis, visualisation and business intelligence techniques.
- Capstone project to deepen and demonstrate students' acquired skills.
- An accredited work placement allowing participants to gain relevant experience and also provide Industry Partners with an opportunity to assess potential recruits.

On completion of the programme, graduates will be eligible to take our highly successful MSc Data Analytics or MSc Artificial Intelligence, providing a deeper and more specialised training in advanced Data Science or Artificial Intelligence topics such as Machine Learning. Transition to these programmes is contingent on spaces and achieving a minimum of a high 2:1 in the Higher Diploma at the discretion of the programme director.

Objectives

Participants will follow a broad immersive set of modules in the fundamentals of programming, data analytics and
visualisation. The pace of delivery will be significantly higher than for normal undergraduate programmes, and there will be a significant Data Analytics and Visualisation Project as well as a Work Placement.

Entry Requirements

Applicants are normally required to hold a minimum of a level 8 honours qualification (2.2 or higher) or equivalent on the NCQ in a cognate discipline. Graduates with a Level 7 degree and relevant practical industry experience in the area of computing and information technology will also be considered. Graduates from non-STEM (Science, Technology, Engineering, Mathematics) disciplines such as languages will be welcomed, but will need to demonstrate an aptitude for logical thinking and problem solving. The application process may include interviews and/or aptitude tests, at the discretion o fthe Programme Director. RPL applications are also welcome and can be completed by contacting the Programme Director.

Long Description

SEMESTER 1
Introduction to Programming in Python: In this module you will be introduction to the concept and techniques of programming using the Python language. The contents of the module include Object-Oriented Programming, Algorithms and Information Processing, File Input/Output, Data Structures, Graphics and Graphical User Interfaces.

Databases: This module introduces modern database concepts and design techniques. Topics covered include database architectures and the different commercial database models, query languages (e.g. SQL), management (e.g. backup, recovery, performance). Learners will learn how to connect to and use databases from their software programs.

Human Computer Interaction On successful completion of this module the learner will be able to: - Elaborate the importance of design in professional and social contexts and the critical role of users in the systems design process. - Distinguish between human cognition and emotion and assess their role in effective interaction system design. - Identify the roles of human agents and those of digital agents in any interaction. - Develop the knowledge and skills necessary to analyse, design and evaluate good quality interactive systems. - Competently differentiate between various Interaction Design processes or approaches. - Analyse technological developments and innovations in social, educational and leisure computing and their implications for user experience and interaction design.

Statistics for Data Science I Introduction to probabilistic and statistical methods needed to make reasonable and useful conclusions from data. Topics include probabilistic reasoning, data generation mechanisms, modern techniques for data visualisation, inferential reasoning and prediction using real data and the principles of reproducible research. The module will rely heavily on R (a free open source language) and will include examples of datasets collected in a variety of domains.

Internet Programming This module introduces learners to the concepts and techniques for building Web Applications, using HTML, JavaScript, CSS and Bootstrap. Learners will build full client-server applications using tools such as Node-JS and MongoDB, creating web applications that interact with a database via a server-side application.

SEMESTER 2

Statistics for Data Science II This module will provide an introduction to commonly used techniques in statistics when analysing data from experiments and observational studies. Topics include classical and modern methods in interval estimation, regression models for prediction problems, modern approaches for visualising multivariate data and the principles of reproducible research.

Applied Data Science with R In this module learners will use the R programming language and popular tidyverse libraries for exploratory data analysis, data visualisation, data modelling and data transformation. Learners will be able to: - Perform data cleaning, manipulation and wrangling techniques to specified data problems. - Implement appropriate data visualisation techniques to examine real world datasets. - Investigate statistical modelling techniques. - Develop best practice in terms of reproducible documentation and version control.

Business Intelligence In this module we introduce fundamental Business Intelligence techniques and tools used for manipulation of data and extraction of information. Techniques include Database and Data Warehouse Technologies and Architectures, Data Integration and ETL (Extract, Transform, Load) concepts and tools, Data Modelling, NoSQL, OLAP, KPIs (Key Performance Indicators), Dashboarding, Querying and Reporting. Learners will get experience using commercially important tools such as Tableau and Power BI.

Data Visualisation: this module is concerned with techniques and technologies for the visual representation of data and the results and evaluation of data analytic processes. This module takes a practical approach to introducing learners to the strengths and weaknesses of human perception, and the use of best practices to represent complex and large data stories using visual primitives. The module demonstrates the role of visualisation in exploratory data analysis and its fundamental role in explaining data analytics outcomes. The practical work in this module is done using the R programming language.

FULL YEAR
Industrial Data Analytics Project. Learners will apply Data Analytics and Visualisation to solve real-world problems. You will be presented with a business-related problem and design and implement a solution using state of the art techniques.

Timetable Info

http://www.nuigalway.ie/science-engineering/school-of-computer-science/currentstudents/timetables/

Delivery Location

National University of Ireland, Galway

Delivery Notes

Primarily classroom-based, supplemented by online delivery as required dependent on Covid restrictions.

Admissions Contact Details
Contact Person

Ms. Geraldine Healy

Address

School of Computer Science
National University of Ireland
Galway

Phone

+353-91-493836

Email

geraldine.healy@nuigalway.ie

Media
Course Overview

Course Overview / Key Facts/ Course Outline / Why Choose This Course? / Course Fees / Find Out More

https://www.nuigalway.ie/courses/taught-postgraduate-courses/data-analytics-and-visualisation.html