Special Purpose Award in Business Analytics
Information and communication (including ICT)
Peter ButlerEmail Phone
Lifelong Learning Centre
Galway Mayo Institute of technology
Lifelong Learning Coordinator
The Certificate in Business Analytics is a conversion course for graduates of Level 7 programmes in any discipline. This will enable students to apply data analysis and business intelligence techniques to their own discipline. They will explore how data is created, stored and accessed as well as how to extract, interpret and present data. They will use tools and techniques to predict future behaviour and ultimately help business gain useful insights and make better decisions. The course will give students a high-level familiarity with databases and they will learn how to use powerful techniques to extract information of interest, work with large data sets (Big Data), and create interactive dashboards for visualising data – interpreting, telling and selling. This course is open to Level 7 or Level 8 graduates from disciplines such as business and finance, as well as those from life and physical sciences or engineering.
Student must hold a Level 7 Major Award, or equivalent, in any discipline. Applicants will be ranked in terms of academic merit on the last working day of each month and offers of places made to those with minimum academic requirements in order of academic merit.
There is no doubt that businesses today have more information available to them than at any time in history. The advances in Information Technology over the past decade, particularly the Internet and Cloud technologies has seen an explosion of information being available from both internal and external sources. So-called “Big Data” describes extremely large data sets that may be analysed computationally to reveal patterns, trends and associations, especially around customer behaviour and interactions. This turns the raw data into actionable information that can be the difference between business success and failure. Yet, many businesses are poorly equipped to deal with such data and lack the skills and competencies to manage it.
On this course, students will learn how to design, develop and query relational databases and explore Big Data databases using post relational database technologies. They will also develop an understanding of the techniques of entity relationship modelling, normalization, decision and predictive modelling and the fundamentals of statistical techniques applied to data analytics. They will be able to manage, explore and cleanse business data using modern BI (Business Intelligence) tools as well as analyse business problems using data mining, data warehousing and other business intelligence toolsets. Students will also develop abilities to design and create business reports and visualisations. All of these skills will be put to practical use in a professional practice project in a work setting (work placement element).
Design and develop relational databases and query using SQL.
Explore Big Data using post relational database technologies (NoSQL).
Understand techniques of entity relationship modelling, normalization, decision and predictive modelling.
Fundamentals of Statistical techniques applied to data analytics.
Manage, explore and cleanse business data using modern BI tools.
Analyse business problems using data mining, data warehousing and other business intelligence toolsets.
Design and create business reports and visualisations.
Programme Learning Outcomes
On completion of this programme the learner will be able to:
1. Design and construct a well-informed business information workflow to solve a business issue.
2. Recognise, understand and appreciate advanced techniques in business analytics.
3. Discuss, plan and implement fundamental techniques in data management and analysis.
4. Describe the limitations of current techniques and technologies in business analytics.
5. Locate and evaluate documentation and information through online research.
6. Apply best practice in the fields of business intelligence and data analytics.
7. Describe advanced concepts in business intelligence and data management in a business context, and the relevant professional and ethical issues.
8. Analyse, model and develop database systems.
Database Design & Development
This module covers the principles of relational database design including primary keys, foreign keys, database schemas, data normalisation, relational integrity tables, views, sequences and indexes. The module will take an in-depth look at Structured Query Language (SQL) and explore data manipulation in SQL: insert, update and delete, SQL queries and built-in SQL functions. On completion of this module the learner will/should be able to design a relational database schema for a software application, devise a set of relational tables and develop a relational database, query a relational database using SQL and evaluate the use of non-relational data storage technologies.
Databases for Big Data
This module introduces students to the use of database management systems for non-relational data models namely graph, document, key value and column databases. An evaluation of the relational data model with respect to these NoSQL data models is assessed along with how to query and manipulate data. Students will learn how these data models cater for transaction management; data distribution; database optimisation and backup. This module will be delivered using a combination of technologies including cloud based and virtual configurations. Assessments and assignments will involve the practical application of the various NoSQL models in environments such as Web content management, Web Crawling Algorithms, Social Networks and Document Search.
Statistics for Business Analytics
This module covers the principles of statistics including probability, combinations and permutations, binomial and normal distributions. Calculation of Mean/Median/Mode/Standard Deviation and the tabulation of data. Frequency distribution and representing data diagrammatically. Data Collection and Sampling and interpreting Statistical Charts and Graphs Forecasting using Time Series. Forecasting using regression. On completion of this module the learner will/should be able to calculate statistical data and represent this diagrammatically, use time series modelling to identify data trends and to make business forecasts, calculate expectation and probability to solve practical business problems, demonstrate an understanding of the wide applicability of quantitative techniques in business and apply statistical and quantitative techniques to solve problems in business.
In this module students will investigate how to manage, explore and cleanse business data for analytics. They will execute model-driven and data-driven analytics using tools such as SPSS and Tableau. They will learn about the classification of data and analytical techniques including time series, regression and generalised linear models. On completion of the module the learner will be able to: generate reports and visualisations, apply data mining methods and develop analytics solutions for finance and marketing functional problems.
This module covers the principles of data warehousing for decision support – data warehouse design and development, data tabulation and cube construction and decision support with data warehousing. The learner will take a detailed look at data mining project management including data preparation and model development, model evaluation and deployment. The learner will also examine data mining case studies. On completion of this module the learner will be able to: create and query a data warehouse, apply Business Intelligence (BI) project management techniques, analyse business problems utilising BI approaches and deploy BI solutions to support decision-making and innovation.
Data Visualisation & Business Reporting
In this module students will learn how to communicate information clearly and efficiently via charts, bar graphs, statistical graphics, scatterplots, heatmaps and information graphics. They will learn how to build dashboards and perform ad-hoc analyses of data-sets. Utilising real-world examples and case-studies, students will explore the many types of data in use today, learn how people perceive different graphical displays, and create visual presentations that make a stronger impact and allow for new interpretations of information. On completion of the module, the learner will be able to: communicate more precisely by pinpointing the most relevant information, apply effective methods for analysing, presenting, and using statistical data, identify the strengths and weaknesses of different data visualisation approaches and learn how to translate simple and complex data into effective visual displays.
Professional Practice Project
The professional practice project (work-placement) component is an integral part of the academic programme of the Certificate in Business Analytics. The aims of the component are to offer the student the opportunity to apply the knowledge and skills gained throughout the course in a relevant work-place setting; facilitate the student in developing the practical competencies and communication skills necessary to function as an effective team member in the work environment. On completion of this module the learner will be able to participate in a team in a business environment as an effective and efficient team member; contribute as an individual contributor and as a full team member; demonstrate an understanding of the main business strategy of the employer and show an understanding of the role of the team and its work in the overall business strategy of the company; take on (minimally) entry-level development and / or analyst roles relating to business analytics. Candidates already in employment will undertake a work-based project centred on business analytics that serves to apply their new acquired skills and competencies to a realistic work-based scenario / problem / data-set. Such candidates will be assigned a dedicated academic supervisor for the duration of the project.
Fully online, all lectures are recorded.
Lifelong Learning Centre
Galway Mayo Institute of technology
1. Apply for the course on www.springboardcourses.ie
2. Send a copy of your Level 7 degree, a copy of your Passport, a copy of a document/ card showing your PPSN and a copy of a document showing your name and address to email@example.com.
3. Applications received each week will be evaluated and places offered until the course is full.
4. Applications under RPL (i.e. do not meet minimum academic entry requirements) will be evaluated only if places remain unfilled on the course the week before it is due to commence.)