Data Management & Analytics

Module Information

2016-17

Module Title: / Data Management & Analytics
Module Code: / B8IS100
Programme: / Information Systems Business Degree
Delivery Mode: / FT PT
Lecturing Team / Name / Email / Telephone
Module Leader: / Darren Redmond / / 087-9806683
Indicative Timetable
Assignments: / CA 1 / CA 2 / CA3 / CA4
Publication Date / Week 3 / Week 8 / Week 15 / Week 2
Submission Date / Week 5 / Weeks 10 / Week 17 / Week 23
Feedback Date / Week 7 / Week 12 / Week 19 / Week 25
Exam Date / May 2017

This module is part of a Programme Accredited by QQI and delivered by Dublin Business School in DBS, Dublin, Ireland.

Table of Contents

Data Management & Analytics

Learning Activities:

Recommended Reading:

Supplementary reading:

Journals:

On-line resources:

Assessment Details:

Course Structure and Delivery:

Teaching Plan:

General Assessment Submission Requirements for Students:

What is referencing and why is it necessary?

Data Management & Analytics

For information about this module see the module guide on moodle.

Learning Activities:

Learning will take place via the following activities:

  • Lectures, case-studies, guest speakers and in-class discussions.
  • Lab demonstrations with notes and practical exercises for the students to try out new concepts introduced by the lecturer.
  • Video Presentations
  • On-line activities: quizzes, blogs and forums

Recommended Reading:

Supplementary reading:

Journals:

(The journals listed below are indicative only – there are many more).

  • MIS Quarterly
  • Data Mining & Knowledge Discovery
  • International Journal of Data Mining, Modelling and Management
  • Information Week

On-line resources:

(The resources listed below are indicative only – there are many, many more).

  • Moodle:
/
  • Data Protection Website:
/
  • Data Blogs from O’Reilly Media:
/
  • Data Mining & Analytics Resources:
/
  • Information Week - Online journal:
/
  • Data Science Central:
/
  • Library –Subject Portal:
/

Assessment Details:

50% Exam as follows:

May 2017 – The written exam will address the theoretical component of the module.

Past exam papers are available on Moodle.

50% Continuous Assessment as follows:

  1. CA1 – Fusion Tables(Individual): 10%.
  1. CA2 – R Programming (Individual) – 10%.

There will be various activities/discussions posted on Moodle which students are required to participate in throughout the year.

  1. CA3 – Big Data Analytics(Individual): 10%
  1. Research project and presentation(Individual): 20%.

Presentations will take place after submission, during class time in weeks 23, 24.

Course Structure and Delivery:

The nature of the module is one of discussion and practical lab work, with high levels of student participation expected. One hour per week will be dedicated to developing practical skills in a computer lab, which will have an impact on your understanding of the theory aspects of this module. The remaining two hours per week will be classroom/theory based.

To gain maximum benefit from this class you should attend all classes, review notes and read relevant chapters in the recommended text books. Students are required to enhance their research skills by sourcing, reading and discussing articles relevant to the topics introduced in class.

Should any student unavoidably miss a session, it is strongly recommended that they familiarise themselves with the relevant notes from Moodle and attempt any missed work, before the next class.

Teaching Plan:

Below is an indicative teaching plan, please note that this plan is subject to alteration during the academic year.

DATA MANAGEMENT & ANALYTICS - September 2016 Intake
Week 1 / Introduction to Data Management & Analytics
Week 2/3 / Information Systems – Fusion Tables
Week 4/5 / Entity Relationship Diagram (CA1 due – 10%)
Week 6/7 / Data Warehousing
Week 8/9 / Try R Programming
Week 10/11 / Data Management(CA2due – 10%)
Week 12/13 / Business Intelligence
Week 14/15 / Big Data
Week 16/17 / Hadoop & Data Analytics (CA3due – 10%)
Week 19/20 / Regression Analysis / Analysis of Variance / Time Series Analysis
Week 21/22 / Data Privacy & Security
Week 23/24 / Knowledge Management (**CA4due – 20%)
Week 25 / Revision.
**Presentations for CA4

General Assessment Submission Requirements for Students:

  1. Online assignments must be submitted no later than the stated deadline.
  1. All relevant provisions of the Assessment Regulations must be complied with.
  1. Extensions to assignment submission deadlines will be not be granted, other than in exceptional circumstances. To apply for an extension please go to and download the Assignment Extension Request Form.
  1. Students are required to retain a copy of each assignment submitted, and the submission receipt.
  1. Assignments that exceed the word count will not be penalised.
  1. Students are required to refer to the assessment regulations in their Student Guides and on the Student Website.
  1. Dublin Business School penalises students who engage in academic impropriety (i.e. plagiarism, collusion and/or copying). Please refer to the referencing guidelines for information on correct referencing using URKIND.

What is referencing and why is it necessary?

Please follow this link to the Harvard Style Referencing Guide - all referencing is required in this format.

The School of Arts generally use APA Referencing , information is available under DBS library guides on