Module Details

Module Code: DATA B7002
Full Title: Data Analytics for Business
Valid From:: Semester 1 - 2019/20 ( June 2019 )
Language of Instruction:English
Duration: 1 Semester
Credits:: 10
Module Owner:: Colin Cooney
Departments: Unknown
Module Description: Global trends are driving skills needs in data analysis techniques. Organisations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value in terms of enhanced productivity and business gain. This module will enable candidates to uncover insights and trends by understanding how to harness relevant organisational data, which leads to the identification of new opportunities, smarter business moves, more efficient operations, higher profits and more satisfied customers.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Explain the field of data analytics, demonstrating an understanding of its role in organisational decision making.
MLO2 Discuss the concept of organisational data as a business asset through leading and advancing the data quality agenda in an organisation, while recognising the practicalities of data management and data governance.
MLO3 Collect, integrate and analyse business data and employ business intelligence tools to support evidenced-based decisions to drive business change and improve business processes.
MLO4 Employ self-service business intelligence tools and techniques for requirements elicitation and analysis to make evidence-based decisions to drive business change and improve business processes.
MLO5 Plan and present findings of problem-solving efforts.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named DkIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
 
Module Indicative Content
Introduction to Business Analysis & Data Analytics
Role in industry, key concepts, global trends, knowledge areas, tasks, techniques, competencies, terminology, big data, advantages & challenges of big data.
Business Intelligence & Organisational Strategy
Alignment of business intelligence & organisational strategy, data analytics as a core business tool, data analytics as a complementary business tool, exposure to real-world implementations in business.
Business Analysis Planning, Monitoring & Elicitation
Problem statement, contextualise problem, identification of KPIs & metrics, plan & manage business analysis approach, activities and communication.
Data Analytics Lifecycle
Key Roles in the Data Analytics process, Discovery (Resources, Stakeholders, Developing hypotheses, Potential data sources), Data Preparation (Analytics sandbox, Data Inventories, Data conditioning, “Dirty” data), Model Planning, Selection & Building, Visualisation & Communication, Operationalising & Monitoring Results.
Data Collection & Sources
Systematic approaches to data collection, Data collection process (Purpose, Identification of requirements, Elicitation, Validation), Sampling methods (Random, Quasi-random, Non-random, Sample size), Recognising and minimising bias, The importance of rigorous documentation.
Fundamental Analytics Skills
Understand statistical terms and concepts, data types & importing, Data & Tables (Percentage, Percentage change, Index, Frequency, Cumulative), Filtering data (Boolean conditions, Database functions), Visualising data (Graphing, Line & Scatter Plots, Histograms, Analysing & Interpreting), Descriptive & predictive management statistics, Regression (Simple linear, Multiple linear, Correlation, Model evaluation), Uncertainty & Probability.
Solution Assessment, Validation & Presentation
Assess proposed solution, allocate requirements, assess organisational readiness, define transition requirements, validate solution, evaluate solution performance, make evidenced-based decisions, create effective charts and visualisations. solution assessment, validation & presentation (to non-technical and managerial staff), alignment with business strategies,
Underlying Competencies
Working with interdisciplinary teams, project management, analytical thinking and problem-solving, behavioural characteristics, Business knowledge, communication skills, interaction skills, spreadsheet technology: formulas, formatting, functions, filtering, sorting, tabling.
Module Assessment
Assessment Breakdown%
Course Work100.00%
Module Special Regulation
 

Assessments

Full-time

Course Work
Assessment Type Continuous Assessment % of Total Mark 100
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,4,5
Duration in minutes 0
Assessment Description
This assessment involves the design and development of analytical models and case study analysis and reporting. Students will be assessed on their ability to understand, articulate and frame the problem. Candidates will then be required to use Business Intelligence tools to implement analytical techniques, analyse the data and report on the findings.
No Project
No Practical
No Final Examination

Part-time

Course Work
Assessment Type Continuous Assessment % of Total Mark 100
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,4,5
Duration in minutes 0
Assessment Description
This assessment involves the design and development of analytical models and case study analysis and reporting. Students will be assessed on their ability to understand, articulate and frame the problem. Candidates will then be required to use Business Intelligence tools to implement analytical techniques, analyse the data and report on the findings.
No Project
No Practical
No Final Examination
Reassessment Requirement
No repeat examination
Reassessment of this module will be offered solely on the basis of coursework and a repeat examination will not be offered.

DKIT reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full-time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Facilitated learning using a variety of interactive pedagogical techniques. Every Week 2.00 2
Practical Contact Lab based practical Every Week 2.00 2
Online (Contact) Contact Readings, videos, mini case studies, discussion forums, multiple choice questionnaires. Every Week 1.00 1
Directed Reading Non Contact Prescribed reading on the subject area as directed by the module leader. Every Week 6.00 6
Independent Study Non Contact Wider reading and reflection on the subject area using a variety of methods. Particular focus will be placed on application of learning. Every Week 5.00 5
Total Weekly Learner Workload 16.00
Total Weekly Contact Hours 5.00
Workload: Part-time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Facilitated learning using a variety of interactive pedagogical techniques. Every Week 2.00 2
Practical Contact Lab based practical Every Week 1.00 1
Online (Contact) Contact Readings, videos, mini case studies, discussion forums, multiple choice questionnaires. Every Week 1.00 1
Directed Reading Non Contact Prescribed reading on the subject area as directed by the module leader. Every Week 6.00 6
Independent Study Non Contact Wider reading and reflection on the subject area using a variety of methods. Particular focus will be placed on application of learning. Every Week 6.00 6
Total Weekly Learner Workload 16.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Marr B.. (2017), Data Strategy: How To Profit From A World Of Big Data, Analytics And The Internet Of Things” by Bernard Marr, Kogan Page, [ISBN: 9780749479855].
  • Harvard Business Review. (2018), HBR Guide to Data Analytics Basics for Managers (HBR Guide Series), Harvard Business Review Press, [ISBN: 1633694283].
  • Yao M., Zhou A., Jia M.. (2018), Applied Artificial Intelligence: A Handbook For Business Leaders, TOPBOTS, [ISBN: 0998289027].
Supplementary Book Resources
  • Marr, M.. (2016), Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results, John Wiley & Sons, [ISBN: 1119231388].
  • Cadle J., Eva M., Hindle K. et al.. (2014), Business Analysis, 3rd. BCS, The Chartered Institute for IT, [ISBN: 178017277X].
  • International Institute of Business Analysis. (2015), A Guide to the Business Analysis Body of Knowledge, [ISBN: 1927584027].
  • Sherman, R.. (2014), Business Intelligence Guidebook: From Data Integration to Analytics, Morgan Kaufmann, [ISBN: 012411461X].
  • Cody, I.. (2016), Data Analytics: Practical Data Analysis and Statistical Guide to Transform and Evolve Any Business, CreateSpace Independent Publishing Platform, [ISBN: 1536875376].
  • Quirk, T., Rhiney, E.. (2016), Excel 2016 for Marketing Statistics: A Guide to Solving Practical Problems (Excel for Statistics), 1st. Springer, [ISBN: 331943375X].
This module does not have any article/paper resources
Other Resources