| Module Title |
Analytics Tools and Programming |
| Module Code |
BAA1088 (ITS: SB5001) |
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Faculty |
DCU Business School |
School |
DCU Business School |
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NFQ level |
9 |
Credit Rating |
10 |
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Description
This module focuses on programming (SQL and Python) for business analytics and machine learning, and it equips students to manage, analyse, and present data effectively.
Mastering these skills helps to handle the complete business analytics pipeline, from extraction and analysis to visualisation, enabling to make data-driven business decisions and present insights clearly to stakeholders.
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Learning Outcomes
1. 1DF17260-3BEA-0001-F774-1FA0DEF03EA0
2. Understand the key concepts of data analytics, the characteristics of big data and how organisations can make use of them.
4. 7,6
5. 1
6. 1DF17261-1010-0001-C3DD-15F0131017D1
7. Understand different data preparation, visualisation and analysis techniques and explain the benefits and limitations of
different techniques.
9. 7,6,11,10
10. 2
11. 1DF17261-9C60-0001-6072-DD24C10013E9
12. Evaluate, select and apply appropriate tools, techniques and frameworks to analyse a complex business-related issue.
14. 7,6,8,11,9,10
15. 3
16. 1E266CA6-87A9-0001-FCEE-1221852A19A1
17. Understand, identify, analyse and assess the legal, regulatory and ethical risks associated with analytics projects.
19. 7,6,11,9,10
20. 4
21. 1DF17261-6EE2-0001-7230-1DA01000136E
22. Evaluate, select and apply appropriate project management methodologies and techniques to manage a data analytics project.
24. 7,6,8,11,9,10
25. 5
26. 1E266CA6-EFD2-0001-2CFF-1EA01C37FC30
27. Prepare and cost a detailed project plan for an analytics project including a data management plan.
29. 7,6,8
30. 6
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| Lecture | 44 | Lectures + Workshops/Tutorials | | Tutorial | 11 | Additional Tutorials | | Independent Study | 100 | Preparation for, and reading after the lectures | | Online activity | 45 | Online training using Kubicle | | Assignment Completion | 50 | Group assignment work: research and completion |
| Total Workload: 250 |
|
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| Section Breakdown | | CRN | 12267 | Part of Term | Semester 1 | | Coursework | 100% | Examination Weight | 0% | | Grade Scale | 40PASS | Pass Both Elements | N | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Mathieu Mercadier | Module Teacher | |
|
| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Group project | Business Analytics Project
Find a real-world company dataset. Using a challenge-based learning (CBL) framework, build an end-to-end business analytics pipeline that explicitly ties the data and model to a concrete operational decision, a named KPI, and a responsible deployment plan.
The work must cover EDA, cleaning, preprocessing, feature engineering, a justified train-test split, all must align to the business question. Then, select and train relevant machine learning algorithms. Assess the error, compare models fairly, and translate results into actionable business insights. Include a brief UN SDG and ethics/GDPR | 30% | Week 12 | | In Class Test | SQL Assessment | 30% | Week 6 | | In Class Test | Python Assessment | 40% | Week 12 |
| Reassessment Requirement Type |
Resit arrangements are explained by the following categories;
RC1: A resit is available for both* components of the module.
RC2: No resit is available for a 100% coursework module.
RC3: No resit is available for the coursework component where there is a coursework and summative examination element.
* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml
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Indicative Content and Learning Activities
SQL
Learn to use SQL to extract and manage data from relational databases.
Develop skills in writing queries, ERD, managing large datasets, performing data joins, and optimising database operations, all crucial for preparing data for analysis.
Python
Introduction to Python programming for data manipulation, analysis, and automation.
Learn to use libraries like NumPy for numerical computations, Pandas for data handling, Matplotlib for basic visualisations, and Scikit-learn for machine learning.
These skills help automate data processing tasks and apply advanced analytics techniques, such as machine learning models (see in BAA1053, semester 2), to gain deeper insights.
Business Analytics and Machine Learning
Introduction to Business Analytics, Data, Visualisation, and Machine Learning.
The focus is on introducing core concepts, terminology, and the end-to-end analytics/ML workflow: problem framing, data preparation, visualisation, modelling, evaluation, and communication.
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Indicative Reading List
Books:
- IIBA: 2015, A Guide to the Business Analysis Body of Knowledge, 5, 6, 8, 10, 11, 978-1-927584
- Behrman, K.: 2022, Foundational Python for Data Science, Addison Wesley,
- Camm, J., D., Cochran, J., J., Fry, M., J., Ohlmann, J., W.: 2023, Business Analytics, Cengage,
- Evans, J., R.: 2020, Business Analytics, Pearson,
- Fenner, M.: 2020, Machine Learning with Python for Everyone, Pearson,
- Lambert, K.: 2019, Fundamentals of Python: Data Structures, Cengage,
- Liang, Y., D.: 2023, Introduction to Python Programming and Data Structures, Pearson,
- Mathur, P.: 2019, Machine Learning Applications using Python – Cases Studies from Healthcare, Retail, and Finance, APress,
- Müller, A., C., Guido, S.: 2016, Introduction to Machine Learning with Python: A guide for Data Scientists, O’Reilly Media,
- Sharda, R., Delen, D., Turban, E.: 2021, Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, Pearson,
- Shellman, M., Afyoundi, H., Pratt, P., J., Last, M., Z.: 2023, A guide to SQL, Cengage,
- Stephens, R.: 2022, SQL in 24 Hours, Sams Teach Yourself, Pearson,
Articles:
None |
Other Resources
- 1: Online Tutorial, Kubicle, SQL, Python Fundamentals, Machine Learning with Python, Kubicle
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