Module overview
This course provides part of the essential knowledge and skills required for conducting the Final Project module in the final year.
It introduces the fundamentals of data analytics programming using appropriate software tools. Upon successful completion, you will be able to read, access, manage and manipulate large datasets; solve data-driven problems; save programs and datasets; apply conditional logic; combine multiple data sources; and produce tables and data listings. You will also gain experience with core data analysis technique.
Linked modules
Pre-Requisites: MANG1019 or MANG1047
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- how to manipulate, analyse, visualise and report data using data analytics programming tools.
- key computer programming concepts for managing and analysing ‘big data';
- the fundamental features of data analytic programming tools and how they are used to manage, analyse and report data;
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- use your analytic skills in problem solving;
- communicate technical ideas to non-specialist managers.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- read structured and unstructured data stored in popular formats such as excel worksheets, text tables into data analytics programming tools;
- store data using appropriate data management structures within such tools.
Syllabus
The topics covered in this module will include:
• Overview of data analytics programming tools:
Submitting programs or scripts; understanding programming syntax; working with data libraries or storage structures.
• Introduction to reading data and adding attributes:
Importing data from common formats such as Excel worksheets and delimited text files; defining and modifying data attributes.
• Manipulating and combing data:
Merging and joining datasets; producing summary reports; enhancing reports using available formatting or output-delivery features; exporting to multiple datasets or observations.
• Reading raw data and performing data transformations:
Applying input controls for custom data reading; transforming character and numeric values; using debugging techniques to verify and troubleshoot code.
• Iterative processing and data restricting:
Using loops and arrays; reshaping datasets through rotation, transposition or equivalent restructuring methods; analysing relationships among variables: using appropriate statistical or analytical procedures.
Learning and Teaching
Teaching and learning methods
Teaching methods include:
• Lectures
• Interactive case studies
• Computer labs
• Directed reading
• Private/guided study
Learning activities include:
• Introductory lectures
• 2 assignments (individual written coursework)
• Case study
• In class debate and discussion
• Private study
• Use of video and online materials
| Type | Hours |
|---|---|
| Independent Study | 114 |
| Teaching | 36 |
| Total study time | 150 |
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Class participation
- Assessment Type: Formative
- Feedback:
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Program | 20% |
| Data analysis project | 80% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Data analysis project | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Data analysis project | 100% |
Repeat Information
Repeat type: Internal & External