Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Programming for Business Analytics | |||
| Reference | CMM202 | Version | 3 |
| Created | February 2024 | SCQF Level | SCQF 11 |
| Approved | July 2018 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| This module teaches students to process, manipulate, visualize and analyse data using Python. Students will explore the capabilities of existing libraries and work on projects where they develop programming and data analytics skills in a business analytics context. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Create computational solutions to data analytics problems. |
| 2 | Deal with extending core Python functionality by leveraging existing modules and libraries. |
| 3 | Generate reproducible data analytics solutions. |
| 4 | Produce a data analytics solution within a business analytics context. |
| Indicative Module Content |
|---|
| Data cleaning, preparation and wrangling; plotting and visualization; advances data analytics techniques matched to business requirements. Introduction to and use of Python libraries such as numpy, pandas, matplotlib and nltk to process and analyse a range of data types. |
| Module Delivery |
|---|
| Core concepts and examples will be introduced in lectures. Practical skills will be developed through structured lab exercises and coursework exercises. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | 30 |
| Non-Contact Hours | 120 | 120 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | 150 |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Prepare and visualise data within a business case context. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| An overall minimum grade of a D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1. |
| B | The student needs to achieve a B in C1. |
| C | The student needs to achieve a C in C1. |
| D | The student needs to achieve a D in C1. |
| E | The student needs to achieve an E in C1. |
| F | The student needs to achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | MCKINNEY, W. (2013) Python for Data Analysis. Data Wrangling with Pandas, NumPy, and IPython. O'Reilly |
| 2 | LUTZ, M. (2013). Learning Python. (5th Ed.): O’Reilly |
| 3 | PADMANBHAN, T.R. (2016). Programming with Python. ELECTRONIC BOOK |
| 4 | Python Language Specification: https://www.python.org/ |