Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Programming Concepts for Business Analytics | |||
| Reference | CMM201 | Version | 3 |
| Created | June 2022 | SCQF Level | SCQF 11 |
| Approved | July 2018 | SCQF Points | 15 |
| Amended | July 2022 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| This module will introduce students to fundamental programming principles and concepts within the context of creating solutions for business analytics. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critically appraise the use of Python, Jupyter, and appropriate packages in the context of data analytics. |
| 2 | Demonstrate a critical understand of core programming techniques and concepts. |
| 3 | Use existing libraries and coding techniques to perform data management, data analysis and data visualization tasks. |
| 4 | Apply programming skills to business decision making problems. |
| Indicative Module Content |
|---|
| 1. An overview of programming languages and tools typically used for developing Data Analytics and Visualisation solutions. 2. Introduction to Programming Logic and Design: The programming environment; object-oriented programming concepts; Variables and Simple Data Types; control structures (if, while); working with lists; working with strings; decision structures; dictionaries; input and output; functions; classes; files, interpreting errors and exceptions; testing and simple debugging of code 3. Reusing existing functionality: libraries and APIs: developing and/ or extending existing solutions within a business analytics context. 4. Tools and techniques for sharing software solutions. |
| Module Delivery |
|---|
| 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 | 36 | 36 |
| Non-Contact Hours | 114 | 114 |
| 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: | A coursework demonstrating the ability to use Python to solve simple coding tasks, and to use Jupyter notebooks and third party-packages to solve data analytics problems. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| Component 1 (coursework) comprises 100% of the module grade. To pass the module, a D grade is required. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | A in Component 1 |
| B | B in Component 1 |
| C | C in Component 1 |
| D | D in Component 1 |
| E | E in Component 1 |
| F | F in Component 1 |
| 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. (2th Ed.): O'Reilly |
| 2 | HARRISON M., PETROU T. (2020) Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python, (2nd Edition) |
| 3 | LUTZ, M. (2013). Learning Python. (5th Ed.): O'Reilly |
| 4 | PADMANBHAN T.R. (2016)Programming with Python. ELECTRONIC BOOK |
| 5 | HETLAND, M.L.,(2017) Beginning Python : From Novice To Professional. 3rd Ed, ELECTRONIC BOOK |
| 6 | PARKER J.(2017. Python : An Introduction To Programming ELECTRONIC BOOK |
| 7 | Python Language Specification: https://www.python.org/ |