Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Introduction To Programming (Python) | |||
| Reference | CM1115 | Version | 2 |
| Created | June 2022 | SCQF Level | SCQF 7 |
| Approved | January 2022 | SCQF Points | 15 |
| Amended | July 2022 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To provide the student with the fundamental knowledge and skills required to create computer programs within the context of analysing business data. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critically appraise a range of tools, programming languages, interfaces and packages for data exploration and analysis in the context of business analytics. |
| 2 | Understand the main concepts of core programming. |
| 3 | Use existing methods and functions to wrangle and manage data. |
| 4 | Apply programming skills to present a business case. |
| Indicative Module Content |
|---|
| Overview of programming languages used for business analytics, fundamentals of programming (logic statements, conditional statements, loops, functions, classes, etc.), fundamentals of data analytics and visualisation, introduction to programming environments (IDE, console, Jupyter notebook), data structures (numbers, variables, strings, lists, tuples, NumPy arrays, etc.), introduction to pandas data frames, reusing existing functionalities, libraries and APIs, development of solutions for the business context, sharing code and working remotely & effectively (online notebooks and repositories e.g. GitHub, Kaggle, Colab, etc.). |
| Module Delivery |
|---|
| Key concepts are introduced through the lectures. The main emphasis of the course will be focused on the lab sessions where individual lab assignments will be interspersed with demonstrations of current techniques and practices. This combination will allow students to develop an understanding of the theoretical underpinning of modern programming structures, whilst promoting development of proficiency in the practical application of software development and data wrangling and management. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 36 | N/A |
| Non-Contact Hours | 114 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| 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: | Portfolio of written work. | ||||
| 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 | Lutz, M. (2013). Learning Python (5th Ed). O’Reily. |
| 2 | Padmanbhan, T.R. (2016). Programming with Python. [E-Book] |
| 3 | Hetland, M.L. (2017) Beginning Python: From Novice to Professional (3rd Ed.). [E-Book] |
| 4 | Parker, J. (2017). Python: An Introduction to Programming. [E-Book]. |
| 5 | Udemy. Python for Business Analysis and Excel. https://www.udemy.com/course/python-for-business/ |
| 6 | Moffitt, C. Practical Business Python. https://pbpython.com/ |
| 7 | Python Language Specification. https://www.python.org |