2024/5, Trimester 2, Blended,
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Occurrence: | 001 |
Primary mode of delivery: | Blended |
Location of delivery: | MERCHISTON |
Partner: | |
Member of staff responsible for delivering module: | Kehinde Babaagba |
Module Organiser: | |
Student Activity (Notional Equivalent Study Hours (NESH)) |
Mode of activity | Learning & Teaching Activity | NESH (Study Hours) | NESH Description |
Face To Face | Lecture | 20 | This will provide a structured exploration of key concepts such as data manipulation, data preprocessing and basic statistics, using Python libraries such as Pandas and Numpy. These sessions combine theoretical insights with practical coding demonstrations to equip learners with foundational skills for real-world data analysis. |
Face To Face | Practical classes and workshops | 20 | In the practical classes, students will engage in hands-on exercises to apply data analysis techniques using Python libraries. This will develop skills in working with data, data cleaning and basic statistics to enhance their understanding of core data science concepts. |
Online | Guided independent study | 160 | This will involve students working through structured learning materials including coding exercises and problem-solving tasks with regular check-ins for feedback and guidance. This will encourage self-directed learning while providing support to deepen understanding of core data science concepts using Python. |
| Total Study Hours | 200 | |
| Expected Total Study Hours for Module | 200 | |
Assessment |
Type of Assessment | Weighting % | LOs covered | Week due | Length in Hours/Words | Description |
Practical Skills Assessment | 40 | 1~2 | Week 8 | HOURS= 12 hours | Practical Skills Assessment: The first practical skills assessment (40% of final mark) is designed to cover most of the fundamental theory of the module, covering LO1,2. |
Practical Skills Assessment | 60 | 1~2~3 | Week 13 | HOURS= 30 hours | Practical Skills Assessment:The second practical skills assessment, weighted at 60% of the final mark, requires students to apply the concepts learned to manipulate, process and analyse data. It is designed to reinforce LO1,2 and will also assess LO3. |
Component 1 subtotal: | 100 | | |
Component 2 subtotal: | 0 | | | | |
Module subtotal: | 100 | | | | |