Core Module Information
Module title: Data Science with Python

SCQF level: 07:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET07113
Module leader: Kehinde Babaagba
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

There are no pre-requisites for this module to be added

Description of module content:

The aim of this module is to provide an introduction to Data Science using Python programming language. The module provides an introduction to Python while exploring how it is applied in the context of data manipulation and processing. Through the case studies and practical exercises, students will develop the skills necessary to gain insights from data. The syllabus includes topics such as:• An introduction to concepts of Data Science• An introduction to building scripts using Python• Core programming and language concepts, such as data types, control structures, functions, importing libraries, and re-usable design• Techniques for creating robust scripts, including exception handling, testing and debugging• Importing and working with externally sourced data (e.g. text and CSV files)• The use of open-source libraries for automating basic data processing (e.g. calculating point statistics, plotting histograms)Indicative case studies:• How to download, format, and import open source datasets using the scripting language.• Answering basic questions relating to open datasets, such as what the median, mode and mean values, interquartile ranges, and why these values are important.• How to visualise data using basic plotting techniques to understand the distribution of the underlying data, with examples of how point statistics may be misleading.

Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Design, implement and test substantial software scripts which solve problems relating to statistics and data science.

LO2: Employ good practice programming and scripting techniques to develop well-written modular code which is reusable, well documented and uses comprehensive error handling techniques.

LO3: Solve complex, applied problems through abstraction by identifying, utilising and integrating publicly available software libraries as appropriate.

Full Details of Teaching and Assessment
2024/5, Trimester 2, Blended,
VIEW FULL DETAILS
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 activityLearning & Teaching ActivityNESH (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 Hours200
Expected Total Study Hours for Module200


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

Indicative References and Reading List - URL:
Contact your module leader