Core Module Information
Module title: Scripting for Data Science

SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET11123
Module leader: Md Zia Ullah
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 the module is to deepen the students' understanding of fundamental programming concepts, introduce more advanced concepts pertaining to script development, and develop an ability to utilise publicly available software libraries to solve data science-related problems. The module provides a fundamental introduction to the chosen scripting language and makes no assumptions about student?s prior exposure to it. The latter parts of the module will focus on applying these concepts to data processing, such that students will develop insight into automating common statistical analyses on imported datasets.The syllabus includes topics such as:• An introduction to building scripts using a popular scripting language widely used in Data Science• 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.• Basic plotting 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: LO1: Demonstrate expertise in the use of a modern interactive development environment using Python

LO2: LO2: Make informed decisions to employ suitable programming construct in an appropriate language for data science-related problems

LO3: LO3: Design and implement a significant piece of code to develop well-written reusable modular code, well documented and uses comprehensive error handling techniques.

LO4: LO4: Provide a critical analysis of developed code

Full Details of Teaching and Assessment
2024/5, Trimester 2, In Person,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: In Person
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Md Zia Ullah
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 20 Ten lecture sessions will cover the following topics: - Introduction to python - Conditions and Loops - Functions - Classes, Objects, and Inheritance - Regular expressions - Data I/0 - Data preprocessing - Basic statistics and - Error handling
Independent Learning Practical classes and workshops 20 Ten practical sessions will facilitate the students to solve critical problems aligned with the corresponding lectures, having one-to-one support from the lecturer and demonstrator.
Online GROUPIND_STUDY 160 INDEPENDENT STUDY
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 50 1~3 Week 6 HOURS= 500 Practical Skills Assessment- Writing efficient codes to solve some programming problems- Writing comments in the code by documenting the underlying thought process
Practical Skills Assessment 50 2~3~4 Week 13 HOURS= 1000 Practical Skills Assessment- Writing a modular, well documented code for solving a specific data wrangling problem- Providing a critical analysis of the code
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

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