CSC-10042 - Introduction to Data Science With Python

Coordinator: Allison Ce Gardner Tel: +44 1782 7 33989

Lecture Time: See Timetable...

Level: Level 4

Credits: 30

Study Hours: 300

School Office: 01782 733075

Programme/Approved Electives for 2020/21

None

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2020/21

Introduction to Data Science with Python introduces data science, its relationship with business analytics, statistics, machine learning and artificial intelligence. It outlines the key terms and skills required by a data scientist. It provides a strong practical foundation in Python for data scientists utilising an interactive learning environment set in real world exercises. The module embeds throughout key issues regarding data processing, data privacy, machine bias and AI ethics and regulation. The aim is to emphasise that an ethical approach is a core skill for a data scientist rather than a separate issue or responsibility.

Aims

This module introduces data science, its relationship with business analytics, statistics, machine learning and artificial intelligence. It outlines the key terms and skills required by a data scientist and provides a strong foundation in Python for data scientists.

Intended Learning Outcomes

outline how ethics and compliance affect data science work, and the impact of international regulations (including the General Data Protection Regulation);: 1
describe the life-cycle of a data science project in the context of providing an impartial, scientific, hypothesis-driven approach;: 1,2
apply skills in data analytics and visualisation, using an appropriate range of programming languages, tools and performance metrics, to provide a solution to a data science problem;: 1,2
evaluate a model for bias and prejudice recognising the professional, economic, social, environmental, moral and ethical issues involved;: 1,2

Study hours

56 hours (approx) online practical activities conducted with some sessions available during block release, including assessed online tasks.
4 hours tutorials/discussion conducted during block release
10 hours of online lectures with associated formative quiz activities.
230 hours independent study/reading

School Rules

None

Description of Module Assessment

1: Online Tasks weighted 60%
Portfolio of online tasks
6 online tasks utilising a combination of questions and activities applying concepts taught in lectures and online practical activities.

2: Coursework weighted 40%
Data Science Structured Report
Structured coursework report based on a practical Python data science task applying the theoretical and practical aspects of the module. Students will be asked to describe the project life-cycle approach they have taken; apply data science skills to the task and evaluate the outcome for bias and prejudice. Format and word count will be listed for each section of the report with a total indicative word count of 2000 words, including figures and tables but not including appendices and references.

CSC-10042 - Module Specification School of Computing and Mathematics Faculty of Natural Sciences

CSC-10042 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences