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STAT40800

Academic Year 2024/2025

Data Prog with Python (online) (STAT40800)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Adrian O'Hagan
Trimester:
Autumn
Mode of Delivery:
Online
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

In this module students will learn how to manipulate data and perform statistical analysis using Python.

This module covers a range of topics, including (but not limited to):
- structure of the Python language
- data manipulation
- data visualisation
- statistical analysis
- regression and classification
- machine learning and clustering algorithms
- APIs and webscraping
- string manipulation and regular expressions

NOTE: This is a purely online module. All content is delivered asynchronously. There are no face-to-face lectures or tutorials.

About this Module

Learning Outcomes:

By the end of the module, students should be:
- Competent Python programmers
- Familiar with a range of Python packages and functions for data analysis and visualisation
- Able to obtain, manipulate and analyse large data sets using Python
- Proficient in a range of different data analysis techniques, such as regression, classification and machine learning
- Capable of visualising and interpreting the results of a statistical analysis

Indicative Module Content:

- structure of the Python language
- data manipulation
- data visualisation
- statistical analysis
- regression and classification
- machine learning and clustering algorithms
- APIs and webscraping
- string manipulation and regular expressions

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

36

Autonomous Student Learning

52

Online Learning

12

Total

100


Approaches to Teaching and Learning:
This is a purely online module. All content is delivered asynchronously. There are no face-to-face lectures or tutorials.

A series of video lectures are posted to the VLE every week. Each set of videos is accompanied by a set of non-assessed (practice) and assessed (for credit) coding exercises. Discussion boards enable communication between students, as well as with the teaching staff.

Requirements, Exclusions and Recommendations
Learning Requirements:

Students should have completed an introductory level statistics course and have a general understanding of calculus.


Module Requisites and Incompatibles
Incompatibles:
COMP30760 - Data Science in Python - DS, COMP41680 - Data Science in Python, COMP47670 - Data Science in Python (MD)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): 2 assignments 30% and 40% Week 6, Week 10 Other No
70
No
Assignment(Including Essay): Weekly coding Week 1, Week 2, Week 3, Week 4, Week 5, Week 7, Week 8, Week 9, Week 10, Week 11 Other No
30
No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
• Self-assessment activities

How will my Feedback be Delivered?

There are non-assessed (practice) and assessed (for credit) coding exercises every week. The exercises are setup on CodeRunner, which allows students correct incorrect attempts (small penalty for assessed exercises). Solutions are released automatically after the deadline passes. Unlike the weekly coding exercises, the midterm assignment and project test the students' ability to interpret their results as well as code proficiently. Students will receive individual feedback on their midterm assignment prior to submitting the final project.

Name Role
Mr Jake Williams Lecturer / Co-Lecturer
Mr Brian Buckley Tutor