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STAT10430

Academic Year 2024/2025

Statistics with Python (STAT10430)

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

Curricular information is subject to change.

This module introduces basic concepts of probability and statistics. Strong emphasis will be placed on exploratory data analysis using numerical and graphical techniques. This module will also expose students to introductory aspects of probability theory and statistical inference, including random variables, sampling distributions, confidence intervals and hypothesis testing. Throughout this module all concepts and techniques will be developed and implemented using python.

About this Module

Learning Outcomes:

At the end of this module you will have a good understanding of how to present and organise data through numerical summaries and graphical displays. You will understand basic concepts in probability theory and statistical inference. Finally, you will have a good working knowledge of the python language especially as it relates to statistics and data science.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

24
Autonomous Student Learning

34
Lectures

24
Tutorial

12
Practical

6
Total

100

Approaches to Teaching and Learning:
Lectures, tutorials, enquiry and problem-based learning.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Exam end of term End of trimester
Duration:
2 hr(s)
 Other No
80
 No
Assignment(Including Essay): in term HW Week 8 Other No
20
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn 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

How will my Feedback be Delivered?

Not yet recorded.

Probability and Statistics for Computer Science, by David Forsyth
Statistics, by McClave and Sincich
An Introduction to Statistics with Python: With Applications in the Life Sciences, by Thomas Haslwanter

Name Role
Dr Áine Byrne Lecturer / Co-Lecturer
Beatriz Barbero Lucas Tutor
Thiago Americo Da Silva Cardoso Tutor
Kate Finucane Tutor
Kseniia Maksimova Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 31, 32, 33 Fri 10:00 - 10:50
Spring Lecture Offering 1 Week(s) - 30 Fri 10:00 - 10:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 12:00 - 12:50
Spring Tutorial Offering 1 Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 11:00 - 11:50
Spring Computer Aided Lab Offering 1 Week(s) - 21, 23, 25, 29, 31, 33 Thurs 12:00 - 12:50
Spring Computer Aided Lab Offering 2 Week(s) - 21, 23, 25, 29, 31, 33 Thurs 12:00 - 12:50
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