# STAT10430 Statistics with Python

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.

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Curricular information is subject to change

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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: Final exam 2 hour End of Trimester Exam No Standard conversion grade scale 40% No

30

Continuous Assessment: Weekly quizzes Throughout the Trimester n/a Standard conversion grade scale 40% No

10

Assignment: Midterm assignment Week 7 n/a Standard conversion grade scale 40% No

15

Class Test: 2 in-class test Throughout the Trimester n/a Standard conversion grade scale 40% No

20

Continuous Assessment: Lab classes Throughout the Trimester n/a Standard conversion grade scale 40% No

25

Carry forward of passed components
No

Resit In Terminal Exam
Autumn Yes - 2 Hour
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
Ms Courtney Clarke Tutor
Thiago Da Silva Cardoso Tutor
Kate Finucane Tutor
Mr Shubbham Gupta Tutor
Mr Xian Yao Gwee Tutor
Mrs Hardeep Kaur Tutor
Mr Wenxuan Liu Tutor
Constantinos Menelaou 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, 30, 31, 32, 33 Fri 10:00 - 10:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 12:00 - 12:50
Tutorial Offering 1 Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 11:00 - 11:50
Computer Aided Lab Offering 1 Week(s) - 21, 23, 25, 29, 31, 33 Thurs 12:00 - 12:50
Computer Aided Lab Offering 2 Week(s) - 21, 23, 25, 29, 31, 33 Thurs 12:00 - 12:50
Spring