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# STAT40720

Academic Year 2024/2025

#### Introduction to Data Analytics (Online) (STAT40720)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Leonard Henckel
Trimester:
Autumn
Mode of Delivery:
Online
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module covers introductory probability and statistical inference, focusing on understanding concepts and methodologies. Topics include: descriptive statistics, probability, sampling distributions, confidence interval estimation, hypothesis testing and regression.

## About this Module

###### Learning Outcomes:

Having successfully completed this module, students will be able to:- Summarise a data set using appropriate graphical and numerical methods.- Understand the basic laws of probability and compute probabilities for several different distributions.- Understand the principle of inferential statistics, estimate population characteristics and provide confidence intervals for those estimates.- Understand the principles of hypothesis testing and for a specified investigation, given the background, identify an appropriate test and interpret the results.- Understand and compute the equation of the least squares line, compute confidence intervals and prediction intervals from the least square estimates and perform some basic diagnostic checks on the performance of regression models.

###### Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

24

Autonomous Student Learning

72

Online Learning

24

Total

120

###### Approaches to Teaching and Learning:
Video lectures posted each week that walk though module content, blending theory with example exercises.
Practice problem sheets to enable self-assessment of learning outcomes. Sample solutions for these will be posted after each problem set. Coding based problem sets posted with solutions again following.
All content delivered using the VLE which includes a monitored discussion forum with topics created for each weeks lecture material and each problem set.
Requirements, Exclusions and Recommendations
Learning Requirements:

A basic knowledge of linear algebra and calculus

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
Assignment(Including Essay): Two assignments Week 6, Week 11 Graded No

14

No
Exam (Online): short MCQs, take within a time window Week 3, Week 4, Week 8, Week 9 Graded No

16

No
Exam (Online): Timed online exam, open book End of trimester
Duration:
2 hr(s)
Graded No

70

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

###### How will my Feedback be Delivered?

Not yet recorded.

Name Role
John O'Sullivan Lecturer / Co-Lecturer