This module will introduce a range of fundamental statistical ideas. The broad aim of the module is to provide students with

1. an understanding of the mathematical ideas that underpin some fundamental statistical methods;

2. proficiency in performing practical data analysis using statistical software;

3. the ability to communicate the results of data analysis by clearly explaining the steps they follow in their analysis.

• Formulate problems involving uncertainty within the framework of probability theory.
• Understand the conditions under which various statistical methods can be applied.
• Summarise a data set using descriptive statistics.
• Calculate confidence intervals and perform hypothesis tests.
• Identify the sources of variation in data.
• Fit linear models to data and evaluate the accuracy of these models.
• Perform variable selection and dimension reduction.
• Write technical reports to communicate the results of data analysis procedures.

You will be guided through learning activities appropriate to your module, which may include:

• Weekly face to face classes (e.g. labs, lectures, exercise classes)
• Electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes)

Students are also expected to undertake at least 100 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

Mathematical reasoning

Practical data analysis

The use of statistical computer packages

Written communication skills.

THE OPPORTUNITY FOR REASSESSMENT IN THIS MODULE:

Opportunities for re-assessment is only permitted provided you have not failed more credit than in the resit rule

adopted by your programme. If the amount of credit you have failed is more than permitted by the relevant resit

rule, you may be permitted to repeat study if you are within the threshold set for the Repeat rule adopted by your programme. You will be notified of your eligibility to resit/repeat any modules after the Examining Board in the Summer period.

All resit assessments will be held in the Resit Examination period, prior to the start of the following academic session.

• Summarizing and Visualizing Data
• Probability Basics 
• Hypothesis Testing and Confidence Intervals 
• Reconstructing Probability Distributions from Data 
• Regression 
• Classification and Unsupervised Learning