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CSC-10052 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

For academic year: 2022/23 Last Updated: 19 January 2023

CSC-10052 - Making Sense of Statistics for Data Scientists
Coordinator: Sergei Annenkov Tel: +44 1782 7 33078
Lecture Time: See Timetable...
Level: Level 4
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2022/23

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2022/23

This module provides an introduction to common techniques for exploring, summarising and modelling data. The module develops transferable skills through solving problems, modelling and using spreadsheets to handle quantitative information. Emphasis is placed on understanding the meaning behind the data and on the importance of the correct presentation of findings.

Aims
The aim of this module is to prepare learners to understand of some of the more common statistical techniques, to encourage good practice and highlight common errors and misconceptions. Key to this module is to provide a differentiated learning framework for apprentices, some of whom may not have had significant mathematical and statistical education beyond Level 2 whilst others may have level 3 or higher mathematics background.
Specifically, the module aims to develop:
1) a sound knowledge of mathematical concepts, skills and techniques important in the use of data science.
2) confidence in applying mathematical and statistical thinking and reasoning in a range of new and unfamiliar contexts to solve real-life problems;
3) competency in interpreting and explaining solutions of problems in context;
4) fluency in procedural skills, common problem-solving skills and strategies.

Intended Learning Outcomes

apply appropriate graphical techniques to summarise data;: 1,2
apply mathematical and statistical thinking and reasoning in a range of new and unfamiliar contexts to solve real-life problems;: 2
interpret and explain solutions of a problem in a given context;: 2
identify the correct, and incorrect, ways of presenting data;: 1,2
interpret, in time-constrained conditions, data and draw suitable conclusions: 1

Study hours

22 hours lectures (delivered online)
11 hours examples activities delivered either online or during block release tutorial sessions
24 hours course work preparation
93 hours private study


School Rules

None

Description of Module Assessment

1: Coursework weighted 40%
Three short timed online tasks
Three online tasks (weighted 10%, 15% and 15%) set at approximately three weekly intervals. Each task should take approximately 30 minutes to complete. Each assessment covers approximately three weeks of material. The tasks are completed in students' own time.

2: Assignment weighted 60%
Assignment
Investigative coursework covering the theoretical and practical aspects of the module. The assessment provides a data science case study situation that requires modelling. Students evaluate the relative merits of different approaches, then formulate an appropriate strategy to solve the problem and then apply this approach. The output of the assessment will be a written mathematical report. The length of the report will not exceed four pages, including figures and tables, but not including appendices. Formatting guidelines will be provided.

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/student-terms-and-conditions/

Last Updated: 19 January 2023