CSC-10052 - Making Sense of Statistics for Data Scientists

Coordinator: Paul D Ledger Tel: +44 1782 7 33251

Lecture Time: See Timetable...

Level: Level 4

Credits: 15

Study Hours: 150

School Office: 01782 733075

Programme/Approved Electives for 2020/21

None

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2020/21

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: Online Tasks 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.

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

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