Skip to main content

Data Skills

Overview

  • Credit value: 15 credits at Level 4
  • Convenor and tutor: Dr Richard Pymar
  • Assessment: assignments (40%) and statistical reports (60%)

Module description

In this practical module we provide an overview of the fundamental concepts of statistics and data analysis, with topics on the design of experiments, sampling and collecting data, and calculating summary statistics. You will learn how to enter data into a powerful and easy-to-use software package, and use it to summarise data, represent data graphically and report conclusions. The examples throughout use real datasets and are drawn from a wide range of areas.

Indicative syllabus

  • Introduction to a spreadsheet software (creating new worksheets, writing formulae, using functions, sorting rows)
  • Visualising data (discrete vs continuous, displaying data using software)
  • Summary measures of location (mean, median, mode, weighted averages)
  • Summary measures of spread (range, deviation, quantiles, variance)
  • Sampling (random sampling, random numbers, with vs without replacement)
  • Collecting data and experimental design (surveys, experiments, ethics)
  • Working with data (quality of data, comparing multiple groups, z scores)
  • Bivariate data (scatterplots, graphical representation, correlation)

Learning objectives

By the end of this module, you will be able to:

  • enter data into a spreadsheet software and use it to graphically represent the distribution of a dataset
  • summarise data and produce a report that may be understood by non-specialists
  • produce and analyse a scatterplot to determine whether a linear correlation exists between two variables
  • measure various forms of the location/center of a dataset
  • determine whether outliers have an effect of mean/median
  • measure the spread of variation in a dataset
  • determine whether a data point is significant using z scores
  • understand the importance of appropriate design of experiments
  • understand the importance of ethics in data collection and analysis.