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Parent Programme
Bachelor in Computing (Level 7 NFQ)
NFQ Level & Reference
Level 7 / Ref: M3.2
12 Weeks X 3 Hours per week
Statistics and Data Science
Module Credit Units

Statistics and Data Science Module

Introduction to Statistics and Data Science

The aim of this module is to equip the learner with the tools and methods to find structure and to give deeper insight into data and to analyse and quantify uncertainty.   Learners will understand how data science can help organisations to reduce costs, make more informed decisions and develop new products and services.

Indicative Syllabus Content

Statistics and Data Science


  • Study types: observational studies and experiments; causation
  • Gathering data: primary & secondary sources; questionnaires; experimental design
  • Population and sample; parameters and statistics; sampling methods
  • Variables and observations
  • Bias
  • Descriptive vs. inferential statistics

Exploratory data analysis (EDA):

  • Types of variables: categorial, numerical
  • Measurement scales: nominal, ordinal, discrete, continuous
  • Types of graphs: bar charts, histograms, boxplots, time series plots, scatterplots, etc.
  • Summarising distributions: measures of centre (mean, median and mode); measures of variance (range, interquartile range, standard deviation); measures of skewness
  • Descriptions of a distribution: shape; modality; outliers
  • Contextualising EDA using industry software: examples using Python


  • Random experiments; outcomes; sample spaces; (simple & compound) events
  • Classical, relative frequency, and subjective probability approaches
  • Calculating simple probabilities; marginal and conditional probabilities
  • Mutually exclusive events; dependent and independent events; multiplication rule; addition rule; Bayes’ theorem
  • Central limit theorem
  • Contextualising probability using industry software: examples using Python

Discrete Distributions

  • Probability mass functions (PMFs)
  • Bernoulli random variables
  • Binomial distribution
  • Poisson distribution
  • Contextualising distributions using industry software: examples using Python

Continuous Distributions

  • Cumulative distribution functions (CDFs) and probability density functions (pdfs)
  • Exponential distribution
  • Normal distribution: properties, z-scores, normal tables
  • Contextualising distributions using industry software: examples using Python

Statistical Inference

  • Point estimates; standard error
  • Confidence intervals
  • Testing theories; Introduction to hypothesis testing
  • Testing a mean or proportion (one-sample tests)
  • Testing the difference between two means or two proportions (two-sample tests)
  • Chi-squared tests
  • Sample size and power
  • Contextualising hypothesis tests using industry software: examples using Python

Making predictions

  • Correlation
  • Regression line; regression analysis; least squares fit; producing predictions
  • Multiple regression and non-linear regression
  • Model validation; outliers; influential observations
  • Contextualising predictions using industry software: examples using Python

Data science in context

  • Report writing
  • Statistics in business: legal issues, ethics, GDPR, privacy
  • Introduction to advanced topics, such as: data warehouses, machine learning, data mining, big data

Minimum Intended Learning Outcomes (MIMLOs)

Upon successful completion of this module, the learner should be able to:
Examine data of various types with consideration of data gathering and transformation.
Analyse data using statistical and probability techniques.
Test hypotheses regarding means, proportions, and differences between two means or proportions using appropriate statistical tests.
Outline data in the form of data visualizations and reports.
Differentiate between ethical considerations, legal requirements and evidence-based reasoning when making decisions.


1, 2, 3, 4, 5
CA1, CA 2, - In Class Written Assessments
Total 100%
CA 3 - Examination
All Assessments

Reassessment Opportunity

Where the combined marks of the assessment and examination do not reach the pass mark the learner will be required to repeat the element of assessment that they failed. Reassessment materials will be published on Moodle after the Examination Board Meeting and will be aligned to the MIMLOs and learners will be capped at 40% unless there are personal mitigating circumstances.

Aims & Objectives

This Statistics and Data Science module will ensure learners meet the following objectives:

  • Develop an understanding of data collection and the types of studies.
  • Assembling data through data cleaning and transformation.
  • Undertake preliminary data analysis using graphs and descriptive and inferential statistics.
  • Identify and develop the model that bests fits the problem requirement.