Inference for Data Analysis (online)

Purposes of statistical inference and learning from data. What is learnt, how it is done and why. Theory covering probability, random variables, likelihoods. Algorithms for performing inference, including implementation using software. Comparison of methods and how to critically assess them.

Accordion / FAQ component

Most modules require a 2.2 degree in a related discipline or equivalent professional experience. Should you have any queries regarding your eligibility, please contact us at info@advancecentre.ie.

N.B. Required to have completed mathematics and statistics modules that contain elements of linear algebra (specifically matrix manipulation) and calculus.

By the end of the course, students should be able to:

  • Identify which distribution is appropriate for certain types of data
  • Understand the purposes of inference and how to do it in practice
  • Estimate parameters and their associated uncertainty via likelihood methods, and interpret these values in the context of real-world problems.
  • Incorporate prior information into common statistical problems and obtain posterior probability distributions of parameters of interest.

Indicative Module Content:

  • Background of what is inference, recap on probability theory, random variables, common distributions for data.
  • Random vectors, independence, and conditional distributions.
  • Expectation, covariance, correlation.
  • Properties of random samples and asymptotics.
  • Frequentist statistical inference (method of moments, MLE, confidence intervals).
  • Uncertainty of estimates: parametric and non-parametric.
  • Numerical inferential algorithms.
  • Hypothesis testing.
  • Introduction to Bayesian inference.
  • Decision theory

Learn from world renowned academic staff in Ireland’s leading, future focused and globally recognised colleges.

Gain an accredited NFQ qualification/micro credential that you may count towards a full award if you so wish in the future.

Previous modules may be used as recognition of prior learning towards Advance Centre degree programmes.

Equip yourself with the latest in demand skillset, tools, know-how and knowledge to succeed in your career.

Gain a competitive edge, influence growth and steer strategic goals in your organisation upon completion of your studies with the Advance Centre.

Yes, if you complete this module it can be credited as part of the MSc Data Analytics or Professional Diploma Data Analytics PT or MSc in Digital Agriculture Full Time or MSc in Digital Agriculture Part Time

Detailed Course Information

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