Data Programming with Python

In this module students will learn how to manipulate data and perform statistical analysis using Python. This module covers a range of topics, including (but not limited to): - Structure of the Python language - Data manipulation - Data visualisation - Statistical analysis - Regression and classification - Machine learning and clustering algorithms - APIs and webscraping - String manipulation and regular expressions NOTE: This is a purely online module. All content is delivered asynchronously

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

N.B. Learners should have completed an introductory level statistics course and have a general understanding of calculus.

By the end of the module, students should be:

  • Competent Python programmers
  • Familiar with a range of Python packages and functions for data analysis and visualisation
  • Able to obtain, manipulate and analyse large data sets using Python
  • Proficient in a range of different data analysis techniques, such as regression, classification and machine learning
  • Capable of visualising and interpreting the results of a statistical analysis

Yes, if you complete this module it can be credited as part of the MSc Data Analytics.

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.

The learner subsidy is provided for this micro-credential to reduce the learner fee by 80%.

 For eligibility criteria please visit the link 

Detailed Course Information

Contact us
  • Twitter
  • LinkedIn
  • Facebook
  • Instagram
  • YouTube