People in business or industry who wish to develop technical skills in data analytics.
Data Science in Python
Overview
| Module Code | COMP47670 |
| Module Title | Data Science in Python |
| Subject Area | Computer Science |
| Credits | 5 |
| NFQ | 9 |
| EFQ | 7 |
| Start Date | Autumn and Spring Next Intake: September 8th |
| Duration | 12 Weeks |
| Time | Mondays 5-6pm for 1st 4 weeks of term. (Subject to change.) |
| Mode of Delivery | Hybrid |
| Course Leader | Not assigned |
| Fee | €900 |
| Application Deadline | September 2025 for Autumn Intake December 2025 for Spring Intake |
The key objectives of this module are
1) to provide students with an initial crash course in Python programming;
2) to familiarise students with a range of key topics in the emerging field of Data Science through the medium of Python.
Students will start by exploring methods for collecting, storing, filtering, and analysing datasets. From there, the module will introduce core concepts from numerical computing, statistics, and machine learning, and demonstrate how these can be applied in practice using popular open source packages and tools. Additional topics that will be covered include data visualisation and working with textual data. This module has a strong practical programming focus and students will be expected to complete two detailed coursework assignments, each involving implementing a Python solution to a data analytics task. COMP47670 requires a reasonable level of mathematical ability, and students should have prior programming experience (but not necessarily in Python). This is a Mixed Delivery module with online lectures and face to face practicals/tutorials.
On completion of this module, students will be able to:
1) Program competently using Python and be familiar with a range of Python packages for data science;
2) Collect, pre-process and filter datasets;
3) Apply and evaluate machine learning algorithms in Python;
4) Visualise and interpret the results of data analysis procedures.
- Introduction to Python
- Introduction to Data Science
- Data Formats and Collection
- Pandas
- Data Visualisation
- Modelling and Prediction
- Classification and Evaluation
- Time-Series Data
- Text Analytics
With the emergence of Generative AI tools, it is possible for practitioners with a moderate level of technical skill to complete data analytics tasks. This module will provide that level of technical skill.
Lecture materials are available online as pre-recorded lectures. There will be four one-hour on-campus labs/tutorials over the first four weeks of the term.
Prior programming experience in a high-level language (but not necessarily in Python)
Practical Assignment (20% of the grade)
Practical Assignment (20% of the grade)
2 hour open-book practical exam (60% of grade)
- Feedback individually to students, post-assessment
- Group/class feedback, post-assessment
Machine Learning with Python
Please note: Learners can avail of only one form of funding per application.
Micro-Credentials Learner Fee Subsidy-Human Capital Initiative Pillar 3
The HCI Pillar 3 Micro-credential Learner Fee Subsidy has been introduced to enable more learners to address critical skills gaps and engage with lifelong learning through micro-credentials. The HCI Pillar 3 Micro-credential Learner Fee Subsidy is funded by Higher Education Authority (HEA) and the Department of Further and Higher Education, Research, Innovation and Science.
HCI Micro-credential Learner Fee Subsidies are available on identified micro-credentials only and in fixed numbers from March 2024 until October 2025.
Please see Eligibility Criteria for further information.
On successful completion of this micro-credential, you will receive credits as per the European Credit and Transfer System. These credits are recognised by the awarding institution as credits aligned to learning completed at postgraduate level.
If you have any questions about this micro-credential, or would like to speak to a UCD staff member, please contact (opens in a new window)microcredentials@ucd.ie.