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Curricular information is subject to change
At the end of this module you will have a good understanding of how to present and organise data through numerical summaries and graphical displays. You will understand basic concepts in probability theory and statistical inference. Finally, you will have a good working knowledge of the python language especially as it relates to statistics and data science.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Tutorial | 12 |
Practical | 6 |
Specified Learning Activities | 24 |
Autonomous Student Learning | 34 |
Total | 100 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Not yet recorded. |
Resit In | Terminal Exam |
---|---|
Autumn | Yes - 2 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.
Name | Role |
---|---|
Dr Áine Byrne | Lecturer / Co-Lecturer |
Beatriz Barbero Lucas | Tutor |
Kate Finucane | Tutor |
Mr Shubbham Gupta | Tutor |