STAT40800 Data Prog with Python (online)

Academic Year 2021/2022

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. There are no face-to-face lectures or tutorials.

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Curricular information is subject to change

Learning Outcomes:

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

Indicative Module Content:

- 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

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

36

Autonomous Student Learning

48

Online Learning

12

Total

96

Approaches to Teaching and Learning:
This is a purely online module. All content is delivered asynchronously. There are no face-to-face lectures or tutorials.

A series of short video lectures are posted to the VLE every week. Each set of videos is accompanied by a set of non-assessed (practice) and assessed (for credit) coding exercises. Discussion boards enable communication between students, as well as with the teaching staff. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Students should have completed an introductory level statistics course and have a general understanding of calculus.


Module Requisites and Incompatibles
Incompatibles:
COMP30760 - Data Science in Python - DS, COMP41680 - Data Science in Python, COMP47670 - Data Science in Python (MD)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Midterm assignment Week 6 n/a Standard conversion grade scale 40% No

25

Continuous Assessment: Weekly coding exercises Throughout the Trimester n/a Standard conversion grade scale 40% No

30

Project: Data analysis project Coursework (End of Trimester) n/a Standard conversion grade scale 40% No

45


Carry forward of passed components
No
 
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
• Self-assessment activities

How will my Feedback be Delivered?

There are non-assessed (practice) and assessed (for credit) coding exercises every week. The exercises are setup on CodeRunner, which allows students correct incorrect attempts (small penalty for assessed exercises). Solutions are released automatically after the deadline passes. Unlike the weekly coding exercises, the midterm assignment and project test the students' ability to interpret their results as well as code proficiently. Students will receive individual feedback on their midterm assignment prior to submitting the final project.

Name Role
Dr Áine Byrne Lecturer / Co-Lecturer
Professor Nial Friel Lecturer / Co-Lecturer
Professor Brendan Murphy Lecturer / Co-Lecturer
Mr John O'Sullivan Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

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