Programming in Python
This module introduces students to Python programming, covering fundamental data structures, control structures, functions and modular programming, and an introduction to data visualisation and machine learning. Students build proficiency in Python for data science, gaining competence in key libraries and applying Python to real-world data science tasks.
Delivery
Course delivery comes in the form of lectures with 15 hours delivered over the first 3 weeks of the term (10 hours of lectures and 5 hours of tutorials). There will be optional clinics on the last day of the course.
Study Hours
Approximately 100 hours of study, of which 15 are contact hours and 85 are private study.
Software Used
Python
Credits
10 CATS credits
Programme Faculty
Programme Faculty
Key Outcomes
- —Python Proficiency: Mastery of Python programming for data science.
- —Data Science Basics: Fundamental concepts and skills for data science in Python.
- —Library Competence: Efficient use of key Python libraries for data science.
- —Practical Application: Applying Python to real-world data science tasks.
- —Versatile Python Use: Adapting Python for diverse data science applications.
- —Effective Coding Practices: Writing efficient and effective Python code.
Desired Skills
- —Present, interpret and analyse information in numerical form and use econometric and other packages effectively
- —Master the programming tools and important libraries for data science
Lecture Programme
- WK 01
Lecture 1: Introduction to Python
- ·Introduction to Python
- ·Python Installation
- ·Introduction to Shell / Google Colab
- ·Instructions and indenting
- WK 02
Lecture 2: Data Structures
- ·Data types in Python: numbers, strings, lists, sets, tuples and dictionaries
- ·Defining and using variables in Python
- ·Basic operations and manipulations on data types
- ·Introduction to DataFrames
- WK 03
Lecture 3: Control Structures and Loop
- ·Conditional statements
- ·Control structures
- ·For and while loops
- ·Imbricated loops
- WK 04
Lecture 4: Functions and modular programming
- ·Defining and using functions and classes in Python
- ·The importance of modularity and code organisation
- ·Importing and using modules and libraries
- WK 05
Lecture 5: Introduction to Data Visualization and Machine Learning
- ·Introduction to the scikit-learn library for machine learning in Python
- ·Pandas and Matplotlib
- ·Simple examples of data science with Python
Assessment
Online coursework submission with the support of your module lecturer and tutor. All sessions are conducted live online at a scheduled time and recorded for review.