Welcome to the M550 Python Module#

The course#

This course is an introduction to python programming for statistics and data science. Since it is an introductory course it does not cover every aspect of the python programming system.

Initially, the course focuses on the python language. The later sessions then introduce python libraries that are particularly useful for statistics and data science.

The material is presented using jupyter notebooks and jupyter book. You can learn more about jupyter here

The approach to teaching is hands on. You are expected to try as many exercises as you can. The jupyter notebooks for the course can be downloaded and extended with your own notes, examples, and ideas.

Course contacts#

Daniel Grose - Lecturer

Sessions#

  • Weeks 2, 3, and 4

  • Room A008

  • 16:00 - 18:00

Assessment#

  • One piece of assessed work

  • Assessment undertaken in small groups (2-4 per group)

  • Marks for both individual and group contributions

  • Issued in session 3 (01/11/2023)

  • Due two weeks later (15/11/2021) by 09:00

  • Submitted as jupyter notebooks (one per individual and one for the group)

Approach to learning#

  • Single jupyter book for course notes

  • Course notes designed to accompany the sessions

  • Each chapter is a jupyter notebook

  • Each notebook can be downloaded and annotated by you

  • Course notes have lots of examples and exercises

Why Python ?#

  • Easy to learn

  • Versatile

  • Syntax is easy to read

  • Lots of open source libraries

  • Used for more than statistics and data science

  • Easy to integrate into larger systems

Outcomes#

Session 1 - The basics#

  • Install jupyter

  • Using jupyter

  • Importing libraries

  • Installing libraries

  • Python libraries for data science

  • Class based methods

Session 2#

  • Variables and expressions

  • Functions

  • Lists

  • if, elif, and else

  • for, while

  • Tuples

  • Dictionaries

Session 3#

  • numpy

  • random numbers

  • matplotlib

  • ggplot