Module 5
Digital Skills Module 5 is the module where data analysis and data science is introduced.
It aims to develop computational skills for students in engineering, but it can also be used by students in other science majors. The course uses the Python programming language and the Jupyter open-source tools for interactive computing.
This first module assumes no coding experience, so the first three lessons are focused on creating a foundation with Python programming constructs using essentially no mathematics. The fourth lesson introduces the basic data structure in scientific computing: arrays. The final lesson is a worked example of linear regression with real data.
Learning Goals
Students will be able to:
Realise that data science has 5 typical application domains, also called 'goals'. Build a pipeline (workflow) to solve data science projects and tasks, always starting with a clear objective(s). Carry out a data science project by breaking down the data into information (knowledge) and error (unknown structure, noise, randomness). Interpreting our data science code, models and outputs so we can take actions that are aligned with the project's objective(s).
How to use
Notes about Notebook, how to install and use these.
Sources
- https://mybinder.org/v2/gh/engineersCode/EngComp1_offtheground/master
- https://github.com/ipython/ipython-in-depth
Both the above sources are licenses CC-BY, and 3-clause BSD license. Same as our remixes, modification of these materials.
Copyright and License
(c) 2018 Kevin G. Dunn. All content is under Creative Commons Attribution CC-BY 4.0, and all code is under BSD-3 clause. We are happy if you re-use the content in any way!