| layout | title |
|---|---|
page |
About |
We are building peer groups of technical project roles in resources companies, to act as a community – together we are building a talent pool with greater data science literacy. If participants think they grasped the opportunities of applying the tools the first time within the program, it is going to be an order of magnitude more powerful applying the learning in follow on projects back in your organisation.
Upon completing the course, learners will be able to:
- Transform data into actionable outcomes
- Evaluate which tool to use, why and when
- Appreciate good practice in data science
- How to work with data scientists
Immerse yourself in this breakthrough learning experience and develop a sharper perspective on the challenges facing your organization and how to leverage your technical and company background into the digital age.
All the course content is delivered via seperate repositories for each week (see below for instructions on getting set up each week). Here's the links to each week:
| Date | Code! | Topic (links to Github) |
|---|---|---|
 |
Prerequisite - data carpentry | |
| 18 September | |
Zero to data science |
| 25 September | |
Getting to know the tools |
| 2 October | |
Simple predictions |
| 9 October | |
Multivariate analysis |
| 16 October | |
Effective data storytelling |
| 23 October | |
Machine learning |
| 30 October | |
Deep learning |
| 6 November | |
Time and network data |
| 13 November | |
Data fusion sandbox |
| 27 November | |
Natural language processing |
| 4 December | |
Spatial data |
| 11 December | Capstone projects and pitches (no code!) |