Students should learn basic data import, visualization, and analysis for data driven models.

MATLAB allows the students to perform the activities in a friendly and simple manner that takes advantage of existing visualization tools, without requiring a strong programming background.

This activity is focuses on critical thinking, data analysis, synthesis of ideas and it paves the way for data-driven model topics that are covered later in the class.

The activity seek to improve communication and problem formulation skills along with the previous ones.

This activity is intended for engineering students that are not familiarized with tools to import, visualize, and analyze time series data using Matlab.
This is an in-class activity, intended to be completed in 75 minutes.

The students are required to complete MATLAB Onramp training before doing this activity. They are also encourage to review how to formulate SMART (specific, measurable, achievable, relevant, and time-bound) goals.

The activity uses time-series data from mineral processing, a core subject for the intended audience

This activity represents the first in-class graded activity of the Machine Learning for Mining Applications elective. It can be easily adapted to other disciplines that can benefit from using basic data analytics. The latter can be accomplished by changing the time series dataset and doing slight modifications in the instructions.

The required technical skills are those associated to basic MATLAB use, as intended in MATLAB onramp training.

I have developed this materials myself. They can be shared as long as credit is given to my authorship.
Introductory Data Analysis for Mineral Processing handout (Acrobat (PDF) 150kB Oct10 24) 
Dataset for workshop (Excel 2007 (.xlsx) 5.9MB Oct10 24) 

The workshop has assigned points for each activity which are used for assessment and grading.