CUrriculum

Monday - Classes from 9:30 to 17:30


 General Introduction

o   Data mining, -omics and machine learning


o   Experimental Design


 Jupyter for Python and R

o    Introduction

 
o    Data structures


o    Reading and Writing data

 

Tuesday - Classes from 9:30 to 17:30


Multivariate data

o    properties


o    data exploration

 
Wednesday - Classes from 9:30 to 17:30

Unsupervised analysis: PCA and beyond

o    Principal Component Analysis

 
o    Clustering

 
o   Other projection methods (TSNE, Self Organising Maps)

 
PCA as a data model, introduction to validation

 

Thursday - Classes from 9:30 to 17:30

 
Supervised analysis: regression and classification

o    model tuning and validation


o    KNN classification

 

Friday - Classes from 9:30 to 17:30

 
Random Forest