Python, more python, pandas and sklearn

Date:

June 27 to July 1. AFTERNOON: 15 to 18h

Classroom:

Not defined yet

Instructor

Alexandre Perera Lluna (1973) holds a degree in Physics (1996, UB), Electronic Engineer (2001, UB) and a PhD in Physics (2003 UB), postdoctoral fellow at Texas A&M University (Tx, USA, 2003-2004) and EADS European Aeronautic Defence and Space Company (CRC Forschung,München, DE, 2005), Ramon y Cajal Fellow (2007) is currently tenured at the Polytechnic University of Catalonia (2013). He is double affiliated as researcher of the Institut de Recerca de Sant Joan de Déu.

Author of more than 75 papers in peer-review journals, five patents and more than 60 contributions to national and international conferences. He is currently the coordinator of the research group B2Slab (http://b2slab.upc.edu) Bioinformatics and Biomedical Signals Laboratory and head of the Biomedical Research Center at UPC.

His research covers artificial intelligence algorithms, multivariate statistics and machine learning applied to bioinformatics and bioengineering, essentially suffering from the disease of curiosity and having fun in whatever we do.

Language

English

Description

This course will cover a crash course for scientific Python for data analysis. This crash course will include three main stages:

Introduction to Python language as a tool. Workflow, ipython, ipython notebook (jupyter), basic types, mutability and inmutability and object oriented programming.
Short introduction to numerical Python and some libraries for graphical visualization such as seaborn or plotnine, including providing interactivity with Jupyter notebooks.
Introduction to scientific kits for data analysis with machine learning. Pandas. Principal components analysis, clustering and supervised analysis with multivariate data.

Course goals

To gain proficiency in coding python, understand basic types
To learn how to build generators and cogenerators
To learn how to build and manage data-frame-based representations of data
To learn how to show interactive visualizations in notebooks
To learn how to use machine learning scientific kit (sklearn)

Course contents

1. Introduction

a. Why Python?
b. Python History
c. Installing Python
d. Python resources

2. Working with Python

a. Workflow
b. ipython vs. CLI
c. Text Editors
d. IDEs
e. Notebook

3. Getting started with Python

a. Introduction
b. Getting Help
c. Basic types
d. Mutable and in-mutable
e. Assignment operator
f. Controlling execution flow
g. Exception handling

4. Functions and Object Oriented Programming

a. Defining Functions
b. Input and Output
c. Standard Library
d. Object-oriented programming

5. Introduction to NumPy

a. Overview
b. Arrays
c. Operations on arrays
d. Advanced arrays (ndarrays)
e. Notes on Performance (\%timeit in ipython)

6. Matplotlib

a. Introduction
b. Figures and Subplots
c. Axes and Further Control of Figures
d. Other Plot Types
e. Animations

7. Python scikits

a. Introduction
b. Pandas

8. scikit-learn

a. Datasets
b. Sample generators
c. Unsupervised Learning
d. Supervised Learning

i. Linear and Quadratic Discriminant Analysis
ii. Nearest Neighbors
iii. Support Vector Machines

e. Feature Selection

9. Practical Introduction to Scikit-learn

a. Solving an eigenfaces problem

i. Goals
ii. Data description
iii. Initial Classes
iv. Importing data

b. Unsupervised analysis

i. Descriptive Statistics
ii. Principal Component Analysis
iii. Clustering

c. Supervised Analysis

i. k-Nearest Neighbors
ii. Support Vector Classification
iii. Cross validation

d. Practical Challenge

Prerequisites

Basic coding skills and will to have fun with coding

Targeted at

People aiming to learn python from scratch up to data management in python.

Evaluation

Final short questionnaire

Software requirements

Python3, jupyter notebook, pandas, and sklearn.