petehellyer / Neuroscience-Python-Course-Development

Pre-material for fMRI Skill

Introduction to Python programming and statistics

Welcome to the pre-material for the fMRI Skill! The aim of the material is to introduce you to programming in Python and to statistics. If you already have experience with programming and/or statistics, then this will be an easy review. If not, it will give you the necessary foundation to be able to explore programming in Python, as well as provide you with the neccisary skills to complete the 4 workshop activities in the fMRI Skill.

Read the instructions carefully before starting the lectures

You are expected to go trough the material in the following order:

1.. Device set-up: there are two documents called Setup-Windows and Setup-Mac. Follow the instructions in the document that correspond to your operative system. If you have any questions about the set-up specifically, please email [e-mail] with the subject line PRE_MATERIALS

2 Introduction to Python: we prepared 10 separate lectures on the most important concepts of programming in Python. The lecture are called LectureN (where N correspond to the number of the lecture) and are already in the order in which you should complete them.

IMPORTANT: don't skip lectures, but do follow the order we provided, because following lectures are based on previously taught material.

IMPORTANT 2: The lecture are characterised by a combination of pratical theory and code here sections. The code here sections require you to apply what you have learnt in the lectures through small exercises.

A summary of the lectures' topics is provided in the following Table.

Lecture	Title	Topic
Lecture1	Introduction to Python syntax and variables	Jupyter Notebook Introduction to Bash Variables Relational Operators Logical Operators Working with Files
Lecture2	Introduction to Functions	Structure and application of functions Introduction to python scripts Debugging Common Python Erros
Lecture3	Introduction to Lists and Arrays	Importing Modules Extract elements from lists Lists update Basic operations with lists Extract elements from arrays Arrays dimentions and shapes Generation of arrays Mathematical operations with arrays
Lecture4	Introduction to Conditional Statements	Conditional statements structure and application Truthiness and conditional statement
Lecture5	introduction to Loops	Introduction to for loops Looping over lists Looping over strings Range function Enumerate function Continue and break Nested loops Introduction to While
Lecture6	Introduction to Strings manipulation	Indexing and iterating through strings String methods Cleaning messy strings
Lecture7	Introduction to Dictionaries	Dictionary methods Iterating through dictionaries
Lecture8	Introduction to Dataframes	Create dataframes Visualize dataframes Get information about dataframes Access and update data Detection and removal of missing values Replacement of wrong data Detection and removal of duplicates Merging of dataframes
Lecture9	Introduction to plotting	Scatterplots Histograms Barplots Boxplots Pie charts Subplots
Lecture10	Introduction to Summary Statistics and Hypothesis Testing	Summarising numerical data Summarising Categorical Data Hypothesis testing and statistical tests Correlation analysis Linear Regression One-way Anova

Thanks to Valentina Giunchiglia [e-mail] and Dragos Gruia [e-mail] for putting together these materials.