Pre-material of the MSc Translational Neuroscience

Welcome to the pre-material of the MSc Translational Neuroscience! The aim of the material is to introduce you to programming in Python and to statistics. If you have already experience with programming and/or statistics, then this will be only an easy review. If not, it will give you the necessary foundation to be able to complete smooothly the modules in the MSc.

Read the instructions carefully before starting the lectures

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

1. Statistical theory: we prepared for you an overview of the main statistical tests and concepts. You should go through the Blackboard stats refresher as first task. In addition to the stats refresher, you will have access to more detailed stats material that you can use for reference if you need to, as well as a decision tree that directs you towards the most appropriate statisticals test to use based on your research question and data types

   You can access the stats refresher at this link - to access you need to use your Imperial Email

   IMPORTANT: to be able to complete the last day of the Python lectures (Lecture10 on statistics), you must have completed the stats refresher. You can work on the the other 9 coding lectures in parallel with the statistical theory, if you prefer.

2. 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, you can join the drop-in session with Dragos and Valentina on Thursday 6th of October from 4.30PM to 5.30PM

3. 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 Common Python Erros Relational Operators Logical Operators Working with Files Introduction to python scripts Debugging
   Lecture2	Introduction to Functions	Structure and application of functions
   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

4. Exercises: when you completed both the statistical theory and the programming lectures, you can continue to complete the 5 exercises that we have prepared for you. The exercises require you to know everything we have mentioned in the lectures (both programming and statistics) and consist in analysing and preparing some real-data on cognitive behaviour, as well as solving some coding exercises. We suggest you to solve each exercise in a separate Jupyter Notebook.

   IMPORTANT: In the week between the 10th and 16th of October, we will open a Q&A Channel on teams, where you will be able to post questions about the lectures and the exercises. The Q&A will remain open for ONE WEEK. During this week, you have the opportunity to ask all the questions you might have. At the end of the week, we will reply to all the questions in the cannel. If we believe that one question might require longer explanations, we will schedule calls with all the students that are facing the same issues.