First steps with Python in Life Sciences

Related tags

Data Analysisfirst-steps-with-python-training
Overview

First steps with Python in Life Sciences

This course material is part of the "First Steps with Python in Life Science" three-day course of SIB-training and is addressed to beginners wanting to become familiar with the Python syntax, environment, and the most common commands.

This course material provides an introduction to python and jupyter notebooks (a web based notebook system for creating and sharing computational documents) in an interactive manner.

prerequisite installation

You can find tips and instructions to ensure you have installed all the required software before starting the course.

course material organization

The course revolves around a sery of jupyter notebooks which take you on your first steps in you python journey.

Each jupyter notebook interleaves theory and examples of codes. We heartily recommend you execute and play around with these bits of code as you follow along : in programming, perhaps even more than anywhere else, practice makes perfect.

Additionally, each notebook is associated with a number of exercises (often in a separate notebook) of varying difficulty, with associated corrections.

If you are attending this course with a teacher (or if you are just curious), you can take a look at our schedule. In short, lessons 00 to 04 deals with generalistic aspect of the python language, while notebooks 05 or 08 present some of the most common modules used in data analysis and/or life sciences.

The notebooks/ folder contains each lesson:

Exercise notebooks:

The data used in the practicals can be found in the data notebooks/data folder, and solutions codes can be found in the notebooks/solutions/ folder (NB: micro-exercises do not have a correction).

You might also like...
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 9, 2023
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

PyMC 7.2k Dec 30, 2022
Statsmodels: statistical modeling and econometrics in Python

About statsmodels statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics an

statsmodels 8k Dec 29, 2022
A computer algebra system written in pure Python

SymPy See the AUTHORS file for the list of authors. And many more people helped on the SymPy mailing list, reported bugs, helped organize SymPy's part

SymPy 9.9k Dec 31, 2022
ForecastGA is a Python tool to forecast Google Analytics data using several popular time series models.
ForecastGA is a Python tool to forecast Google Analytics data using several popular time series models.

ForecastGA is a tool that combines a couple of popular libraries, Atspy and googleanalytics, with a few enhancements.

JR Oakes 36 Jan 3, 2023
Multiple Pairwise Comparisons (Post Hoc) Tests in Python
Multiple Pairwise Comparisons (Post Hoc) Tests in Python

scikit-posthocs is a Python package that provides post hoc tests for pairwise multiple comparisons that are usually performed in statistical data anal

Maksim Terpilowski 264 Dec 30, 2022
Hidden Markov Models in Python, with scikit-learn like API

hmmlearn hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models. For supervised learning learning of HMMs and

null 2.7k Jan 3, 2023
Deep universal probabilistic programming with Python and PyTorch
Deep universal probabilistic programming with Python and PyTorch

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

null 7.7k Dec 30, 2022
Fast, flexible and easy to use probabilistic modelling in Python.
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Jan 2, 2023
Comments
  • Module 2-create your own functions - text columns

    Module 2-create your own functions - text columns

    Your tutorials are fantastic! minor format issues: the multiple column format in some pages (ex: module 2 in python training) collapse the text and making it unreadable. Hope to see it fixed to complete the tutorial! thank you.

    opened by catalicu 1
Releases(October2022)
Owner
SIB Swiss Institute of Bioinformatics
SIB Swiss Institute of Bioinformatics
GitHub Repository
Incubator for useful bioinformatics code, primarily in Python and R

Collection of useful code related to biological analysis. Much of this is discussed with examples at Blue collar bioinformatics. All code, images and

Brad Chapman 560 Jan 03, 2023
Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.

FilterPy - Kalman filters and other optimal and non-optimal estimation filters in Python. NOTE: Imminent drop of support of Python 2.7, 3.4. See secti

Roger Labbe 2.5k Dec 30, 2022
Bearsql allows you to query pandas dataframe with sql syntax.

Bearsql adds sql syntax on pandas dataframe. It uses duckdb to speedup the pandas processing and as the sql engine

14 Jun 22, 2022
Stitch together Nanopore tiled amplicon data without polishing a reference

Stitch together Nanopore tiled amplicon data using a reference guided approach Tiled amplicon data, like those produced from primers designed with pri

Amanda Warr 14 Aug 30, 2022
ToeholdTools is a Python package and desktop app designed to facilitate analyzing and designing toehold switches, created as part of the 2021 iGEM competition.

ToeholdTools Category Status Repository Package Build Quality A library for the analysis of toehold switch riboregulators created by the iGEM team Cit

0 Dec 01, 2021
SNV calling pipeline developed explicitly to process individual or trio vcf files obtained from Illumina based pipeline (grch37/grch38).

SNV Pipeline SNV calling pipeline developed explicitly to process individual or trio vcf files obtained from Illumina based pipeline (grch37/grch38).

East Genomics 1 Nov 02, 2021
The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases.

The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases.

Bell Eapen 14 Jan 02, 2023
Programmatically access the physical and chemical properties of elements in modern periodic table.

API to fetch elements of the periodic table in JSON format. Uses Pandas for dumping .csv data to .json and Flask for API Integration. Deployed on "pyt

the techno hack 3 Oct 23, 2022
A Pythonic introduction to methods for scaling your data science and machine learning work to larger datasets and larger models, using the tools and APIs you know and love from the PyData stack (such as numpy, pandas, and scikit-learn).

This tutorial's purpose is to introduce Pythonistas to methods for scaling their data science and machine learning work to larger datasets and larger models, using the tools and APIs they know and lo

Coiled 102 Nov 10, 2022
cLoops2: full stack analysis tool for chromatin interactions

cLoops2: full stack analysis tool for chromatin interactions Introduction cLoops2 is an extension of our previous work, cLoops. From loop-calling base

YaqiangCao 25 Dec 14, 2022
Synthetic data need to preserve the statistical properties of real data in terms of their individual behavior and (inter-)dependences

Synthetic data need to preserve the statistical properties of real data in terms of their individual behavior and (inter-)dependences. Copula and functional Principle Component Analysis (fPCA) are st

32 Dec 20, 2022
The Master's in Data Science Program run by the Faculty of Mathematics and Information Science

The Master's in Data Science Program run by the Faculty of Mathematics and Information Science is among the first European programs in Data Science and is fully focused on data engineering and data a

Amir Ali 2 Jun 17, 2022
A python package which can be pip installed to perform statistics and visualize binomial and gaussian distributions of the dataset

GBiStat package A python package to assist programmers with data analysis. This package could be used to plot : Binomial Distribution of the dataset p

Rishikesh S 4 Oct 17, 2022
Bigdata Simulation Library Of Dream By Sandman Books

BIGDATA SIMULATION LIBRARY OF DREAM BY SANDMAN BOOKS ================= Solution Architecture Description In the realm of Dreaming, its ruler SANDMAN,

Maycon Cypriano 3 Jun 30, 2022
A variant of LinUCB bandit algorithm with local differential privacy guarantee

Contents LDP LinUCB Description Model Architecture Dataset Environment Requirements Script Description Script and Sample Code Script Parameters Launch

Weiran Huang 4 Oct 25, 2022
A model checker for verifying properties in epistemic models

Epistemic Model Checker This is a model checker for verifying properties in epistemic models. The goal of the model checker is to check for Pluralisti

Thomas Träff 2 Dec 22, 2021
Vectorizers for a range of different data types

Vectorizers for a range of different data types

Tutte Institute for Mathematics and Computing 69 Dec 29, 2022
Statistical & Probabilistic Analysis of Store Sales, University Survey, & Manufacturing data

Statistical_Modelling Statistical & Probabilistic Analysis of Store Sales, University Survey, & Manufacturing data Statistical Methods for Decision Ma

Avnika Mehta 1 Jan 27, 2022
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

37 Dec 15, 2022
Spectacular AI SDK fuses data from cameras and IMU sensors and outputs an accurate 6-degree-of-freedom pose of a device.

Spectacular AI SDK examples Spectacular AI SDK fuses data from cameras and IMU sensors (accelerometer and gyroscope) and outputs an accurate 6-degree-

Spectacular AI 94 Jan 04, 2023