• Advanced Courses in Life Sciences

    Header of Systems Biology at Transmitting Science

Online Course – 4th Edition

Python Machine Learning in Biology

July 12th-16th, 2021


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This course will be delivered live online

Online live sessions from Monday to Friday from 14:30 to 16:30 and 17:30 to 20:00 (GMT+2, Madrid time zone).


Python Machine Learning in Biology

Course overview

The field of biological sciences is becoming increasingly information-intensive and data-rich. For example, the growing availability of DNA sequence data or clinical measurements from humans promises a better understanding of the important questions in biology. However, the complexity and high-dimensionality of these biological data make it difficult to pull out mechanisms from the data. Machine Learning techniques promise to be useful tools for resolving such questions in biology because they provide a mathematical framework to analyze complex and vast biological data. In turn, the unique computational and mathematical challenges posed by biological data may ultimately advance the field of machine learning as well.

This course will cover basics of the Python programming language as well as the pandas and sklearn Python libraries for data wrangling and machine learning.

By the end of this course, participants will understand:

  • How to input and clean data in Python using the pandas library
  • How to perform exploratory data analysis in Python
  • How to use the sklearn library in Python for machine learning workflows
  • How to choose an appropriate machine learning model for the task
  • How to use supervised machine learning models (SVM, Decision Trees, Neural Networks, etc.) for classification tasks
  • How to use unsupervised machine learning models for clustering tasks
  • How to evaluate machine learning models and interpret their results

This course is intended to give participants a conceptual overview of machine learning algorithms and an intuition for the mathematics underlying them, equipping participants to be able to choose and implement appropriate models for biological datasets.


Graduate or postgraduate degree in Life Sciences and basic knowledge of Statistics. While some Python knowledge is useful, the course will cover basic Python skills necessary to input, clean, and explore data as well as build and evaluate machine learning models.

All participants must have a personal laptop and a good internet connection (Windows, Macintosh, Linux).




July 12th-16th, 2021

Schedule and Course length

Online live sessions from Monday to Friday (GMT+2, Madrid time zone):

14:30 to 16:30 Q&A session and live coding with the instructor

17:30 to 20:00 Coding exercises (supervised by the instructor)

35 hours

22.5 hours of online live lessons, plus 12.5 hours of recorded classes and assignments.

This course is equivalent to 1 ECTS (European Credit Transfer System) at the Life Science Zurich Graduate School.

The recognition of ECTS by other institutions depends on each university or school.




Places are limited to 16 participants and will be occupied by strict registration order.

Participants who have completed the course will receive a certificate.

Nichole Bennett instructor fro Transmitting Science

Nic Bennett
The University of Texas at Austin
United States of America



Python Foundations

  • Morning: Python Basics, Handling Data in Pandas, Basic Pandas Data Cleaning
  • Afternoon: Exploratory Data Analysis in Pandas, Data Visualization in Python.


Supervised Machine Learning: Classification

  • Morning: KNN, Introduction to sklearn workflow.
  • Afternoon: Train/Test Split, and Bias-Variance Tradeoff, Model Evaluation.


Supervised Machine Learning: Classification

  • Morning: Decision Trees and Random Forest
  • Afternoon: Support Vector Machines


Unsupervised Machine Learning

  • Morning: Clustering Methods (K Means Clustering)
  • Afternoon: Advanced Clustering Methods Hierarchical Clustering, DBSCAN


  • Special Topics
  • Participants will have the option to learn a particular model or receive an introduction to Neural Networks theory and applications.


  • Course Fee
  • Early bird (until May 31st, 2021):
  • 520 €
    (416 € for Ambassador Institutions)
  • Regular (after May 31st, 2021):
  • 610 €
    (488 € for Ambassador Institutions)
  • The price is VAT included.
    After registration you will receive confirmation of your acceptance in the course. Payment is not required during registration.

You can check the list of Ambassador Institutions. If you want your institution to become a Transmitting Science Ambassador please contact us at communication@transmittingscience.com


Discounts are not cumulative and apply only on the Course Fee. We offer the possibility of paying in two instalments (contact haris.saslis@transmittingscience.com).

Former participants will have a 5 % discount on the Course Fee.

20 % discount on the Course Fee is offered for members of some organizations (Ambassador Institutions). If you want to apply to this discount please indicate it in the Registration form (proof will be asked later).

Unemployed scientists living in Spain may benefit from a 40 % discount on the Course Fee. If you would like to enquire about this discount, please contact the course coordinator. That would apply for a maximum of 2 places and they will be covered by strict registration order.