• Advanced Courses in Life Sciences

    Header of Systems Biology at Transmitting Science

Online Course – 3rd Edition

Python Machine Learning in Biology

July 20th-24th, 2020

Statistics and Bioinformatics

Statistics and Bioinformatics


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This course will be delivered ONLINE: 22.5 hours of online live lessons,  plus 12.5 hours of recorded classes and assignments. A good internet connection is required to follow the course.

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).




This course will be delivered online.

Please check the schedule for the live online part, and be aware that it is GMT+1.


July 20th-24th, 2020




35 hours Online.

This course is equivalent to 2 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 14 participants and will be occupied by strict registration order. If the course fills up there will be an assistant instructor to help during the practise time.

Participants who have completed the course will receive a certificate at the end of it.

Nichole Bennett instructor fro Transmitting Science

Nichole 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 June 30th, 2020):
  • 460 € *
    (368 € for Ambassador Institutions)
  • Regular (after June 30th, 2020):
  • 575 € *
    (460 € for Ambassador Institutions)
  • This includes the course material (VAT included).
    * Participants from companies/industry will have an extra charge of 100 €.

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


  • Monday to Friday (GMT+1):
    • 1:30pm-3:00pm Q&A session and live coding with the instructor
    • 3:00pm-4:00pm Break
    • 4:00pm-7:00pm Coding exercises (supervised by the instructor)

The rest of the time will be taught with recorded classes and assignments, to be done between the live sessions.


Discounts are not cumulative and apply only on the Course Fee. We offer the possibility of paying in two instalments (contact courses.greece@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 the country where the course will be held, as well as PhD students based in that country without any grant or scholarship to develop their PhD, could benefit from a 40 % discount on the Course Fee. If you want to ask for this discount, please contact the course coordinator. That would apply for a maximum of 2 places and they will be covered by strict inscription order.