Data Science for Life Sciences Advanced Course 5 Days

  • Five days DSfLS Advanced Course in an effective learning environment
  • Developed and delivered by industry-experienced and research-driven Life Scientists with Data Science and Machine Learning experts
  • Acquiring valuable knowledge on ML/DL skills in life science context
  • Including both theory and hands-on labs
  • Certification at completion

Day 1: ML/DL Fundamentals

Topics: Context and Healthcare applications – Machine Learning/ Convolutional neural network – Image classification : supervised learning/regression – Data-driven approach: Representation – Overfitting and Underfitting, Generalization – Back propagation fundamentals/ Model selection/ Validation/ Parameters 

Goals:

  • Understand the basics of ML/DL in Healthcare context

Day 2: ML/DL Frameworks

Topics: Methods, models and practices for Healthcare – Reinforcement Learning Paradigm & Markov Decision Processes – Q-Learning, Sequence data analysis, Series data – Reccurent neural networks and representation techniques – FRNN (Fully Reccurent NN) & LSTM (Long Short-Term Memory)

Goals:

  • Specify a meaningful pipeline
  • Get hands-on experience on Deep Learning

Day 3: Relevant ML/DL Frameworks

Topics: Notebook/ TensorFlow/ Google Cloud ML engine hands-on lab – Learn & Optimize (experimentation, recent architecture: Inception/ Yolo) – Deep Learning use cases in Healthcare (feature selection, classification, regression, visualization)

Goals:

  • Get hands-on experience in Machine Learning & Deep Learning (ML/DL)

Day 4: Building a ML Life Sciences application

Topics: Use case: Breast cancer stage prediction – Use Cloud computing services based on Machine Learning As a Service (MLaaS) – Build and deploy a Flask application using Docker

Goals:

  • Implementing AI services for enterprise applications

Day 5: Evaluation Metrics of ML Models

Topics: Collaboration between clinicians and scientists – Training sets, Testing sets, Seens and Unseens – Various types of errors and cross-validation protocols – Parameter tuning and evaluation – Performance management and Observability

Goals:

  • Evaluate & Leverage your ML models

Registration Details

Skills Pre-requisites:

  • Basic knowledge in Life Science related fields, Statistics, Clinical Analysis and Assays, Pharmaceutical Research or Medical Research.
  • Basic knowledge in Statistics GPU and Machine Learning.
  • Programming experience (preferably in Python, R).
  • Git and GitHub experience.

Further details:

  • Full-time availability: Course runs 9 am – 5 pm
  • Given the circumstances and local regulations, some of our upcoming bootcamps will take place remotely
  • By submitting your registration, you confirm your registration and participation in the selected course. You will then receive an invoice from our team
  • Contact us if you have questions before registering

Next Courses & Registration

Ready to unlock your Life Scientist potential with Data Science?

Public bootcamp course dates and options will be published soon. In the meantime, please use the contact form below to reach out us for more information and training options.

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