AI in Healthcare and Drug Discovery

AI in Healthcare & Drug Discovery is a practical course designed to help learners understand, apply, and operationalize AI tools across the pharmaceutical and healthcare landscape. As life sciences organizations increasingly adopt AI-driven research, professionals who can bridge data science and drug development are becoming essential across the industry. In this course, you’ll explore the core principles of AI in pharma and healthcare, including how machine learning accelerates drug discovery, how predictive models support clinical trial design, and how genomic data enables precision medicine. You’ll learn how to analyze real-world evidence, extract insights from clinical texts, and apply AI responsibly within regulatory frameworks. Through guided exercises and hands-on projects, you’ll gain practical experience using industry tools such as Python, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, SQL, Jupyter Notebooks, MLflow, Databricks, RDKit, DeepChem, Biopython, Hugging Face Transformers for Biomedical NLP, spaCy, Apache Spark for Healthcare Data, and Power BI and Tableau for Clinical Dashboards. You’ll also work with specialized platforms, including Orange Data Mining, KNIME, cBioPortal, EpiGraphDB, LitVar 2.0, and Airtable for AI project management. By the end of the course, you’ll be able to design and evaluate AI-driven drug discovery pipelines, optimize clinical trials using intelligent analytics, and apply ethical AI governance frameworks to deliver real impact in pharmaceutical and life sciences environments

• Understand AI applications across the pharmaceutical value chain
• Analyze clinical, genomic, and real-world evidence data
• Build predictive models to support clinical trial design
• Apply responsible and compliant AI practices in healthcare

• Basic computer and internet skills
• Ability to follow course materials in English