Technologies

Medical and biomedical data analysis in Python

Comprehensive training in medical and biomedical data analysis using Python. Participants will learn pandas and scipy for clinical data analysis, BioPython for bioinformatics, medical imaging tools (SimpleITK, pydicom), Electronic Health Records (EHR) data processing, and FDA/HIPAA compliance requirements.

Why choose this training?

Python has become one of the most important tools in biomedical data analysis, offering a rich ecosystem of libraries for clinical data processing, bioinformatics, and medical imaging. Growing regulatory requirements in the pharmaceutical and medical sector mean that specialists must combine advanced programming skills with knowledge of FDA and HIPAA compliance.

During the training, participants will learn practical applications of pandas and scipy for clinical data analysis, BioPython for bioinformatics, and SimpleITK and pydicom tools for medical image processing — with emphasis on EHR data processing and regulatory compliance.

After completing the training, participants will be able to: analyze clinical trial data using pandas and scipy, apply BioPython for biological sequence and genomic data analysis, process medical images in DICOM format using SimpleITK and pydicom, build pipelines for Electronic Health Records data processing. These competencies directly translate into higher efficiency in IT project execution.

This training is particularly valuable for: data analysts working in the medical and pharmaceutical sector, bioinformaticians and biostatisticians, clinical researchers using Python for data analysis.

What sets our approach apart?

At EITT, we believe the best learning happens through practice. During 3 days of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but above all the ability to apply it in practice.

With over 2,500 trainings in our portfolio and a 4.8/5 rating from participants, EITT is a trusted partner in competency development for organizations of all sizes. Our trainers are practitioners with years of experience who share current knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.

Benefits

Analyze clinical trial data using pandas and scipy
Apply BioPython for biological sequence and genomic data analysis
Process medical images in DICOM format using SimpleITK and pydicom
Build pipelines for Electronic Health Records data processing
Perform survival analysis with the lifelines library
Apply NLP methods for medical text processing
Create reproducible analytical reports in Jupyter Notebook
Ensure analysis compliance with FDA and HIPAA requirements

Who is this training for?

Data analysts working in the medical and pharmaceutical sector

Bioinformaticians and biostatisticians

Clinical researchers using Python for data analysis

Data specialists in pharmaceutical companies and CROs

Python programmers interested in biomedical applications

Data engineers in healthcare organizations

Professionals responsible for medical image processing

Prerequisites

Basic knowledge of Python (variables, functions, data structures, loops)
Familiarity with the pandas library basics
Basic understanding of descriptive statistics
Own laptop with Python 3.9+ and Jupyter Notebook installed

Training program

The Python ecosystem for biomedical sciences
Environment setup: Jupyter, conda, virtualenv
Overview of key libraries: pandas, numpy, scipy, scikit-learn
Medical data standards (HL7, FHIR, CDISC)
FDA and HIPAA regulatory requirements in data analysis

Loading and cleaning clinical data with pandas
Statistical analysis of clinical trial results
Survival analysis with the lifelines library
Medical data visualization with matplotlib and seaborn
Handling missing data and imputation in a medical context

Introduction to BioPython — sequences, structures, databases
DNA, RNA, and protein sequence analysis
Searching biological databases (NCBI, UniProt)
Sequence alignment and phylogenetic analysis
Processing data from NGS experiments

Loading DICOM images with pydicom
Image processing and filtering with SimpleITK
Anatomical structure segmentation
Radiomic feature extraction
Integration of imaging data with clinical data

Working with Electronic Health Records data
Medical text processing with NLP (spaCy, scispaCy)
Anonymization and de-identification of patient data
Building reproducible analytical pipelines
Documentation and reporting compliant with FDA requirements

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Training Topics

Frequently asked questions

What prerequisites do I need to meet before the training?

Basic knowledge of Python (variables, functions, data structures, loops) and the pandas library is required. Basic understanding of descriptive statistics is recommended. Knowledge of biology or medicine is not required — key concepts will be explained during the training.

What format is the training delivered in and how long does it last?

The training lasts 3 days and is available in online (live) and on-site formats. The program combines lectures with intensive hands-on exercises using real medical datasets. Participants receive training materials, ready-to-use Jupyter notebooks, and a completion certificate.

Who is this training designed for?

The training is designed for data analysts in the medical and pharmaceutical sector, bioinformaticians, clinical researchers, and data specialists in pharmaceutical companies and CROs. It is also suitable for Python programmers looking to develop competencies in biomedical applications and data engineers in healthcare organizations.