A beginner-friendly Python course for biologists: from your first line of code to sequence analysis with Biopython and a machine-learning project.
Everything you need to know before enrolling.
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Learn at your own pace with recordings
Python is the most widely used programming language in modern biology. This course teaches it from zero, using biological examples at every step: DNA sequences, gene lists, codon tables and expression data instead of abstract exercises.
You move from variables and loops to functions, file handling, Pandas, NumPy and Biopython, and then build a real machine-learning model that classifies proteins. By the end you can write your own scripts to automate everyday lab and data-analysis tasks.
Shivani Singh is a PhD scholar in Computational Biology & Bioinformatics at Sharda University, currently working as a Bioinformatics Educator. She specializes in protein structure prediction, molecular docking, and AI-driven drug repurposing. With an MSc in Bioinformatics, she has conducted hands-on workshops in molecular docking, MD simulations, and bioinformatics pipelines. Her teaching combines research experience with practical skills in structural bioinformatics, cheminformatics, and machine learning for life sciences.
25 answers about this course
The course is designed for biologists, biotechnology and life science students, researchers, bioinformatics learners, and anyone interested in applying Python to biological data.
No. The course is beginner-friendly and starts with Python fundamentals.
No. The course introduces programming through biology-focused examples and gradually progresses toward bioinformatics applications.
The course has about 15 hours of recorded lessons across 15 modules, which you can complete at your own pace.
The course is delivered through recorded sessions that you can watch anytime.
Anytime. All sessions are recorded, so you can learn on your own schedule.
The course is conducted in English.
Yes. The curriculum starts with Python fundamentals including variables, data types, strings, lists, dictionaries, sets, control flow, and functions.
Yes. Biological examples include DNA/RNA sequence manipulation, GC-content calculation, gene lists, codon tables, protein analysis, and biological datasets.
Yes. The course includes installation and use of Python with Anaconda for package management.
Yes. Both Jupyter Notebook and VS Code are introduced as Python development environments.
Yes. Pandas is introduced for biological data analysis, while NumPy is covered for numerical and scientific computing.
Yes. The course includes sequence objects, sequence manipulation, reverse complements, transcription, translation, and protein analysis using Biopython.
Yes. File handling includes reading and writing biological files such as FASTA files.
Yes. The final portion of the course introduces machine learning for biological applications using scikit-learn.
Logistic regression is used to build a simple binary classification model for protein classification.
Yes. The course includes an end-to-end machine learning workflow involving protein features, training/testing data, model development, evaluation, and prediction.
Protein classification, such as distinguishing enzyme and non-enzyme classes, is used as an example application.
Yes. Biopython's ProteinAnalysis is used to calculate features such as molecular weight and aromaticity.
Yes. The course covers training/testing splits, feature scaling using StandardScaler, feature preparation, and biological labels.
Yes. Basic visualization using Matplotlib or Pandas plots is included during the machine learning data preparation stage.
Yes. A certificate of completion is provided.
You will be able to write Python programs, manipulate biological sequences, work with biological datasets, use Pandas and NumPy, perform sequence analysis with Biopython, prepare biological data for machine learning, and build a basic protein classification model.
Yes. The curriculum focuses on practical Python skills applicable to genomics, proteomics, sequence analysis, biological data processing, and machine learning.
You get full access to all course materials for 3 months from the date of enrollment.
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Hear from learners building practical skills with Bio Help Learning.
★★★★☆I really enjoyed the course and found it very useful: it gave a clear and practical overview of the whole WGS workflow, from raw reads to a VCF, and the hands-on sessions helped a lot.
Marc Peralta SerranoUniversity of BarcelonaWhole Genome Sequencing (WGS) Data Analysis Training
★★★★★The workshop provided me an understanding of the principles and techniques involved in bioinformatics, molecular docking, protein-ligand interactions, homology modeling, and drug design.
Ridam BhowmickMaulana Abul Kalam Azad University of Technology, West Bengal.Molecular Docking and Drug Discovery
★★★★★As i am from biotechnology background this workshop, actually very helpfull for me
Anushka ChavanDeogiri college AurangabadBioinformatics Training
★★★★★Learn about different molecular docking tools.
Shovan NaskarIndian Institute of Technology (Banaras Hindu University), VaranasiBioinformatics Workshop
★★★★★This workshop helped me to learn working with various softwares and also to develop an interest over drug designing
Subhodeep SahaUniversity of CalcuttaBioinformatics Training
★★★★★From this workshop I learned many new things such as docking, about new websites,how to use Swiss software,basic bioinformatics etc, it's very helpful for me.
Sayan DuttaUniversity of CalcuttaBioinformatics Training
★★★★☆The workshop was informative and provided valuable insights into bioinformatics tools and techniques.
Santhosh M EReva University BangaloreBioinformatics Tools and Techniques
★★★★★The Drug Discovery Workshop was highly informative and helped me gain valuable knowledge about the drug discovery process, including molecular docking, drug–target interactions, and computational tools used in drug design.
ANUSHA P VCSIR-CCMB, Hyderabad IndiaDrug Discovery WorkshopA growing community learning bioinformatics and life-science skills across borders.
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