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Cancer Bioinformatics in Python: Pandas, Biopython & Machine Learning

A hands-on cancer bioinformatics course using Python, Pandas, Biopython, and machine learning to analyze, visualize, and interpret cancer-related biological data.

  • 5.0/5
  • English
  • Updated Sep 2026
INR

₹45000

₹60000 25% off
USD

$600

$700 14% off

Indian learners pay in INR; international learners are billed in USD.

Enroll for International Students

Paying from outside India? Use this link to complete your payment.

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Cancer Bioinformatics in Python: Pandas, Biopython & Machine Learning

About this course

Cancer Bioinformatics in Python: Pandas, Biopython & Machine Learning is a practical, hands-on course designed to introduce participants to Python-based approaches for analyzing and interpreting cancer-related biological and genomic data.

Participants will learn how to use Python, Pandas, and Biopython for biological data processing, manipulation, exploration, and visualization. The course also introduces machine-learning concepts and workflows that can be applied to biological datasets, including data preprocessing, feature preparation, model development, performance evaluation, and interpretation.

Through guided practical exercises, participants will work with representative cancer-related datasets and learn how computational programming and machine learning can support cancer research, biomarker exploration, and biological data analysis.

What you will learn

Understand the applications of Python in cancer bioinformatics.
Develop practical skills in Python for biological data analysis.
Use Pandas to import, clean, manipulate, and analyze biological datasets.
Use Biopython for sequence and biological data processing.
Perform exploratory data analysis and generate meaningful visualizations.
Prepare biological datasets for machine-learning applications.
Understand basic supervised machine-learning concepts and workflows.
Build and evaluate introductory machine-learning models.
Understand feature selection and model-performance metrics.
Explore potential applications of machine learning in cancer biomarker research.
Interpret computational results in a biological context.

Skills you will gain

Python Programming Pandas Biopython Biological Data Processing Sequence Analysis Data Cleaning Data Manipulation Exploratory Data Analysis Data Visualization Feature Engineering Machine Learning Classification Model Evaluation Feature Selection Biomarker Analysis Cancer Bioinformatics
Certification

Available

Issued by Dr. Omics Edu

Course curriculum

1 module

  • Day 1 – Python essentials for bioinformatics: installation and environment setup (conda/pip, Jupyter).
  • Day 2 – Data types in bioinformatics computing (int, float, string, bool, None).
  • Day 3 – String handling for DNA and protein sequences (slicing, methods, regex basics).
  • Day 4 – Efficient data structures for biological data (lists, tuples, dictionaries, sets).
  • Day 5 – Control structures for genome data processing (loops, conditionals, comprehensions).
  • Day 6 – Functions for automating bioinformatics tasks (arguments, return values, modularity).
  • Day 7 – Importing, exporting, and handling biological files (FASTA, FASTQ, CSV, JSON).
  • Day 8 – NumPy fundamentals and DataFrames with Pandas for tabular biological data.
  • Day 9 – Data manipulation for sequence and expression analysis using Pandas.
  • Day 10 – Visualization of genomic and proteomic data (Matplotlib basics).
  • Day 11 – Interactive visualizations with Seaborn and Plotly.
  • Day 12 – Biopython fundamentals: sequence objects, parsing, and format conversion.
  • Day 13 – Biopython for sequence and structural analysis (alignments, motifs, PDB handling).
  • Day 14 – Machine learning biomarkers using scikit-learn (classification/regression basics).
  • Day 15 – Single-cell analysis introduction with Scanpy and AnnData (data structures, preprocessing).

What you need to start

  • Basic understanding of molecular biology, genetics, or cancer biology.
  • Basic computer skills.
  • Prior Python programming experience is helpful but not mandatory.
  • Basic understanding of statistics is recommended.
  • No prior machine-learning experience is required.

Who this course is for

  • Undergraduate and postgraduate students in Bioinformatics, Biotechnology, Biochemistry, Biology, Genetics, Microbiology, and Life Sciences.
  • PhD scholars and research students.
  • Bioinformatics and computational biology researchers.
  • Students interested in Python-based cancer data analysis.
  • Beginners interested in applying machine learning to biological datasets.
  • Life-science professionals looking to develop practical programming and computational skills.
INR

₹45000

₹60000 25% off
USD

$600

$700 14% off

Indian learners pay in INR; international learners are billed in USD.

Enroll for International Students

Paying from outside India? Use this link to complete your payment.

Make an Enquiry
Active batch
Open for enrolment
Programming for Cancer Bioinformatics (Python)
  • Starts 19 Oct 2026
  • Ends 06 Nov 2026
  • Timing 7:00 PM – 8:00 PM
  • Days Mon, Tue, Wed, Thu, Fri
  • Platform MS Teams
This course includes
  • Format Live
  • Level Advanced
  • Language English
  • Modules 1
  • Certificate Yes
  • Provider Dr. Omics Edu
  • Live
  • instructor-led interactive sessions.
  • Hands-on Python programming exercises.
  • Practical work with Pandas and Biopython.
  • Representative cancer-related biological datasets.
  • Step-by-step data preprocessing and analysis workflows.
  • Introduction to machine-learning workflows for biological data.
  • Model evaluation and interpretation exercises.
  • Supporting datasets and learning resources.
  • Course materials for reference and practice.
  • Certificate of participation/completion as applicable.
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