About this course
The Six Month Certification Course: Python & AI/ML for Genomics Data Analysis at Dr. Omics Edu is a 6-month live, mentor-led online course in Python, AI and machine learning for genomics. Twenty-four modules take you from Python basics to AI and ML models built on real genomics data. You learn on real biological data and finish with a machine learning model built on a genomics dataset that you can show to an employer, a guide or an admissions panel. In this course you move through python programming for biological data, data wrangling with pandas and numpy and genomics data types and preprocessing, then apply them in a hands-on project. The course runs live online across 24 modules, so you can ask your mentor questions as you work. It is an advanced-level course, best suited to learners who already know the fundamentals. It is offered as a certification course.
What you will learn
• Python programming for biological data
• Data wrangling with Pandas and NumPy
• Genomics data types and preprocessing
• Supervised and unsupervised machine learning models
• Model evaluation, feature selection and validation
• Deep learning and AI applications in genomics
• A capstone genomics ML project
Bioinformatics data scientist, machine learning analyst in genomics, research associate, and a strong profile for computational biology roles.
Skills you will gain
Python
Pandas
NumPy
scikit-learn
Biopython
Jupyter
Certification
Available
Issued by Dr. Omics Edu
Course curriculum
24 modules
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Installing Python and using Jupyter Notebook/Google Colab
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Variables, data types, operators, input, and output
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Conditional statements: if, else, and elif
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For and while loops
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Introduction to lists, tuples, and dictionaries
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Practical: Calculator and basic Python programming exercises
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NumPy arrays and basic array operations
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Pandas DataFrames and CSV file handling
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Filtering, sorting, grouping, and merging datasets
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Missing-value handling and data cleaning
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String manipulation for biological sequences
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Practical: DNA sequence analysis using Python
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Functions, modules, and reusable code
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Object-oriented programming: classes, objects, and methods
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Organizing biological data using Python classes
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Regular expressions for sequence pattern matching
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Debugging and error handling
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Practical: Sequence-processing mini-project
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Introduction to Biopython and sequence objects
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Reading and writing FASTA files using SeqIO
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Sequence records, annotations, and translation
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Pairwise sequence alignment fundamentals
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Retrieving biological sequences using NCBI Entrez
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Practical: Automated sequence retrieval and analysis
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DNA, RNA, proteins, and the central dogma
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Genome structure, genes, exons, introns, and regulatory regions
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Sanger, next-generation, and third-generation sequencing
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SNPs, insertions, deletions, and structural variants
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Exploring genes using UCSC and Ensembl genome browsers
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Practical: Gene structure exploration and annotation
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FASTA and FASTQ formats and Phred quality scores
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SAM, BAM, and CRAM alignment file formats
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VCF structure and variant annotations
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Sequencing quality control and adapter trimming concepts
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Interpreting quality control reports
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Practical: Genomic file inspection and QC report interpretation
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Read alignment fundamentals using BWA and Bowtie
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Introduction to variant calling and GATK workflows
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RNA-seq pipeline and gene expression count matrices
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Genome annotation using GTF and GFF files
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Understanding end-to-end genomic data processing workflows
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Practical: Genomic pipeline walkthrough using sample outputs
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Searching and downloading datasets from NCBI and GEO
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Gene annotation using Ensembl and UCSC
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Introduction to TCGA and disease genomics resources
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Sample metadata and experimental design
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Dataset organization and reproducible file management
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Practical: Downloading and documenting a public genomic dataset
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Descriptive statistics: mean, median, variance, and standard deviation
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Probability rules and conditional probability
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Normal, binomial, and Poisson distributions
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Hypothesis testing fundamentals
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t-tests and chi-square tests
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Practical: Statistical analysis of sample biological data
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Multiple testing correction using Bonferroni and FDR
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Fundamentals of differential gene expression analysis
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Correlation and linear regression
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Effect size and statistical power
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Selecting appropriate statistical tests for genomic studies
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Practical: Differential expression analysis using a sample dataset
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Advanced plotting with Matplotlib and Seaborn
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Interactive visualization using Plotly
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Volcano plots and Manhattan plots
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Heatmaps and clustermaps for gene expression data
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Creating clear, publication-style visualizations
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Practical: Genomic visualization project
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Planning an exploratory data analysis workflow
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Data quality and completeness assessment
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Univariate and bivariate analysis
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Identifying patterns, relationships, and outliers
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Summarizing findings from genomic datasets
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Practical: End-to-end exploratory data analysis report
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Supervised and unsupervised learning
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Training, validation, and test datasets
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Linear and logistic regression
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Cost functions and gradient descent fundamentals
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Bias-variance trade-off and regularization
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Practical: Regression model development and comparison
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K-nearest neighbors and Naive Bayes
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Support vector machines and kernel concepts
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Decision trees and random forests
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Ensemble learning and introduction to XGBoost
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Comparing algorithms for classification tasks
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Practical: Machine learning algorithm comparison using genomic data
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Challenges of high-dimensional genomic datasets
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Feature selection: filter, wrapper, and embedded methods
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Lasso-based feature selection for gene identification
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Handling imbalanced data using SMOTE and class weighting
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Developing feature-selected classification models
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Practical: Genomic feature selection and classification project
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K-fold and stratified cross-validation
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Hyperparameter tuning using grid search and random search
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ROC-AUC and precision-recall curves
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Understanding overfitting, underfitting, and data leakage
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Building reliable model evaluation workflows
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Practical: Tuning and validating a genomic classifier
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Perceptrons and neural network architecture
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Activation functions and forward propagation
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Backpropagation and gradient descent
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Introduction to TensorFlow/Keras or PyTorch
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Training and evaluating basic neural networks
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Practical: Building a neural network for sample data
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Convolutional neural networks and convolutional layers
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One-hot encoding of DNA sequences
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CNN architecture for motif and promoter classification
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Model training and performance evaluation
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Interpreting training and validation results
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Practical: DNA sequence classification using a CNN
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Recurrent neural networks and LSTMs
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Attention mechanisms and transformer architectures
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Genomic and protein language models
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Introduction to DNABERT, ESM, and AlphaFold
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Using pretrained models for biological prediction tasks
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Practical: Guided pretrained genomic or protein model exercise
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Advanced clustering using DBSCAN and Gaussian mixture models
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Dimensionality reduction using PCA, UMAP, and t-SNE
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Introduction to genomics, transcriptomics, and proteomics
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Fundamentals of multi-omics data integration
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Clustering and visualization of multidimensional biological data
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Practical: Multi-dimensional biological data analysis project
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Importance of interpretable machine learning in genomics
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Permutation-based feature importance
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Understanding and applying SHAP values
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Introduction to LIME for explaining predictions
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Interpreting model outputs and identifying influential features
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Practical: Explainable AI analysis of a genomic classifier
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Saving and loading trained models using Pickle or Joblib
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Building prediction APIs using Flask or FastAPI
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Introduction to Docker and application containerization
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Overview of cloud deployment options
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Integrating a trained model into a basic prediction workflow
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Practical: Developing a local API for a genomic ML model
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Selecting a genomic dataset and defining a research question
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Data acquisition, cleaning, preprocessing, and exploratory analysis
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Feature engineering and model development
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Model evaluation, tuning, and interpretation
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Refining the project based on analytical findings
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Practical: Developing an end-to-end genomics AI/ML project
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Finalizing the Jupyter Notebook or project report
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Preparing presentation slides and summarizing key findings
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Reviewing methods, results, and model performance
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Capstone project presentation and feedback
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Final assessment and course completion certification
What you need to start
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A basic understanding of biology, biotechnology, genetics, bioinformatics, or related life sciences is recommended.
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No advanced Python programming experience is mandatory; foundational programming concepts are covered.
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Basic computer literacy and an interest in data analysis, genomics, or artificial intelligence.
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Access to a laptop or desktop with a stable internet connection.
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Willingness to participate in practical exercises, programming assignments, and project work.
Who this course is for
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B.Sc, M.Sc, B.Tech, M.Tech and PhD students in biotechnology, bioinformatics, life sciences or computer science who want to apply machine learning to biological data.
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Students interested in computational biology, genomics, and biological data science.
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Bioinformatics learners seeking to strengthen Python programming and AI/ML skills.
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Research scholars working with genomic, transcriptomic, or other biological datasets.
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Life science graduates interested in machine learning and deep learning applications.
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Researchers and professionals looking to incorporate data-driven methods into biological research.
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Beginners who want structured training and guided experience through genomics AI/ML projects.
