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Python programming for Biologist

Unlock the power of Python in biological research with our 15-day Python Programming for Biologists course! Tailored for beginners, this intensive 1-hour daily program equips biologists with essential programming skills to analyze and interpret biological data. From sequence analysis to machine learning, this course bridges biology and computational tools, empowering you to tackle real-world bioinformatics challenges.

Why Join This Course?

  • Biology-Focused Learning: Explore Python through practical, biology-relevant examples like DNA sequence analysis, protein classification, and gene expression data processing.

  • Beginner-Friendly: No prior programming experience required—just a passion for biology and a desire to learn!

  • Hands-On Approach: Each 1-hour session combines concise theory with engaging exercises, ensuring you apply concepts immediately.

  • Cutting-Edge Tools: Master Python, Biopython, Pandas, NumPy, and scikit-learn to handle modern bioinformatics tasks.

  • Build a Machine Learning Model: By the end, create a simple model to classify proteins, a valuable skill for research and industry.

Register Now

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Educator

Shivani Singh

Difficulty

Beginner to Expert

Mode

Live Session

language

Language

English

Python Programming

Mode - Live Session

Certificate Provided

Hours 15 Hours

Time 7:00 PM IST

Course Start Date 22 July

Last date 20 July

Course Benefits

  • Practical Skills: Gain hands-on experience with tools used in genomics, proteomics, and bioinformatics.

  • Flexible Learning: Daily 1-hour sessions fit busy schedules, with exercises you can revisit anytime.

  • Real-World Applications: Learn to process biological datasets and build models for tasks like protein function prediction.

  • Future-Ready: Acquire in-demand skills for academia, biotech, and pharmaceutical industries.

Python programming for Biologist

Day-1

Introduction to Python for Biologists

  • Welcome and class introductions

  • Overview of the 15-day training program

  • Why Python for biologists? (e.g., bioinformatics, data analysis)

  • Python in biology: applications (e.g., genomics, protein analysis)

  • Python vs. other languages (R, Perl, MATLAB) with biology examples

Day-2

Setting Up Python and Writing First Code

  • Installing Python and Anaconda for easy package management

  • Introduction to Jupyter Notebook and VS Code for coding

  • Writing and running “Hello World” using Python’s print function

  • Exploring variables, data types (int, float, string), and basic expressions

  • Hands-on: Simple calculations (e.g., DNA base pair counts)

Day-3

Working with Strings and Basic Operations

  • String operations: concatenation, slicing, and formatting

  • Biology example: Manipulating DNA/RNA sequences as strings

  • Arithmetic, relational, and logical operations in Python

  • Hands-on: String quiz and calculating GC content of DNA

  • Introduction to lists: creating and accessing elements

Day-4

Lists, Tuples, and Data Structures

  • Lists: operations (append, remove, sort) with biology examples (e.g., gene lists)

  • Tuples: immutable sequences for fixed data (e.g., codon tables)

  • Hands-on: List/tuple quiz and exercises (e.g., storing sequence lengths)

  • Overview of Python data structures for biological data

Day-5

Dictionaries and Sets for Biological Data

  • Dictionaries: key-value pairs for storing data (e.g., gene annotations)

  • Sets: unique elements for tasks like unique amino acids

  • Hands-on: Exercises with dictionaries (e.g., mapping codons to amino acids)

  • Practice quiz on sets and dictionaries with biology context

Day-6

Control Flow in Python

  • Conditional statements: if, elif, else with biology examples

  • Introduction to loops: for and while loops

  • Hands-on: Filtering sequences based on length or content

  • Using else with loops for biological data processing

Day-7

Functions and Modular Programming

  • Defining and calling functions in Python

  • Writing functions for biological tasks (e.g., calculating molecular weight)

  • Hands-on: Writing functions to process sequence data

  • Introduction to objects and classes with simple biology examples

Day-8

File Handling and Introduction to Pandas

  • Reading and writing files using open (e.g., FASTA files)

  • Introduction to Pandas for biological data analysis

  • Loading and exploring datasets (e.g., gene expression data)

  • Hands-on: Reading a CSV file and basic Pandas operations

Day-9

NumPy for Scientific Computing

  • Introduction to NumPy for numerical operations

  • One- and two-dimensional NumPy arrays for biological data

  • Basic matrix operations (e.g., for sequence alignment scores)

  • Hands-on: NumPy exercises with biology data (e.g., protein feature arrays)

Day-10

Biopython for Sequence Analysis

  • Introduction to Biopython and its role in bioinformatics

  • Working with sequence objects (DNA, RNA, protein)

  • Sequence operations: slicing, concatenation, case conversion

  • Hands-on: Reverse complements, transcription, and translation

Day-11

Introduction to Machine Learning for Biologists

  • Overview of machine learning in biology (e.g., protein classification)

  • Introduction to scikit-learn for simple ML tasks

  • Understanding the protein classification problem (e.g., enzyme vs. non-enzyme)

  • Hands-on: Loading sample protein sequences and labels

Day-12

Extracting Features from Protein Sequences

  • Using Biopython’s ProteinAnalysis to compute features (e.g., molecular weight, aromaticity)

  • Hands-on: Extracting features from protein sequences

  • Storing features in NumPy arrays for machine learning

  • Brief overview of feature scaling and its importance

Day-13

Preparing Data for Machine Learning

  • Splitting data into training and testing sets using scikit-learn

  • Scaling features with StandardScaler for better model performance

  • Hands-on: Preparing a dataset with protein features and labels

  • Visualizing data with simple matplotlib or Pandas plots

Day-14

Building a Simple Machine Learning Model

  • Introduction to logistic regression for binary classification

  • Hands-on: Training a logistic regression model on protein features

  • Evaluating model accuracy on training and test sets

  • Discussion: What does the model learn from protein features?

Day-15

Predicting and Applying Machine Learning

  • Hands-on: Predicting classes for new protein sequences

  • Interpreting predictions (Positive vs. Negative class)

  • Saving and loading models with scikit-learn

  • Wrap-up: Real-world applications and resources for further learning

  • Project & Quiz: End-to-end ML pipeline and Review quiz

Educator - Shivani Singh

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.

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FAQ

Session Joining link will be send to your register email on the day of session start you can join session by clicking joining link.

All Session are evening sessions time of session is between 7:00 to 8:00 PM IST.

Please read refund policy in refund policy section page.

No, any short Prerequisites knowledge you required to join the course.