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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.
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.
Isha Gupta
Intermediate
Recorded
English
Certificate Provided
Time 7:00 to 8:00 PM IST
Last date 07 July
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.
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
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)
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
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
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
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
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
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
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)
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
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
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
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 print outputs or Pandas plots
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?
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
Isha Gupta, an accomplished Bioinformatics faculty from Aligarh. Holds B.Tech and M.Tech degrees in Bioinformatics. Passionate about genomics and proteomics, with expertise in diverse bioinformatics projects. Dedicated to fostering student growth in the field.
I learned how to use python for data retrieval, analysis and many more things.
I got a chance to know about application of python in life sciences
I learnBiopython and data analysis
I learned one of the main programne
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.
