Learn Python Programming specifically for Pharmaceutical Sciences and build practical skills you can apply to pharmaceutical data and analysis.
This course is designed for B.Pharm students who want to understand Python from the ground up without assuming prior programming experience.
You will start with the fundamentals of Python, including variables, data types, operators, input/output, strings, conditional statements, loops, functions, and libraries. The course then progresses into Python data structures, NumPy, CSV file handling, and data manipulation using Pandas.
The course also introduces data visualization using Matplotlib, where you will learn how to create and understand line charts, histograms, scatter plots, box plots, and other visualizations using pharmacy-related datasets.
Throughout the course, concepts are connected to pharmaceutical examples and practical applications. You will work with datasets and practical exercises involving areas such as medicine inventory, healthcare data, adverse drug reaction (ADR) analysis, pharmacokinetic data, drug concentration-time curves, and dissolution profiles.
Python programming fundamentals from beginner level
Control structures and functions
Lists, tuples and dictionaries
NumPy arrays and operations
Reading and writing CSV and Excel data
Data cleaning and handling missing values
Data analysis using Pandas
Data visualization using Matplotlib
Pharmaceutical and healthcare datasets
Practical pharmaceutical data analysis
Programming practice through assignments and quizzes
The course also includes practice programs, assignments, quizzes, source code, sample datasets, important university questions, and assignment solutions to help you reinforce what you learn.
Whether you are completely new to programming or want to connect your pharmaceutical knowledge with modern data and programming skills, this course provides a structured path from Python fundamentals to practical pharmaceutical data analysis.
By the end of the course, you will have the foundation needed to work with pharmaceutical datasets, perform basic analysis, create meaningful visualizations, and apply Python to pharmaceutical and healthcare-related problems.
