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BP101TSemester 12 creditsTheoryKEY SUBJECT

Basics of Python Programming for Pharmaceutical Sciences

Complete unit-wise syllabus for BP101T as per the PCI B.Pharm NEP 2020 curriculum (Semester 1 — Python + Core Sciences Foundation).

All Sem 1 Subjects
URL:https://pharmacode.in/syllabus/semester-1/bp101t-basics-of-python-programming-for-pharmaceutical-sciences/

Unit-wise Syllabus

5 Units
1
Introduction to Python Programming6 Hours
  • Installing Python and an IDE (Jupyter Notebook, PyCharm, VS Code); advantages of IDEs over text editors
  • Python variables and data types (integers, floats, strings, booleans); type casting; basic operators (arithmetic, comparison, logical); input/output operations
  • Basic string operations and manipulation techniques
  • Introduction to standard libraries and third-party libraries; installing and uninstalling libraries
2
Control Structures & Functions6 Hours
  • Conditional statements: if, if-else, if-elif-else, nested conditions
  • Loops: for loop, while loop; break and continue statements
  • Defining and calling functions; passing arguments and returning values
  • Writing modular programs for pharmaceutical applications — dosage calculation and BMI calculation
3
Data Structures & File Handling6 Hours
  • Lists, tuples, and dictionaries; indexing and slicing; basic operations on lists and dictionaries; string manipulation techniques
  • Introduction to NumPy arrays; basic operations using NumPy (array creation, arithmetic operations)
  • Reading and writing CSV files; understanding structured healthcare datasets
  • Importing small pharmaceutical datasets and performing basic data access and manipulation tasks
4
Data Handling with Pandas6 Hours
  • Introduction to Pandas library; Pandas Series and DataFrame structures
  • Reading CSV and Excel files — PK study datasets and ADR reports
  • Inspecting datasets using head(), tail(), info(), describe(); data cleaning and handling missing values
  • Filtering and selecting data based on conditions; grouping data and performing aggregation functions
5
Data Visualization with Matplotlib6 Hours
  • Introduction to Matplotlib; creating line plots, histograms, scatter plots, and box plots
  • Labeling axes, titles, and legends
  • Visualizing pharmaceutical datasets — concentration-time curves for oral and IV administration, ADR reporting rates, dissolution profiles
  • Scientific interpretation of plots

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  • Reference textbooks and recommended reading list
  • Previous year question papers (PYQ)
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Other subjects in Semester 1

BP102TGeneral PharmacyBP103THealthcare Psychology and Communication SkillsBP104THuman Anatomy, Physiology and Pathophysiology IBP105TIntroduction to PharmacognosyBP106TPharmaceutical Inorganic and Analytical ChemistryBP107P–BP111PPracticals — General Pharmacy, Healthcare Psychology, Anatomy, Pharmacognosy, Inorganic Chemistry
BP102T