Course Content

Combined Syllabus Units

Unit 1: Introduction to Python and Basics

• Python overview, features, installation, and environment setup (IDLE, Jupyter Notebook).

• Basic syntax: variables, data types (int, float, str, bool, complex), operators (arithmetic, comparison, logical, bitwise, assignment).

• Input/output: print(), input(), type conversion.

• Comments, indentation, and first programs.

• (Common in CSVTU foundational courses; setup from NIT Raipur ML tools.)

Unit 2: Control Flow and Loops

• Conditional statements: if, if-else, if-elif-else, nested if.

• Loops: while, for (with range(), iterables), break, continue, pass.

• Loop else clause.

• (Core problem-solving focus in CSVTU; basic scripting in both.)

Unit 3: Data Structures

• Lists: creation, indexing, slicing, methods (append, extend, insert, remove, pop, sort, reverse).

• Tuples: immutable sequences, packing/unpacking.

• Sets: unordered, unique elements, operations (union, intersection, difference).

• Dictionaries: key-value pairs, methods (keys, values, items, get, update).

• Comprehensions (list, dict, set).

• Strings: manipulation, methods, formatting, slicing.

• (Strong emphasis in CSVTU; foundational for data handling in NIT Raipur.)

Unit 4: Functions and Modules

• Defining functions: def, parameters (positional, keyword, default, variable-length *args/**kwargs).

• Return values, scope (local/global), lambda functions.

• Recursion basics.

• Modules: import, standard libraries (math, random, sys), creating custom modules.

• Exception handling: try-except-else-finally, raise.

• (Essential in both for modular code.)

Unit 5: File Handling and Advanced Topics

• Files: open modes (r, w, a, r+), reading/writing (read(), readline(), write()), with statement.

• Text vs. binary files, CSV/JSON handling basics.

• Object-Oriented Programming: classes, objects, init, inheritance, polymorphism, encapsulation.

• (File ops in CSVTU; OOP often in applied contexts.)

Unit 6: Data Science/ML Tools (NIT Raipur Emphasis)

• NumPy: arrays, operations, indexing, broadcasting.

• Pandas: Series, DataFrame, data import (CSV/Excel), cleaning, grouping, merging.

• Matplotlib: basic plots (line, bar, scatter, histogram), customization.

• Introduction to Jupyter for interactive coding.