Python Programming

Topics

• What programming is, the instruction model, why computers need precise instructions
• Why Python, readability, versatility, the 2026 job market, what you can build
• Installing Python and VS Code, your working environment, the file structure of a Python project
• The Python interpreter, interactive mode vs running scripts, when to use each
• print(), your first function, string literals, numbers in print
• Comments, single-line with #, multi-line with triple quotes, why comments matter
• Variables, names for data, assignment, variable naming rules and conventions (snake_case)
• Data types, integers, floats, strings, booleans, Python's four primitive types
• type() function, checking what type a variable holds
• input(), reading from the user, the string nature of all input
• Type conversion, int(), float(), str(), converting between types, why input() always gives a string

Hands-on

• Hello World (three versions, print, variable, f-string)
• A program that asks your name and age and tells you what year you were born
• A temperature converter (Celsius to Fahrenheit and back)
• A simple receipt printer (quantity × price = total)

Every exercise uses real-world input and output. From the first day the programs do something recognisable, not abstract.

Topics

Conditions and decisions:

• Comparison operators, ==, !=, <, >, <=, >=, what each returns (True or False)
• Boolean operators, and, or, not, combining conditions
• if statement, the basic decision structure
• if-else, two paths
• if-elif-else, multiple paths, the ladder structure
• Nested if, decisions within decisions, when it is appropriate and when to refactor
• Truthiness, why if my_list: works, what Python considers truthy and falsy

Loops and repetition:

• while loop, repeat while a condition is true, the infinite loop risk, the exit condition
• for loop, iterating over a range, iterating over a sequence
• range(), start, stop, step, the three forms
• break, exiting a loop early
• continue, skipping the current iteration
• else with loops, the Python pattern most beginners never learn
• Nested loops, when you need to iterate over two dimensions

Hands-on

• Number guessing game (random number, user guesses, count attempts, hint after each guess)
• Grade classifier (marks in, grade out, A/B/C/D/F with boundaries)
• Multiplication table generator
• Prime number checker
• FizzBuzz (the interview classic, every programmer should have written it)
• Simple ATM loop (deposit/withdraw/balance/exit menu)

Assessment

• Ten logic problems of increasing difficulty, drawn from patterns that appear in TCS NQT and AMCAT coding assessments

Topics

Lists:

• Creating lists, accessing by index, negative indexing
• Slicing, start:stop:step, copying a list, reversing
• List methods, append, extend, insert, remove, pop, clear, sort, reverse, index, count, copy
• List comprehensions, the Python idiom for creating lists from existing sequences, filtering, transforming
• Nested lists, lists of lists, 2D data representation

Tuples:

• Tuple vs list, immutability, when to use a tuple
• Tuple packing and unpacking, assigning multiple variables in one line
• Named tuples, readable tuple access
• Tuples as dictionary keys, why this works when lists cannot be keys

Dictionaries:

• Key-value storage, the hash map under the hood
• Creating dictionaries, accessing values, updating, deleting
• Dictionary methods, keys, values, items, get, update, pop, setdefault
• Dictionary comprehensions, building dictionaries from sequences
• Nested dictionaries, dictionaries within dictionaries, real data representation
• Default dict and Counter, the collections module extensions

Sets:

• Set properties, unique elements, unordered, mutable
• Set operations, union, intersection, difference, symmetric difference
• When to use a set, membership testing, deduplication
• Frozen sets, immutable sets

Every exercise ends with the question "why this structure for this problem?" That answer is what you give in an interview when asked to justify a data structure choice.

Hands-on

• Student records manager (list of dictionaries)
• Word frequency counter (Counter)
• Contact book (dictionary, search and update)
• Data deduplication tool (set)
• Shopping cart (list with running total)
• A phonebook that handles multiple numbers per person (nested dictionary)

Assessment

• Data structure selection exercises, given six problem descriptions, choose the right structure, implement the solution, and write one sentence justifying the choice

Topics

• String immutability, why strings cannot be modified in place
• String indexing and slicing, same syntax as lists
• String methods, upper, lower, strip, lstrip, rstrip, split, join, replace, find, index, count, startswith, endswith, isalpha, isdigit, isalnum
• f-strings, Python 3.6+ string formatting, expressions inside strings, format specifiers
• String formatting, format(), % formatting (for reading legacy code), f-string equivalents
• Regular expressions, re module basics, search, match, findall, sub, the pattern matching tool
• String encoding, ASCII, Unicode, UTF-8, why encoding matters when reading files or web data
• Multiline strings, triple-quoted strings, their uses

Real-world string tasks:

• Cleaning dirty data, stripping whitespace, normalising case, removing special characters
• Parsing structured text, splitting CSV-like strings, extracting fields
• Validating input, email format, phone number format, password rules
• Text analysis, word count, character frequency, finding patterns

Hands-on

• Email validator (format check without a library)
• Phone number formatter (accept multiple formats, output standard format)
• Word frequency analyser on a real text file
• Password strength checker with specific rules
• A simple cipher (Caesar cipher encode and decode)
• A text cleaner that takes messy user input and normalises it

Topics

• Why OOP, organising related data and behaviour, the alternative to functions-only code
• Classes and objects, the blueprint and the instance
• __init__ method, the constructor, self, initialising instance variables
• Instance methods, functions that belong to a class, self as the first parameter
• Class variables vs instance variables, shared vs per-object data
• __str__ and __repr__, string representation, why they matter for debugging
• Encapsulation in Python, the convention approach (_private, __name_mangling), property decorators
• Inheritance, super(), method override, extending behaviour
• Multiple inheritance, Python's MRO (Method Resolution Order), the diamond problem
• Polymorphism, duck typing, why Python's approach differs from Java
• Special methods (dunder methods), __len__, __eq__, __add__, __iter__, making custom objects behave like built-ins
• Dataclasses, Python 3.7+ decorator for simple data-holding classes

Hands-on

• Bank account class hierarchy (Account, SavingsAccount, CurrentAccount, interest calculation, transaction history, overdraft protection)
• An inventory system using OOP (Product, Category, Inventory)
• A card game simulation (Card, Deck, Hand, using special methods throughout)
• A student grade tracker (Student, Course, Grade, full class design from a brief)

Assessment

• Design and implement a class hierarchy from a brief, similar to the OOP question in Python-focused interviews at Zoho, Freshworks, and analytics companies

Topics

• Modules, importing your own files, the import system, __name__ and __main__
• Standard library modules, datetime, math, random, os, sys, collections, itertools, the ones you will use weekly
• pip, installing packages, requirements.txt, virtual environments (venv)
• Virtual environments, why they exist, creating and activating, the discipline of per-project environments
• Key third-party packages for different roles: NumPy, Pandas (introduction), requests, schedule, pyautogui introduction, openpyxl, pdfplumber
• requests library in depth, GET, POST, headers, authentication, handling JSON responses, error handling
• Working with APIs, making real API calls, parsing the response, handling rate limits

Hands-on

• A weather CLI tool using the OpenWeatherMap API (free tier)
• A currency converter using a public exchange rate API
• An Excel report generator using openpyxl that creates a formatted spreadsheet from data
• A scheduled task runner using the schedule library
• One exercise uses a publicly available Tamil Nadu government dataset (district-level demographic or agricultural data), downloaded, read with Python, cleaned, and summarised