Python Bootcamp 2025-2026: 100 coding lectures

Explore Python variables and dynamic typing, learn assign and user-defined variables, apply typecasting, inputs, and basic global versus local scope in functions.

Learn Python variable rules: variables are case sensitive, may include alphanumeric characters and underscores, must not start with a number, include spaces or special symbols, and avoid keywords.

Learn how to use the Python print statement, the built-in output function, including passing arguments and variables, with formatting and separators in Jupyter notebooks.

Learn arithmetic operators in Python, including addition, subtraction, multiplication, division, exponentiation, modulus, and floor division, with practical examples in a Jupyter notebook.

Explore Python comparison operators, including greater than, less than, and equal checks, distinguish between assignment and equality, and practice booleans in the Jupyter notebook to build conditional logic.

Convert Fahrenheit to centigrade using the standard formula, read user input, print the centigrade result, and apply if-else logic to suggest a doctor for temperatures above 37 or rest otherwise.

Learn assignment operators, including pre- and post-increment and decrement, and compound forms like plus-equal and multiply-equal, with practical examples.

Explore a real-life bank balance update using assignment operators to handle deposit and withdrawal from an initial 500 USD, with a conditional check for sufficient funds and an updated balance.

Master Python logical operators and, or, and not through practical login scenarios, learning how combined conditions control program flow and decision making.

Demonstrates membership operators in and not in to check element presence in sequences, and uses is and is not to test object identity with concrete examples.

Discover Python's operator precedence and how parentheses control evaluation. Learn the left-to-right order for division, multiplication, addition, and subtraction with practical examples.

Explore Python data types and data structures, from integers, floats, strings, and booleans to lists, tuples, sets, and dictionaries, with numpy for arrays.

Explore Python strings, covering properties, indexing and slicing, built-in functions like len, max, min, dot methods, and string definitions with single, double, or triple quotes, noting immutability and duplicates.

Learn string concatenation and repetition in Python, using single or double quotes, spaces, and prefixes. Understand converting numbers to strings for mixed concatenation and building full names.

Learn indexing and slicing in Python by accessing elements with position numbers and slicing with start:end:step on data like strings. Use forward and reverse indexing, negative indices, to extract data.

Develop a Python program that takes a user-input string, reverses it with slicing [::-1], and compares it to the original to decide if it is a palindrome, printing the result.

Explore Python inbuilt string functions like len, max, min, and ord, with syntax and examples. See how ASCII order governs character comparisons, including spaces, numbers, and capitalization.

Explore python string methods through the string class and dot notation. Learn key methods such as capitalize, upper, lower, title, and swap case with practical examples.

Write a program that converts the search input to lowercase to enable caseless comparison, matching queries to stored python course names in a database and reporting when found.

Explore Python's inbuilt string methods, including zfill for left padding with zeros, center, rjust, and ljust for alignment, and the count method to tally occurrences.

Explore string methods such as index, replace, and split to locate first occurrences, replace Python with Java, and split text into word tokens based on patterns.

Explore essential Python string methods, including startswith, endswith, isupper, islower, and isalpha, with boolean checks and practical examples.

Understand why strings are immutable in Python and how methods like upper or lower return new values without changing the original. Learn to redefine or store changes in a variable.

Explore non-primitive data types in Python, including lists, tuples, sets, and dictionaries. Learn how lists are mutable and ordered, support duplicates, and use indexing, slicing, and concatenation.

Compare Python lists and NumPy arrays, highlighting differences in data types and dimensionality. Learn how lists hold multiple types and arrays enforce a single type for multi-dimensional computations.

Explore indexing and slicing in Python lists through forward and reverse access using zero and negative indices. Modify list elements without loops with practical examples in a Jupyter notebook.

Explore list and string operations in python by printing course names, capitalizing the first letters of words, replacing artificial intelligence with AI, and squaring the number 30.

Apply python inbuilt functions on lists, including len, max, min, sum, compute the average as sum divided by length, and note that mixing integers and strings can trigger an error.

Learn how Python lists use inbuilt methods like append, extend, and insert to modify ordered, mutable lists, including end-of-list behavior with practical car examples.

Explore Python list methods, including pop, remove, and clear, to delete elements by index or value. Learn how list ordering and first occurrence affect removals and practice with examples.

Explore Python list methods by copying elements into a new list, counting occurrences, finding index positions, and sorting or reversing the list to see ascending and descending orders.

Explore Python tuples, their immutability and ordered nature, and learn indexing, slicing, concatenation, and repetition, plus count and index methods for element lookup.

Explore packing and unpacking tuples in Python, including assigning multiple values in one line, discarding with underscores, using * for variable-length unpacking, and nested unpacking.

Tuples are immutable; convert to a list to append elements, then reconvert to a tuple to modify data while preserving the immutability concept.

Compare lists and tuples in Python: lists are mutable, tuples immutable; both support indexing, slicing, and concatenation, so use lists for changing data and tuples for fixed data.

Explore Python sets that store unique objects, disallow duplicates, and remain unordered, enabling add or remove operations and union-based concatenation.

Explore the unique, unordered nature of sets by removing duplicates and handling case differences; learn to compute max, min, sum, and average from the set's unique values.

Explore set methods in Python, learn how to add single elements with add, update multiple elements with update, and understand that sets are unordered and enforce uniqueness.

Explore set methods in Python: discard vs remove, including when to use each to avoid errors, and learn how clear and pop affect the set.

Learn to create unique email ids in python using sets and the add method, check before adding to avoid duplicates, and show if an email id exists or is created.

Explore set operations to compare document contents, find intersections and differences between set one and set two, and observe common points, unique elements, and symmetric differences.

Compare three text documents using Python sets to identify common and unique words after lowercasing and tokenizing; compute intersections and differences across Python, Java, and C documents.

Remove duplicates from a list by converting to a set, then reconverting, noting that sets are unordered; or preserve order with a for loop that appends unseen items.

Plot Venn diagrams to visualize common and unique elements across datasets using Python libraries like matplotlib, seaborn, and plotly, with practical steps for installation, labels, and changing colors.

Explore Python dictionaries by defining key-value pairs with curly braces, learn that keys must be unique while values can be any type, and index by keys since dictionaries are mutable.

Explore how a duplicate key behaves in a Python dictionary: the latest value overwrites the existing key, updating the dictionary to show NLP for key 11 0 1.

Learn how to merge dictionaries with the double asterisk syntax, handle duplicate keys by the latest dictionary, and access elements via key indexing.

Learn how to add elements to a Python dictionary using key and value pairs with square bracket syntax, and update car entries like engine sizes.

Explore how Python in-built functions work with dictionaries, emphasizing key uniqueness, value duplication, and how max, min, and sum relate to dictionary data, including mixed type errors.

Explore the built-in dictionary methods in Python, including pop, pop item, clear, and copy, with practical examples.

Explain the difference between the dict get method and indexing by key, showing how get returns none for missing keys while indexing raises an error, and when to use each.

Explore dictionary methods in Python by printing keys, values, and items, and use fromkeys to assign a common value to multiple keys, illustrated with student Python course examples.

Explore nested dictionaries in Python by building a bank customer dataset, accessing and updating nested values, and printing specific details like age and salary.

Map dictionary data to a multi-dimensional data frame using Python and pandas, then export the results to Excel with index set to false.

Master Python conditional logic by learning if statements, else and elif, using boolean expressions, and applying indentation rules for nested conditions to shape program logic.

Learn to write an if statement in Python, using DateTime to get the current year, compute age from year of birth, and accept or reject registration accordingly.

Apply if-else logic to validate a password against guidelines: length at least eight, starts with p, second letter uppercase, and ends with a digit, accepting valid input.

Implement if-elif-else logic to compute a car's average speed from distance and time. Enforce warnings and penalties across defined speed ranges, including fines and license cancellation.

Explore nested conditions in a bank loan application, using age, salary, credit score, and optional co-applicant checks with proper indentation and current year handling.

Learn how to use python loops to iterate over elements, including for loops, while loops, and nested loops, with both conditional and unconditional looping concepts.

Learn how to write Python for loops to iterate over strings, using an iterator, colon and indentation, printing elements and applying string methods or conditions to selectively process items.

Practice for loops to iterate a string and identify vowels, printing whether each character is a vowel or not.

Explore for loops over lists and tuples, printing and transforming elements: uppercase strings and multiply numbers by 100, with preview of separating even and odd numbers in the next video.

Learn to separate even and odd numbers in a Python list by using modulus, for loops, and list append to two distinct lists.

Explore how for loops work with sets, noting unordered order and removal of duplicates, then convert even numbers to squares and odd numbers to cubes using two sets.

Learn to iterate dictionary items with for loops, extract keys and values using dict.items, and print keys, values, or both while applying filters, such as values starting with s.

Swap dictionary keys with values by iterating over dictionary.items(), creating a new empty dictionary, and printing the final swapped dictionary.

Classify bank customers by credit score into valued, good, and moderate groups using Python dictionaries and a for loop.

Explore while loops in Python, contrasting with for loops, showing how the condition is checked before each iteration and how statements execute while the condition remains true, repeating until false.

Explore while loops that run indefinitely when the condition stays true, printing each iteration. Learn to limit iterations by turning the condition false and interrupting the kernel if needed.

Explore how to implement a while loop to block a credit card after three wrong pin entries, track attempts, and reset on success.

Explain a while loop login system that prompts for username and password, validates credentials, reports success or failure, and repeats until the user chooses to re login or exit.

Print a multiplication table using a while loop, taking user input for the table number and using an initializer that increments until ten.

Learn how to implement nested loops in Python by combining colors and items to produce red apple, red tomato, green apple, and green tomato, using for and while constructs.

Master nested loops by printing tables from 1 to 10 using a while loop with an inner for loop and range, including adjustable start and end table values.

Define a customer data set in Python and build a procedural banking simulator with user ID, password, and mobile OTP authentication. Display demographics and transactions, and support deposits and withdrawals.

Define customer data structures with IDs, passwords, mobile numbers, plus demographic and transactional data, then import random as R, DateTime as dt, and time for login timing and OTP.

Authenticate users by validating a case-insensitive user id, verifying passwords, generating a random six-digit otp for login, then load and display customer details.

Develop and validate a Python procedural banking demo that updates customer details, including age, manages savings deposits and withdrawals, handles loan deposits, and prints updated balances with login timestamps.

Define test data and libraries, then run validation to catch bugs while developing. Simulate a banking workflow with user authentication, otp verification, account access, and balance updates.

Explore Python functions across procedural, functional, and object oriented paradigms, including inbuilt, user defined, anonymous, and recursive functions, plus dunder methods and constructors.

Explore Python built-in functions like abs, all, and enumerate; learn standard syntax with parentheses and arguments, understand default arguments, and how enumerate can start indexing from a custom value.

Explore Python’s inbuilt range, type, sum, round, and divmod functions. Learn to generate sequences with start, stop, and step, compute sums and remainders, and inspect types.

Explore how the isinstance function checks types and directs operations on lists, sets, and tuples, including append and add methods and immutability.

Map multiple iterables into a zipped structure with the zip function and unzip it back into separate lists using the asterisk to unpack names, ages, and weights.

Create and call user defined functions in Python using def, specify arguments, and return statements, while recognizing inbuilt functions and the standard function syntax.

Compare the print and return statements in Python functions, showing how print outputs to the console while return passes a value that can be stored or reused in subsequent code.

Explore bounded and unbounded arguments in Python functions, showing how bounded arguments must follow code logic and types, while unbounded ones remain independent of calculations.

Develop a custom function to compute list length, sum, maximum, and minimum without built-in functions, practicing iteration and aggregation logic.

Explore the four main types of Python functions by argument: required and positional, keyword, default, and variable length argument. Learn how combinations and call rules affect values and execution.

Explore how to define a function with required and positional arguments, compute taxable income and tax amounts, and understand argument order for accurate results.

Learn how keyword arguments let you pass named parameters in any order, providing values like name, age, city, postal code, and account type to a bank account function.

Explore default arguments in Python by defining functions with a default discount and message, then override them at call time to tailor outcomes.

Explore variable length arguments in Python, learning why they are needed beyond default and keyword arguments. Define a function that greets any number of people and prints greetings.

Learn to implement a Python factorial function using an iterative loop, range, and a starting initializer, validating with five and ten factorials.

Create a Python function to generate the fibonacci sequence up to a given number by iterating with a and b starting at 0 and 1, appending values to a list.

Explore Python lambda functions as anonymous and unnamed, short-range helpers with flexible arguments, learn their standard syntax, and apply simple expressions like uppercasing or numeric power.

Explore how modules, libraries, and packages in Python enable code reuse, import across projects, and organize inbuilt, user-defined, and third-party components using pip.

Master the Python os and math modules: learn to get current working directory, create and change directories, list files, and perform pi, sqrt, power, trig, log, and factorial operations.

Explore the Python random module: import it, inspect its methods, generate floats and integers, use the choice method, create one-time passwords, pick random items from a list, and shuffle data.

Explore the Python date and time module, import datetime as dt, print current date and time, and manipulate dates with timedelta for future or past calculations.

Learn to use Python's built‑in calendar module to print monthly and yearly calendars, inspect leap years, and determine weekdays for specific dates.

Discover how to create a user defined Python module in a Jupyter notebook by defining a file with variables and functions, converting to .py, and importing and exporting its features.

Learn to create and import a .py module in a Python project, run it in a Jupyter notebook, and explore aggregation and tax calculation functions plus offer price logic.

Learn to install and use Python's third-party libraries with pip, leveraging packages for web frameworks, data visualization, machine learning, and NLP to accelerate development.

Explore a third-party module such as pywhatkit for automating tasks with Python, including sending scheduled WhatsApp messages, emails, Google searches, playing YouTube videos, taking screenshots, and more.