All Chapters and Videos
This page lists every lesson of the course with its video. Click a thumbnail to open the lesson at its video, or open the notes to read the explanation, practise the 15 questions and take the MCQ test. There are 27 video lessons in 3 sections.
Python Programming (12 lessons)
Prerequisite : Programming Basics
Programming basics before Python: what a program is, algorithms, flowcharts, pseudocode, compilers vs interpreters and types of errors. Notes, Q/A and MCQ.
Google Colab and Python Basics
Start Python with Google Colab: notebooks, code and text cells, print(), comments, indentation, keywords and identifiers. Notes, examples, Q/A and MCQ.
Variables and Data Types
Python variables and data types: int, float, complex, str, bool, None, list, tuple, set and dict, type(), naming rules and mutable vs immutable objects.
Basic Operators
Python operators explained: arithmetic, comparison, logical, assignment, bitwise, membership and identity operators, with precedence rules and examples.
Data Reading (Input) and Type Casting
Read user input in Python with input(), convert types with int(), float(), str(), bool(), handle conversion errors and format output with f-strings.
Decision Making
Python decision making: if, if-else, if-elif-else, nested if, conditional (ternary) expressions, truthy and falsy values and the match statement.
Dictionary
Python dictionaries: key-value pairs, accessing with [] and get(), adding, updating and deleting items, keys(), values(), items(), looping and comprehensions.
File System and Google Drive
Python file handling: open(), file modes r, w, a, x, r+, w+, a+, reading and writing files, the with statement, and using Google Drive in Colab.
NumPy (11 lessons)
Array Basics
NumPy array basics: why NumPy, creating 0-D, 1-D, 2-D and 3-D arrays, ndim, shape, size, dtype, and helper functions zeros, ones, arange and linspace.
Array Indexing
NumPy array indexing: accessing elements of 1-D, 2-D and 3-D arrays, negative indexing, fancy (integer array) indexing and boolean indexing.
Array Slicing
NumPy array slicing with start:stop:step, negative steps, slicing rows and columns of 2-D arrays, and why slices are views of the original array.
Array Copy vs View
NumPy copy vs view: how copy() creates independent data, how views share memory, the base attribute, and which operations return views or copies.
Array Shape and Reshape
NumPy shape and reshape: the shape attribute, reshape() rules, the -1 unknown dimension, flattening with ravel() and flatten(), and transpose.
Array Iterating
Iterating NumPy arrays: for loops over 1-D, 2-D and 3-D arrays, np.nditer(), np.ndenumerate(), and why vectorised operations are preferred.
Array Join
Joining NumPy arrays with concatenate(), stack(), hstack(), vstack() and dstack(), the axis argument, and shape rules for joining arrays.
Array Split
Splitting NumPy arrays with array_split() and split(), uneven splits, splitting 2-D arrays along axes, and hsplit(), vsplit() and dsplit().
Array Search
Searching NumPy arrays with where(), searchsorted(), argmax(), argmin(), nonzero() and conditions, with clear examples and outputs.
Array Sort
Sorting NumPy arrays with np.sort() and ndarray.sort(), sorting strings and booleans, 2-D sorting by axis, descending order and argsort().
Array Filter
Filtering NumPy arrays with boolean index lists and conditions, combining conditions with & | ~, np.where() replacement and counting matches.
Pandas (4 lessons)
Pandas Basics
Pandas basics: what pandas is, installing and importing it, Series and DataFrame data structures, and common uses in data analysis.
Pandas Series
Pandas Series: creating from lists and dictionaries, custom index labels, loc and iloc access, vectorised operations, filtering and useful methods.
Pandas DataFrame
Pandas DataFrame: creating from dictionaries, selecting columns and rows with loc and iloc, filtering, adding and dropping columns, and sorting.
CSV Files and DataFrames
Read and write CSV files with pandas: read_csv(), head(), tail(), info(), describe(), handling missing values, and saving with to_csv().