Python for Beginners: Complete Python Programming

Install Python using the Anaconda distribution on Windows, macOS, and Linux, and set up Spyder IDE and Jupyter Notebook to manage packages, update environments, and explore data science workflows.

Learn how variables and identifiers work in Python, including dynamic typing and how assigning values changes a variable's type. Explore valid and invalid identifiers, case sensitivity, and reserved keywords.

Explore Python operators, operands, and operator precedence, covering mathematical, relational, logical, membership, and identity operators with practical examples and precedence rules.

Explore conditional statements in Python, including simple, chain, and nested forms, using boolean expressions like temperature comparisons to decide outcomes.

Learn to read user input with input(), convert strings to int or float, and use conditionals to respond if the number is greater than, equal to, or below 50.

Explore Python iteration with while and for loops, master indentation defining code blocks, and implement a countdown function using range to avoid infinite loops.

Master how to define and call functions in Python, including naming, parameters, and return values, with examples like calculating circle area from a radius using pi (22/7).

Explore Python lists by creating lists with square brackets, indexing and slicing elements, including negative indices and nested lists, and performing list operations like concatenation and repetition.

Explore Python list methods, including insert, append, extend, remove, pop, index, count, sort, and reverse; understand in-place sorting, ascending and descending orders, and how to use these operations on lists.

Explore tuples in Python: create immutable sequences with comma or parentheses, access by index or slicing, concatenate and repeat, zip combines multiple sequences into tuples, and build dictionaries from tuples.

Explore Python strings, including literals with single, double, and triple quotes; practice indexing, slicing, immutability, concatenation, and repetition, and apply string methods like capitalize, find, replace, split, join, and strip.

Explore sets in Python as unordered, distinct, hashable collections where duplicates vanish and only immutable elements like numbers and strings can be included, with union, intersection, add, and remove operations.

Explain how to create Python dictionaries (literals and dict()), how keys map to values, and how to add, update, delete, and iterate over keys and values.

Learn how to open, read, write, and close files in Python, using modes like r, w, and a, reading lines and writing text to ensure data is saved.

Explore syntax and semantic errors, learn how exceptions like index, key, type, and name errors occur, and practice identifying overflow and indentation issues in Python.

Explore how namespaces and scope rules organize names in Python, detailing global and local namespaces, enclosing functions, and built-in scope, plus their lifetimes during execution.

Learn how to use modules in Python by importing entire modules, selecting specific objects with from module import, and renaming modules with as to manage namespaces.

Learn NumPy, the fundamental package for scientific computation, focusing on the array as the main homogeneous object, exploring shape, size, and dtype, plus element-wise operations, broadcasting, and axis-based sums.

Learn to generate random numbers with NumPy, using uniform and standard normal distributions, and master indexing, slicing, reshaping, and stacking arrays.

Review the fundamentals of Python for beginners, including input, file read/write, errors and exceptions, namespaces, modules, numpy, and future data science modules like pandas, Matplotlib, SciPy, sklearn, and statsmodels.