
In this section you will understand what is python and importance of python programming in Data Science.
Explore why Python offers concise, high-level, dynamically typed, object-oriented programming with extensive libraries, interpreter execution, and platform independence, while highlighting its suitability for data science and mobile limitations.
Install and run Python across platforms, compare 2.7 and 3.x, and explore Colab, Jupyter notebooks, and Anaconda with Spyder for data science work.
Learn Python identifiers: naming rules, case sensitivity, allowed characters, underscores for private and magic methods, reserved keywords, and bases like decimal, binary, octal, and hexadecimal.
Explore how Python's input() reads user data as strings across versions, and how typecasting with int, float, complex, and bool converts values for arithmetic operations.
Explore Python operators, from arithmetic to relational and logical, including strings, concatenation, and special operators, with practical examples and common pitfalls.
Explore Python operators from bitwise and assignment to ternary, identity, and membership, with examples of l-values and r-values, comparisons, and applications in data science, databases, and memory.
this lecture explains python control statements, focusing on conditional statements, transfer statements, and iterators; it highlights indentation, relational operators, and practical use with input, if-else, loops, and atm-like examples.
Learn to implement control statements in Python using if-else and nested conditions, with examples of PIN and password validation, string comparisons, and input-driven branching.
Examine while loops in python, covering initialization, conditions, increments, and pitfalls like infinite loops and indentation, with modulus-based checks for even numbers and sequence sums.
This lecture covers Python control statements, including if-elif and exception handling, and dives into nested for loops to create star patterns and number patterns with range-based iteration.
Learn how to slice strings in Python using positive and negative indices, with step values, and understand when slicing is possible and how the default step works.
Learn to locate and count substrings in Python strings using find, index, and count, handle missing results with exceptions, and perform forward and reverse searches within a main string.
Learn Python list data type basics and key inbuilt functions, including len, count, index, membership, and core operations: append, extend, insert, remove, and pop.
Learn how to order list elements using sorting and reversing, with ascending and descending options and ASCII-based character ordering. Explore list operations like extend, repetition, delete, clear, and memory management.
Explore set operations such as union, intersection, difference, and symmetric difference, apply membership tests, and convert lists to sets to remove duplicates, noting that order is not preserved.
This course is completed designed for beginners who have never programmed before, as well as existing programmers who want to increase their career options by learning Python coding completely from beginners to expert level.
The fact is, Python is one of the most popular programming languages in the world – Huge companies like Google use it in mission critical applications like Google Search Console, Facebook friend recommendation, Search Engine Optimization and Networking applications .
Python is the number one language choice for machine learning, data science and artificial intelligence. To get those high paying jobs you need an expert knowledge of Python, and that’s what you will get from this course.