
Learn how programming languages enable humans to communicate with computers, with a focus on Python and its libraries for data science.
Discover Python programming as a high-level, interpreted language known for simplicity and readability, with cross-platform support, English-like syntax, and essential libraries for data science, machine learning, and data analysis.
Explore what a Python IDE is and how to use Google Colab for data science, including code and text cells, and the automatic saving to Google Drive with easy sharing.
Learn to write your first Python program in Google Colab using text and code cells, print statements, hello world, basic addition, and string literals in quotes, then run with ctrl-enter.
Explore how comments in Python document code, using single line, inline, and multi-line styles; learn to use hash symbols and triple quotes to improve readability and explainability.
Understand how Python variables store and reuse data with an assignment operator and print, then follow naming rules—no starting with numbers, no spaces or special characters, and use underscores.
Explore Python's basic data types—integers, floats, and complex numbers, plus booleans and strings—learn how to declare them, use print, and inspect types with type().
Explore Python data structures: lists, tuples, sets, and dictionaries, and learn how they store multiple values, how to create them, and how to check their types.
Explore implicit and explicit Python type casting, learn how Python auto-converts int, float, and bool, and use built-in functions to convert string, list, tuple, and set, including nested forms.
Explore how to collect user input in Python with the input function, convert to int, float, or bool, and use evaluate for lists, tuples, sets, or dicts.
Explore Python string indexing and slicing by applying positive and negative indices, end index plus one rule, and step values to fetch characters, slices, and reverse sequences.
Explore list indexing and slicing in Python, using positive and negative indexes to fetch single or multiple elements, apply steps, and reverse a list.
learn to index and slice tuples in Python and fetch single or multiple elements using positive and negative indexes, including reversing tuples and understanding end and step rules.
Explore creating a dictionary and retrieving values by keys using indexing, slicing, and the get method. Practice accessing skills and languages and specific items by index.
Explore Python arithmetic operators, including addition, subtraction, multiplication, division, exponential, flow division, and modulus, with practical examples on numbers, lists, tuples, and strings.
Master Python assignment operators, including plus equals, minus equals, and multiply equals, to update variables like salary with increments, decrements, and 5% hike.
Explore Python comparison operators, including equal, not equal, greater than, less than, and greater than or equal to and less than or equal to with true or false results.
Explore how logical operators and, or, and not work in Python to compare multiple conditions, with truth tables and practical examples.
Explore Python membership operators in and not in to test whether a value appears in sequences such as lists, tuples, sets, dictionaries, or strings.
Explore identity operators in Python, using is and is not to check whether two variables share the same values. See examples with numbers, lists, and SQL and Java.
Learn how Python functions are standalone tools that take input and return output, while methods are tied to specific objects via dot notation, such as string.upper and string.split.
Create lists in Python with brackets or the list function; lists are ordered, mutable, and allow duplicates, and use len, max, min, sum, range, and enumerate.
Explore list methods in Python, including append, insert, extend, remove, pop, clear, delete, reverse, sort, copy, count, and index, highlighting mutability and practical examples.
Explore creating tuples with parentheses or the tuple() function in Python, accessing via indexing and slicing, and using functions like length, max, min, sum, range, and enumerate, noting immutable nature.
Master python set basics: create sets with curly braces or set(), note they are unordered and store unique values, with immutable elements; use add, discard, remove, pop, clear, union, intersection.
Explore Python dictionaries, learn how to create and access keys and values, and master methods such as keys, values, items, get, update, setdefault, pop, popitem, clear, and delete.
Explore Python string handling through essential functions and methods. Learn to print, measure length, check type, and apply upper, lower, title, split, join, strip, and format.
Explore how simple and compound statements form the foundation of Python control flow, and learn how if, else, elif, and nested if enable decision making.
Explore Python's if statement, its syntax with a colon and indentation, and how to evaluate conditions to print outcomes or make decisions based on true or false.
Master python's if-else statements with colon syntax and an else block to decide true vs false, using voting eligibility, positive/negative, even/odd, palindrome, divisibility, and pass/fail.
Master the elif concept to handle multiple conditions with if, elif, and else in Python. Learn practical examples from checking numbers, divisibility, and basic calculators to solidify conditional logic.
Learn how to create a nested if statement with outer and inner if. See examples that test positive, negative, or zero, and even or odd, with elif and else.
Master Python for loops and iterators, using range and iterable types like lists, tuples, sets, dictionaries, and strings. Apply examples such as printing values, squaring numbers, and filtering even numbers.
Explore practical for loop problems in Python, from summing sequences and products to factorials, character counts, and data-type conversions used in data science tasks.
Learn to solve Python pattern problems with for loops, printing square, right-angle, inverted, pyramid, and diamond patterns using string multiplication and careful spacing.
Explore nested for loops by iterating a nested list to print elements, sum values, and compute discounted prices, while finding maximum and minimum values and counting odd/even numbers.
Master break and continue in Python loops to control iteration. Use break to stop on conditions, continue to skip elements, with prime and palindrome examples.
Master while loops by learning to initialize a counter, set a stopping condition, and increment each step, contrasting them with for loops and examples like printing hello five times.
Define and use custom functions with the def keyword, parameters, and return statements, and compare them with built-in Python functions while learning defaults and docstrings.
Compare print and return in Python, showing how return sends a value back to the caller for storage and reuse, while print merely displays output.
Explain local and global variables in Python, showing how locals stay inside a function while globals are accessible outside, and to convert a local to global with the global keyword.
Explore how to write flexible Python functions with *args for positional arguments and **kwargs for keyword arguments. Learn to sum any number of values and pass key-value pairs using items.
In this lecture, explore custom function practice problems in Python, including summing list elements, finding common elements between lists, removing duplicates, and generating factorial and Fibonacci values.
Explains lambda functions as small anonymous one-line expressions in Python, with syntax and examples for squaring numbers, adding numbers, checking positivity, even/odd, and palindromes, plus limits.
Explore how the map function applies a function to every element of an iterable, using lambda for in-line logic and examples like squaring numbers, uppercasing strings, and even/odd classification.
Explore how the Python filter function selects iterable elements with a predicate, returning a filter object that can become a list, with examples like even numbers and palindromes.
Explore the reduce function from a module to cumulatively apply a function to an iterable, yielding a single value, with examples for sums, products, and joining words.
Explore Python comprehensions to build lists, sets, and dictionaries from iterables with single-line expressions, for loops, and optional if conditions; learn examples like filtering values over 500 and computing squares.
Explore object oriented programming as a system built around real world objects to create organized, reusable, and maintainable code through encapsulation, inheritance, polymorphism, and abstraction.
Explore the four core oops components in Python: class as a blueprint, object as an instance, variables as properties, and methods as behavior.
Create class and define object shows building a house class with design, floors, and rooms, objects, and accessing attributes; extend to a calculator class with add, subtract, multiply, and divide.
Learn how class variables stay constant across all objects, while instance variables vary per object, initialized by the init constructor using self, with circle and employee examples.
Encapsulation wraps data and methods into a single unit and restricts direct access to class components, as shown by an ATM that hides balance with private data and controlled methods.
Explore polymorphism in Python by using the same method name across rectangle and triangle classes to compute areas, and see how the plus operator adapts for numbers and strings.
discover how Python inheritance lets a child class inherit properties and methods from one or more parents, covering single, multiple, multi-level, and hierarchical inheritance with examples.
Learn how abstraction hides background details to focus on what a thing does, using abstract base classes and abstract methods to enforce implementation in concrete calculators like addition and multiplication.
Welcome to Complete Python Bootcamp for Data Science in 2026 , the most beginner-friendly and practical course designed to help you master Python and kickstart your journey into the world of Data Science.
Whether you are a student, working professional, beginner, or someone switching careers, this course will take you from zero to advanced with real-world examples, clear explanations, and hands-on coding exercises.
In this course, you will start by learning Python fundamentals such as variables, data types, conditional statements, loops, functions, and file handling. Once you master the basics, you will dive into essential Data Science libraries like:
NumPy: Numerical computing, arrays, broadcasting
Pandas: Data cleaning, manipulation, grouping, merging, missing values
Matplotlib & Seaborn: Stunning data visualizations, charts, plots, analytics
Exploratory Data Analysis (EDA): Real datasets, trends, correlations, insights
You’ll also work on mini projects and guided examples to understand how Python is used in real Data Science workflows.
By the end of this course, you will be able to confidently analyze datasets, create visualizations, clean messy data, and prepare yourself for advanced topics like Machine Learning.
This is the perfect course if you want to become a Data Analyst, Data Scientist, or Python Developer.
No prior programming experience needed you will learn everything step-by-step.
Enroll today and start your journey toward a successful Data Science career!