Data Science Bootcamp with 5 Data Science Projects

With an exponentially growing community around data science, machine learning, AI. Python is a language that opens programming access to the world. Python is considered easy to read, write and learn. Plus, it's extremely scalable. It is widely used in small, large, online, or offline projects.

This guide covers the installation part of the process and also teaches how you can run python script in your browser.

In this video lecture, we will understand what are variables in Python and how we will use them.

In this lecture, we will learn python built-in Data types and Python Type Casting

In this lecture, you will learn what is scope variable with some example?

In this video, you will learn about Python Arithmetic operators and Python Assignment Operators.

In this video, you will learn about Comparison, Logical and Bitwise Operator and you also learn how we can use this operator in Real Worlds Python project.

In this lecture, we are going to learn about Strings and string formatting, and also we will implement examples for better understanding.

In the previous lecture, we learned about the different string formatting. In this lecture, we are going to learn about the different String methods.

In this lecture, we are going to learn about taking input from the users.

In the previous videos, we learned about various strings methods as well as how we can taking input from users.

In this section, we are going to learn about a very important topic i.e Loops and Conditionals Statement.

In the previous lecture, we learned about if-elif-else statements.

In this lecture, we are going to learn about the For and While loops, and for better understanding, we implement few examples.

Previously, we learned about some conditional statements and as well as loops.

In this lecture, we are going to learn about the break and continue statements to alter the flow of a loop.

In the previous section, we learned about Loops and Conditionals and in this section, we are going to learn about how Lists and Tuples work in Python Programming.

Let’s start with the differences between Lists and Tuples.

In the previous lecture, we learned about the differences between the lists and tuples in Python.

In this lecture, we are going to learn about different operations which can be performed on the lists.

In the previous lecture, we learned about the different operations that can be performed on the lists.

In this lecture, we are going to learn about different operations that can be performed on tuples.

In the previous section, we learned about two Data Structures Lists and Tuples and in this section, we are going to learn another important Data Structures i.e Python Dictionaries.

We started our section with the previous lecture on the introduction to dictionaries and now we are going to continue, with the lecture on Operations in Dictionaries and also we learn how we can perform different dictionaries operations in python with the help of examples.

In the previous lecture, we learned about Dictionary Operations and Now we are going to learn about Nested Dictionaries.

In Python, a nested dictionary is a dictionary inside a dictionary. It's a collection of dictionaries into one single dictionary.

In our previous lecture, we learned about dictionaries in detail and In this lecture, we are going to learn about Sets which are another important data structure in Python.

In the previous lecture, we learned about Defining a set, Adding elements to a set, removing elements from the set

And this lecture, we are going to learn about the different set operations.

Previously, we learned about Sets and Dictionaries and many more in detail

In this section, we are going to learn about Stacks and Queues in Python.

In the previous lecture, we learned about the Definition of stacks and queues and the operations used for the implementation of stacks and queues. And in this lecture, we are going to learn how to implement stacks and queues using Python lists.

In the previous lecture, we learned about implementing Stacks and Queues using lists and in this lecture, we are going to learn how to implementing Stacks and Queues using Python Deque Libraries.

In the previous section, we learned about Stacks and Queues and now we are going to learn about the most interesting concept in Data structures i.e., Time Complexity in Python.

We need to learn how to compare the performance of different algorithms and choose the best one to solve a particular problem. Time Complexity is the most important factor to choose optimal algorithms in the real world.

In the previous lecture, we learned about the Time Complexity and Worst-case scenarios and in this lecture, we are going to learn about One of the most famous searching techniques i.e., Linear Search in Python and we will also learn how we can easily implement Linear Search with the help of Python

In the previous lecture, we learned that Linear Search Algorithm, that is not a very good choice for larger data structures as the time complexity is big O(n) and that’s why In this lecture we are going to learn Binary Search Algorithm in Python

Previously, we learned two different searching algorithms and now we are going to learn about the Bubble Sort Algorithm in Python.

In the previous lecture, we learned that the Bubble sort algorithm is not too good for real-world scenarios because of its high time complexity, so in this lecture, we are going to learn two more sorting algorithms i.e Insertion and Selection Sort, and check if they are suitable to use in the real-world scenarios.

In the previous lecture, we learned that the Insertion and Selection sort using Python which is not too good for real-world scenarios, so in this lecture, we are going to learn about Merge Sort and check if the time complexity of merge sort is better than other sorts or not.

In this lecture, we are going to learn about functions in Python. A function is a block of code that only runs when it is called.

In the previous lecture, we learned how to define a function and calling a function and in this lecture, we are going to learn about default parameters.

In the previous video, we learned about default parameters in functions, and in this lecture, we are going to learn about positional arguments.

Positional arguments are arguments that need to be included in the proper position or order.

In the previous lecture, we learned about positional arguments and in this lecture, we are going to learn about keyword arguments.

In the previous lectures, we learned about various arguments and in this lecture, we are going to learn how to create and import modules in Python.

In the previous section, we learned about functions and in this section, we are going to learn about Lamda functions,

In the previous lecture, we learned about the lambda functions and in this lecture, we are going to learn about the python built-in functions i.e., filter(), map() and zip() functions.

In the previous lecture, we learned about built-in functions which are filter() function, map() function, zip() function

And in this lecture, we are going to learn about the list, set, and dictionary comprehensions.

In the previous section, we learned about Lambda functions, Comprehensions, and various python built-in functions.

In this section, we are going to learn about python in-built functions.

Let’s start this lecture with Aggregate functions.

In the previous lecture, we learned about aggregate functions and in this lecture, we are going to learn about the Analytical functions.

We are going to learn about four amazing analytical functions in python which are eval(), len(), factorial(),sort().

Previously, we learned about the aggregate and analytical functions in Python, and in this lecture, we are going to learn about Recursion using factorial and Fibonacci functions.

In the previous lecture, we learned how to solve the factorial problem using Recursion in Python, and in this lecture, we are going to learn to solve the Fibonacci problem using Recursion in Python.

In the previous section, we learned about Recursions and computed factorial and Fibonacci numbers using recursion.

In this section, we are going to learn about Python Object-oriented programming(OOP) concepts which are Classes, and Objects, and also their properties.

In the previous lecture, we learned about object-oriented concepts which are classes and objects and in this lecture, we are going to learn about other object-oriented concepts i.e inheritance in Python.

In the previous lecture, we learned about Inheriting properties and functions from one class into another class and in this lecture, we are going to learn about Data Encapsulation.

In the previous lecture, we learned about hiding important details from unwanted users i.e Data Encapsulation in Python, and in this lecture, we are going to learn about Polymorphism.