Course Introduction
This course is designed for placement preparation and will mainly cover data structures and
algorithms using C and C++. Even if you do not know C++, you will still be able to follow
along easily. The notes will be made available as a PDF in the description below.
Data Structures and Algorithms
Data structures are used to arrange data in main memory for efficient usage while algorithms
are a sequence of steps to solve a given problem. In this course, we will cover arrays, linked
lists, and graphs as examples of data structures and dive into solving problems using
different algorithms.
Programming Languages
C and C++ will be the primary languages used in this course but Java can also be used to
implement the algorithms. I do not recommend Python or JavaScript for beginners but rather
suggest learning C to get a solid foundation in programming.
Conclusion
Learning data structures and algorithms is a responsibility and I will teach this course in a
way that is easy to understand for beginners. Don't worry if you make mistakes or have
trouble at first, just follow along step by step and everything will become clear.
Time Complexity and Big O Notation (with notes)
CodeWithHarry
So the input size didn't increase and the runtime of the algorithms didn't increase either .No ,
it doesn't depend on the size of the input . When we ask questions like as the input will
increase, Then the runtime will change as per what? And after that Now you will go to
aunty's house You will be treated. Consider there are different routes to come and go.
Asymptotic Notations: Big O, Big Omega and Big Theta Explained (With Notes)
CodeWithHarry
We 'll talk a little bit about asymptotic notation. we talked about order. We talked about
ordering. We have primarily 3 types of asymptic notation big O, big Theta (Θ ) and big
Omega (Ω) big O is represented by capital (O), which is in our English. Big O is set to be O (
g ( n ) ) if and only if there exist a constant ( c ) and a constant n -node such that 0 ≤ f ( n) ≤
cg (n) is O (g (N) If you watch this video completely then I guarantee that you will understand
these three notations. Mathematically, mathematically this function can be anything. When
we do analysis of algorithms comparing any 2 algorithms then f ( n ) will be time and what is
n , it 's input ok , size of input. G ( n) is your function which will come inside the big O. O ( n²)
is Anything Can Be Algorithm it is g (n) that will be here and which is your algorithm. If you
guys can find any such constant ( C ) and ( n ) -node , then f ( n) is O ( g ( n)" This is the
This course is designed for placement preparation and will mainly cover data structures and
algorithms using C and C++. Even if you do not know C++, you will still be able to follow
along easily. The notes will be made available as a PDF in the description below.
Data Structures and Algorithms
Data structures are used to arrange data in main memory for efficient usage while algorithms
are a sequence of steps to solve a given problem. In this course, we will cover arrays, linked
lists, and graphs as examples of data structures and dive into solving problems using
different algorithms.
Programming Languages
C and C++ will be the primary languages used in this course but Java can also be used to
implement the algorithms. I do not recommend Python or JavaScript for beginners but rather
suggest learning C to get a solid foundation in programming.
Conclusion
Learning data structures and algorithms is a responsibility and I will teach this course in a
way that is easy to understand for beginners. Don't worry if you make mistakes or have
trouble at first, just follow along step by step and everything will become clear.
Time Complexity and Big O Notation (with notes)
CodeWithHarry
So the input size didn't increase and the runtime of the algorithms didn't increase either .No ,
it doesn't depend on the size of the input . When we ask questions like as the input will
increase, Then the runtime will change as per what? And after that Now you will go to
aunty's house You will be treated. Consider there are different routes to come and go.
Asymptotic Notations: Big O, Big Omega and Big Theta Explained (With Notes)
CodeWithHarry
We 'll talk a little bit about asymptotic notation. we talked about order. We talked about
ordering. We have primarily 3 types of asymptic notation big O, big Theta (Θ ) and big
Omega (Ω) big O is represented by capital (O), which is in our English. Big O is set to be O (
g ( n ) ) if and only if there exist a constant ( c ) and a constant n -node such that 0 ≤ f ( n) ≤
cg (n) is O (g (N) If you watch this video completely then I guarantee that you will understand
these three notations. Mathematically, mathematically this function can be anything. When
we do analysis of algorithms comparing any 2 algorithms then f ( n ) will be time and what is
n , it 's input ok , size of input. G ( n) is your function which will come inside the big O. O ( n²)
is Anything Can Be Algorithm it is g (n) that will be here and which is your algorithm. If you
guys can find any such constant ( C ) and ( n ) -node , then f ( n) is O ( g ( n)" This is the
