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.
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.
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 mathematical definition of big
O. If you ca n't find it then its is not f (n ) is O. This question is its own
truth , it has validity , it will remain valid.
To define an algorithm, To define the events in the life of an algorithm , We
have , Best Case Worst Case and Expected Case. And along with that, I 've packed
one more thing into this video : The definition of Log. If you watch this video
till the end , Then you will find out what this 'Log ' really is. 1. . . 5. . .
7. . . 9 and 24 are the numbers in it; They 're in ascending order , You can see
for yourself. If you know even a bit of maths, You 'll know that it is in an
ascending order. Now what I say is that I 'll give a number : 'A' And I 'd like you
to tell me If this number exists within the array , or not. Suppose the value of A
is 8. So what will be your answer ? Yes. Meaning 1. If A is. . . Sorry, your answer
will be no , because it is n't there. If the value is 9, What will the answer be ?
Your answer will Algo 1 is a simple person. It does n't have much of a brain. It is
comparing it with all the numbers. Is this the best way to do this work ? Obviously
not. Because Algo. 1 is lucky , He will get A=1. It will tell us in the first
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.
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.
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 mathematical definition of big
O. If you ca n't find it then its is not f (n ) is O. This question is its own
truth , it has validity , it will remain valid.
To define an algorithm, To define the events in the life of an algorithm , We
have , Best Case Worst Case and Expected Case. And along with that, I 've packed
one more thing into this video : The definition of Log. If you watch this video
till the end , Then you will find out what this 'Log ' really is. 1. . . 5. . .
7. . . 9 and 24 are the numbers in it; They 're in ascending order , You can see
for yourself. If you know even a bit of maths, You 'll know that it is in an
ascending order. Now what I say is that I 'll give a number : 'A' And I 'd like you
to tell me If this number exists within the array , or not. Suppose the value of A
is 8. So what will be your answer ? Yes. Meaning 1. If A is. . . Sorry, your answer
will be no , because it is n't there. If the value is 9, What will the answer be ?
Your answer will Algo 1 is a simple person. It does n't have much of a brain. It is
comparing it with all the numbers. Is this the best way to do this work ? Obviously
not. Because Algo. 1 is lucky , He will get A=1. It will tell us in the first
