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
comparison itself. In one comparison only.
Before Solving Some Questions of Time Complexity I will tell you some tricks to get rid of
time complexity. After that we will do some set of questions. which will make you a very good
grasp in such questions. due to the time complexity of any algorithm when you have to find it
so what is the first step that you do and at the same time how to approach this problem. In
this way, whatever instructions are going on here , it is taking almost ( k ) time. We believe
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
comparison itself. In one comparison only.
Before Solving Some Questions of Time Complexity I will tell you some tricks to get rid of
time complexity. After that we will do some set of questions. which will make you a very good
grasp in such questions. due to the time complexity of any algorithm when you have to find it
so what is the first step that you do and at the same time how to approach this problem. In
this way, whatever instructions are going on here , it is taking almost ( k ) time. We believe
