Algorithms and Data Structures Tutorial
We will learn these concepts by using well-known algorithms in this course we will also be writing code.
This course is less about specific algorithms and more about the tools you will need to evaluate
algorithms. An algorithm is a set of steps or instructions for completing a task. The field of computer
science has identified several that do the job well for a given task. Understanding algorithms is not just
knowing that an algorithm exists but understanding when to apply it requires properly understanding the
problem at hand. Learning about algorithms gives you a deeper understanding about complexity and
efficiency in programming. The course will focus on some of the tools and concepts you'll need to be
aware of before we can dive into the topic of algorithms if you're ready we 're going to try and cultivate
together as we work through our topic
John and britney took the same amount of turns to find the answer to the number they were looking
for. When the answer was three they both took the same number of turns this is important when the
number was larger but not much larger 10 in this case we start to see that britney strategy did better she
took four tries while john took 10. John and brittany took turns to find the answer when the number was
5. When the answer was 100 it took him 20 times the amount of tries to get that answer compared to
britney. The speed at which the result was obtained differed between john and brittany and britney in the
game. The two strategies britney and john used were examples of search more specifically these are
search algorithms. The strategy john took where he started at the beginning of the range and just counted
one number after the other is a type of search called linear search. linear search is a search algorithm and
we can use it in the real world for example i could tell you to walk into a bookstore and find me a
particular book using the linear search algorithm.
An algorithm definition must contain a specific set of instructions in a particular order. Each step must
not be a complex one and needs to be explicitly clear. The last guideline is that the algorithm should
actually complete and can not take an infinite amount of time. When using a search algorithm the end
result can actually be nothing which indicates that the value was n't found. The guidelines help us define
what an algorithm is but also helps us verify that the algorithm is correct executing the steps in an
algorithm for a given input must result in the same output every time. The same set of guidelines makes
for good algorithmic thinking which is one of the most important skills we want to cultivate when we
encounter a problem before rushing into thinking about solutions. Algorithm correctness is proved by
mathematical induction which is a form of reasoning used in mathematics to verify that a statement is
correct. Algorithms are used in the sequencing of dna(Distributed interNet Applications Architecture) and
more efficient sequencing algorithms allow us to research and understand diseases better and faster but
let 's not get ahead of ourselves we 'll start simple by evaluating john 's linear search algorithm in terms of
its efficiency.
We will learn these concepts by using well-known algorithms in this course we will also be writing code.
This course is less about specific algorithms and more about the tools you will need to evaluate
algorithms. An algorithm is a set of steps or instructions for completing a task. The field of computer
science has identified several that do the job well for a given task. Understanding algorithms is not just
knowing that an algorithm exists but understanding when to apply it requires properly understanding the
problem at hand. Learning about algorithms gives you a deeper understanding about complexity and
efficiency in programming. The course will focus on some of the tools and concepts you'll need to be
aware of before we can dive into the topic of algorithms if you're ready we 're going to try and cultivate
together as we work through our topic
John and britney took the same amount of turns to find the answer to the number they were looking
for. When the answer was three they both took the same number of turns this is important when the
number was larger but not much larger 10 in this case we start to see that britney strategy did better she
took four tries while john took 10. John and brittany took turns to find the answer when the number was
5. When the answer was 100 it took him 20 times the amount of tries to get that answer compared to
britney. The speed at which the result was obtained differed between john and brittany and britney in the
game. The two strategies britney and john used were examples of search more specifically these are
search algorithms. The strategy john took where he started at the beginning of the range and just counted
one number after the other is a type of search called linear search. linear search is a search algorithm and
we can use it in the real world for example i could tell you to walk into a bookstore and find me a
particular book using the linear search algorithm.
An algorithm definition must contain a specific set of instructions in a particular order. Each step must
not be a complex one and needs to be explicitly clear. The last guideline is that the algorithm should
actually complete and can not take an infinite amount of time. When using a search algorithm the end
result can actually be nothing which indicates that the value was n't found. The guidelines help us define
what an algorithm is but also helps us verify that the algorithm is correct executing the steps in an
algorithm for a given input must result in the same output every time. The same set of guidelines makes
for good algorithmic thinking which is one of the most important skills we want to cultivate when we
encounter a problem before rushing into thinking about solutions. Algorithm correctness is proved by
mathematical induction which is a form of reasoning used in mathematics to verify that a statement is
correct. Algorithms are used in the sequencing of dna(Distributed interNet Applications Architecture) and
more efficient sequencing algorithms allow us to research and understand diseases better and faster but
let 's not get ahead of ourselves we 'll start simple by evaluating john 's linear search algorithm in terms of
its efficiency.
