Data Structures and Algorithms High Level MADE EASY Review

Data Structures and Algorithms – High Level MADE EASY Review

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Data Structures and Algorithm are the building blocks for programming. If you want to win over coding interviews or you want to develop your own products. You need to have a solid learning source. Data Structures and Algorithms – High Level MADE EASY udemy course is really awesome in understanding Algo and DS quickly.  In this article, We will review the complete course on various parameters.

 

Data Structures and Algorithms – High Level MADE EASY ( Silent Features) –

This course has divided into many sections.  Actually, the section-wise study is more systematic. At the time of writing this article total of 14,446 students have registered for this course.  Well, Let’s have the look at the content delivery of this udemy course.

Number of Hours: 8h 19 m

Total Sections: 3

Total Number of Lectures:  37

Lifetime Access: Yes

Data Structures and Algorithms - High Level MADE EASY course
Data Structures and Algorithms – High Level MADE EASY Review

Course Content ( Topics) –

Now let’s see what you will learn in this course.

Merge Sort
Pseudocode
Merge Sort Analysis
Guiding Principles of Algorithm Assessment
Big Oh Definition
Big Oh Examples
Omega, theta, little oh
Divide and Conquer Inversions
Counting Inversions
Multiplying Matrices
Hash
Heap
Binary Search Tree

Student Feedbacks-

Most Importantly this section has a higher weight in deciding to buy a course or not. The Reason is very clear that the author may create an impressive catalogue for advertisement. But the real user always put the right opinion.

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Most Importantly why I personally referring this course because it is completely focused on main topics.  I really believe clarity makes things doable. Otherwise, students like us lose our vision. Also, it is a complete roadmap to achieve a good level in Algorithms and DS concepts.

 

Please write your comment on why you like this course in the comment section.2

Thanks 

Data Science Learner Team

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