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This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects
On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.
Week - 01 Introduction
A data structure is a named location that can be used to store and organise data. An algorithm is a collection of steps to solve a particular problem. Data structures provide a means to manage large amounts of data efficiently.
Week - 02 Arrays, Strings, and Lists
Arrays are used to store multiple values in a single variable, instead of declaring separate variables for each value. A string is a sequence of characters like - "hello".
Week - 03 Stacks and Queues
The stack is a linear data structure that is used to store a collection of objects. It is based on Last-In-First-Out (LIFO).
Week - 04 Trees
A tree is a non-linear data structure where data objects are organised in terms of hierarchical relationships. In this week's session, we’ll be discussing them one by one.
Week - 05 Heaps and Tries
Heap and Trie are special cases of trees. A heap is a special tree-based data structure in which the tree is a complete binary trees.
Week - 06 Graphs & Algorithms
A graph is a data structure that consists of a finite set of vertices, also called nodes and a finite set of ordered pairs of the form (u, v) called edges.
Week - 07 Sorting
Week - 08 Searching and Hashing
Searching is probably one of the most common actions performed in a regular business application. This involves fetching data stored in data structures like arrays, lists, and maps. Hashing is the process of converting a given key into another value. A hash function is used to generate the new value according to a mathematical algorithm.
Week - 09 Greedy Algorithms
A greedy algorithm is a simple, intuitive algorithm that is used in optimization problems.
Week - 10 Divide and Conquer
The divide and conquer approach is pretty straightforward, the problem at hand is divided into smaller subproblems and then each problem is solved independently. As we keep dividing the subproblems into even smaller problems, we eventually reach a stage where no more division is possible.
Week - 11 Backtracking
Backtracking is an algorithmic-technique for solving problems recursively by trying to build a solution incrementally.
Week - 12 Dynamic Programming
Dynamic Programming is also an algorithmic technique for solving optimization of problems. It is done by breaking the problem down into simpler subproblems, and utilizing the fact that the optimal solution to the overall problem depends upon the optimal solution to its subproblems.
Our courses have been designed by industry experts to help students achieve their dream careers
Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 1 Projects that are available in this program.
Optimize and Solve problems with given restrictions
Our courses have been designed by industry experts to help students achieve their dream careers
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Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals
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Live Individual & Group Sessionsto resolve queries, Discuss Progress and Study Plans.
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Job-Oriented Industry Relevant Curriculumavailable at your fingertips curated by Global Industry Experts along with Live Sessions.
Our courses are designed by leading academicians and experienced industry professionals.
1 industry expert
Our instructors are industry experts along with a passion to teach.
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Instructors with 10 years extensive industry experience.
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