Data Structures & Algorithms

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Course Highlights

This course is a part of the AI Ascend program.
In this online course, we consider the common data structures that are used in various computational problems.
You will learn how these data structures are implemented in Python programming languages.
This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it.
Job Roles-
- Data Scientist

At the end of this course, you should be able to:

At the end of this course, you should be able to:

This course introduces the various data structures & algorithm techniques to be applied to the data to make it fit for further analysis. The course is divided into 7 modules which cover the important topics for data structures & algorithms:
The first one is introduction & efficiency which talks about algorithm efficiency, complexity notation.
Next one is list based on collections. This discusses how processes such as creation, insertion, deletion, and manipulation can be applied on a list, string, array, linked list, stack, and queues.
The third module is searching & sorting. This module describes algorithms such as binary search, bubble sort, merge sort, quick sort, and recursion.
The fourth module, maps & hashing, explores maps, hashing, and collision algorithms.
The fifth module, trees, describes binary search tree, advanced tree, and their different operations.
The sixth module, graphs, explores different operations of graphs.
The seventh module, the case study, talks about the shortest path problem, dynamic programming, and traveling salesman problem.
A Machine Learning engineer needs to preprocess the data as a part of EDA, write algorithms to build models. So, a good foundation in the DSA course is essential.