Rs. 599

Career Builder: Introduction to Rapid Algorithms(Part II)

An advanced course on Data Algorithms for students exploring the potential of Data Science or IT professionals who have experienced the need for learning Data Algorithms.

1 year access

Course Introduction:

Problem solving is one skill that is and always will be in-demand in the skill marketplace. As technology keeps advancing, the need for professionals who can leverage it to develop solutions will never cease. Being acquainted with algorithms that put data to its best use to formulate solutions will give you a better edge over your peers and help you reach greater heights in your career. In this course, you will learn various industry-standard algorithms used by Data Professionals.

Course Objectives:

The contents of this course have been structured in such a way that the 3 most common types of advanced algorithms, namely Greedy algorithms, Graph algorithms and Dynamic programming are taught in less than 2 hours. This fast paced course is suitable for working professionals and students alike.

Pre-requisites and Target Audience:

This course is a sequel to this basic course on Data Algorithms. It is recommended that you take that course to understand basic algorithms which are essential to make the best use of this course. Apart from a sound knowledge in basic Data Algorithms and Data Structures, you will be required to be proficient with any of the following programming languages so that you can practice the examples outlines in this course:

  • C++
  • C#
  • Java
  • Python

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Course Plan

1. Greedy Algorithms
4 videos
Indroduction to Greedy Algorithms 03:06

Activity Selection Problem 04:30

Kruskal's Minimum Spanning Tree Algorithm 12:07
2. Graph Algorithms
5 videos
Introduction to Graph Algorithms 10:24

How to detect cycle in a Direct Graph 05:35

Prim's Minimum Spanning Tree Algorithm 13:51

Dijkstra's Shortest Path Algorithm 13:08
3. Dynamic Programming
4 videos
Introduction to Dynamic Programming 05:53

Longest Increasing Subsequence 07:43

Subset Sum Problem 06:09

0-1 Knapsack Problem 07:24