Byte-Sized-Chunks: Sorting and Searching Algorithms

A visual way to master basic algorithms for strong fundamentals!
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  • Lectures 14
  • Length 3.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 3/2016 English

Course Description

Note: This course is a subset of our 20+ hour course 'From 0 to 1: Data Structures & Algorithms' so please don't sign up for both:-)

This is an animated, visual and spatial way to learn data structures and algorithms

  • Our brains process different types of information differently - evolutionarily we are wired to absorb information best when it is visual and spatial i.e. when we can close our eyes and see it
  • More than most other concepts, Data Structures and Algorithms are best learnt visually. These are incredibly easy to learn visually, very hard to understand most other ways
  • This course has been put together by a team with tons of everyday experience in thinking about these concepts and using them at work at Google, Microsoft and Flipkart

What's Covered:

  • Big-O notation and complexity
  • Sorts: Quick, heap, merge, bubble, selection
  • Search: Binary searching a sorted list

Talk to us!

Mail us about anything - anything! - and we will always reply :-)

What are the requirements?

  • Basic knowledge of programming is assumed, preferably in Java

What am I going to get from this course?

  • Design and implement software that use sorting and searching algorithms
  • Understand the trade-offs, complexity and use-cases for different types of sort and search algorithms
  • Visualise - really vividly imagine - the common data structures, and the algorithms applied to them

What is the target audience?

  • Nope! Please don't enroll for this class if you have already enrolled for our 15-hour course 'From 0 to 1: Data Structures and Algorithms'
  • Yep! Computer Science and Engineering grads who are looking to really visualise data structures, and internalise how they work
  • Yep! Experienced software engineers who are looking to refresh important fundamental concepts

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Needle In A Haystack: Sorting and Searching!
You, This Course, and Us!
Preview
01:07
16:02

What is the performance of your code? How do you measure this? What is complexity and what is its relationship with performance?

16:46
The Big O notation is used to express complexity based on the size of the input specified for any algorithm. How is Big O expressed, how is it calculated and many examples to drive the concepts home!
19:13

The Big O notation becomes much clearer when you practice find the complexity of some sample pieces of code. Let's see how many of these you get right!

10:52

A sorting algorithm is not just defined by its complexity, there are a whole bunch of other characteristics which can be used to determine which sorting algorithm is the right one for a system. Let's understand what these characteristics are and what are the trade offs we might make.

15:24

The simplest and most naive sorting algorithm.

14:42
Closely allied with selection sort is bubble sort. Its an adaptive sort with the same time complexity as selection sort.
14:32
Insertion sort is an improvement over both bubble sort and selection sort. Let's see how exactly it works and why it's preferred in many cases.
14:24
Shell sort builds on top of insertion sort, it improves the complexity of it's running time by partitioning the list in a clever way.
19:23
This belongs to a class of algorithms which uses divide and conquer to break the problem set into smaller pieces. This also makes a time-space trade off to get a faster running time.
15:30

Quick sort is the sort of choice for developers of programming libraries. Let's see what makes it so attractive.

11:09

Binary search is a pretty nifty way to search through a sorted list in O(Log N) time. It's also an interview favorite so make sure you understand it well!

19:06
Back to sorting. The Heap Sort uses a heap to transform an unsorted array into a sorted array. Phase I is converting the unsorted array into a heap.
17:42
Phase II actually outputs the final sorted array. It involves removing the elements from the heap and placing it in a sorted array. The cool thing is that all of this can be done in-place.

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Instructor Biography

Loony Corn, A 4-person team;ex-Google; Stanford, IIM Ahmedabad, IIT

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: longtime Flipkart employee too, and IIT Guwahati alum

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)

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