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# Algorithms Data Structures in Java #2 (+INTERVIEW QUESTIONS)

via Udemy

### Overview

Tries Data Structures, Ternary Search Trees, Data Compression, Substring Search and Sorting Algorithms

What you'll learn:
• Grasp the fundamentals of algorithms and data structures
• Develop your own algorithms that best fit to the personal need
• Detect non-optimal code snippets
• Understand data compression
• Understand sorting algorithms
• Understand tries and ternary search trees
• Understand Strings and StringBuilders

This course is about data structures and algorithms. We are going to implement the problems in Java, but I try to do it as generic as possible: so the core of the algorithms can be used in C++ or Python. The course takes approximately 12hours to complete. I highly recommend typing out these data structures several times on your own in order to get a good grasp of it.

Section 1 - Tries

• what are prefix trees (tries)

• basics operations: insertion, sorting and autocomplete

• longest common prefix problem

• prefix trees applications in networking (IP routing)

Section 2 - Ternary Search Trees

• what is the problem with tries?

• what are ternary search trees

• basic operations: insertion and retrieval

• applications of tries (IP routing and Boggle Game)

Section 3 - Substring Search Algorithms

• substring search algorithms

• brute-force substring search

• Z substring search algorithm

• Rabin-Karp algorithm and hashing

• Knuth-Morris-Pratt (KMP) substring search algorithm

Section 4 - Strings

• strings in Java programming

• what is the String Constant Pool?

• prefixes and suffixes

• longest common prefix problem

• longest repeated substring problem

• suffix tries and suffix arrays

Section 5 - Sorting Algorithms

• basic sorting algorithms

• bubble sort and selection sort

• insertion sort and shell sort

• quicksort and merge sort

• comparison based and non-comparison based approaches

• string sorting algorithms

• bucket sort and radix sort

Section 6 - Data Compression Algorithms

• what is data compression

• run length encoding

• Huffman-encoding

• LZW compression and decompression

Section 7 - Algorithms Analysis

• how to measure the running time of algorithms

• running time analysis with big O (ordo), big Ω (omega) and big θ (theta) notations

• complexity classes

• polynomial (P)and non-deterministic polynomial (NP)algorithms

• O(1), O(logN), O(N) and several other running time complexities

First, we are going to discuss prefix trees: modern search engines for example use these data structures quite often. When you make a google search there is an autocomplete feature because ofthe underlying trie data structure. It is also good for sorting: hashtables do not support sort operation but on the other hand, tries do support.

Substring search is another important field of computer science. You will learn about Z algorithm and we will discuss brute-force approach as well as Rabin-Karp method.

The next chapter is about sorting. How to sort an array of integers, doubles, strings or custom objects? We can do it with bubble sort, insertion sort, mergesort or quicksort. You will learn a lot about the theory as well as the concrete implementation of these important algorithms.

The last lectures are about data compression: run-length encoding, Huffman encoding and LZW compression.

Thanks for joining the course, let's get started!

Holczer Balazs

## Reviews

4.4 rating at Udemy based on 796 ratings

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