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


Algorithms Data Structures in Java #2 (+INTERVIEW QUESTIONS) | Udemy
English | Size: 2.27 GB
Genre: eLearning

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 12 hours 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 of the 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!

Who this course is for:
This course is meant for university students with quantitative background (mathematics, computer science) but anyone with core java knowledge can get a good grasp of the lectures

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