Language: English | Size: 2.27 GB | Duration: 12h 57m
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
Requirements
Core Java
Internet connection
Description
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 - Trieswhat are prefix trees (tries)basics operations: insertion, sorting and autocompletelongest common prefix problemprefix trees applications in networking (IP routing)Section 2 - Ternary Search Treeswhat is the problem with tries?what are ternary search treesbasic operations: insertion and retrievalapplications of tries (IP routing and Boggle Game)Section 3 - Substring Search Algorithmssubstring search algorithmsbrute-force substring searchZ substring search algorithmRabin-Karp algorithm and hashingKnuth-Morris-Pratt (KMP) substring search algorithmSection 4 - Stringsstrings in Java programmingwhat is the String Constant Pool?prefixes and suffixeslongest common prefix problemlongest repeated substring problemsuffix tries and suffix arraysSection 5 - Sorting Algorithmsbasic sorting algorithmsbubble sort and selection sortinsertion sort and shell sortquicksort and merge sortcomparison based and non-comparison based approachesstring sorting algorithmsbucket sort and radix sortSection 6 - Data Compression Algorithmswhat is data compressionrun length encodingHuffman-encodingLZW compression and decompressionSection 7 - Algorithms Analysishow to measure the running time of algorithmsrunning time analysis with big O (ordo), big Ω (omega) and big θ (theta) notationscomplexity classespolynomial (P) and non-deterministic polynomial (NP) algorithmsO(1), O(logN), O(N) and several other running time complexitiesFirst, 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!
Overview
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