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
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
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!