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via Udemy |
Go to Course: https://www.udemy.com/course/algorithms-and-data-structures-in-java-part-ii/
Certainly! Here’s a comprehensive review and recommendation for the Coursera course on Data Structures and Algorithms: --- **Course Review and Recommendation: Data Structures and Algorithms on Coursera** If you're looking to strengthen your understanding of fundamental data structures and algorithms, this Coursera course offers an excellent, well-rounded introduction. Spanning approximately 12 hours of content, it covers a broad spectrum of essential topics with practical implementations primarily in Java, while maintaining a focus on making the core concepts applicable across languages like C++ and Python. **Course Content Overview** The course is divided into seven detailed sections: 1. **Tries (Prefix Trees)** Learn what prefix trees are and their applications such as autocompletion and IP routing. The instructor emphasizes core operations like insertion and sorting, with illustrative examples. A practical skill that’s immensely useful for search engines and text processing. 2. **Ternary Search Trees** Explore the limitations of tries and how ternary search trees can be an effective alternative. You will understand how to implement insertion and retrieval and see their applications in networking and word games like Boggle. 3. **Substring Search Algorithms** Delve into algorithms for searching substrings, from brute-force to advanced techniques like Rabin-Karp and Knuth-Morris-Pratt (KMP). These are critical for string processing tasks such as search engines, DNA sequencing, and text analysis. 4. **Strings in Java** This section clarifies Java-specific string concepts, including the String Constant Pool, prefixes, suffixes, and problems like the longest common prefix or repeated substrings. The course also introduces suffix trees and arrays—powerful tools for string analysis. 5. **Sorting Algorithms** Gain insights into sorting techniques including bubble sort, insertion sort, quicksort, merge sort, and non-comparison sorts like bucket and radix sort. This section combines theory with implementation, vital for efficient data handling. 6. **Data Compression Algorithms** Understand the principles of data compression through run-length encoding, Huffman coding, and LZW compression. These algorithms are foundational in reducing data size for storage and transmission. 7. **Algorithms Analysis** Learn how to evaluate algorithm efficiency through Big O, Omega, and Theta notation. The section discusses complexity classes, providing the tools to analyze and compare algorithm performance effectively. **My Personal & Professional Recommendations** - **Hands-On Practice:** The course encourages coding the data structures multiple times. To truly grasp these concepts, actively implement each structure and algorithm, ideally in multiple programming languages. This approach solidifies understanding and builds coding confidence. - **In-Depth Learning:** Don't rush through the content; take time to comprehend the theory behind each algorithm alongside the implementations. Experiment with variations and edge cases. - **Practical Applications:** Think about real-world problems where these data structures and algorithms are applicable. Building small projects, such as autocomplete features, string search utilities, or simple compression tools, will reinforce your learning. - **Complementary Resources:** While this course provides a solid foundation, supplement your study with additional resources such as coding challenges on LeetCode, HackerRank, or Codeforces, focusing on string, sorting, and algorithm problems. **Final Verdict** This course is highly recommended for students, software engineers, or hobbyists aiming to build a strong foundation in data structures and algorithms. Its clear explanations, practical implementations, and broad coverage make it an ideal starting point or a valuable refresher. **Rating: 4.5/5** **Recommended for:** Beginners with some programming background and intermediate coders seeking to deepen their understanding of core CS concepts. --- Feel free to ask if you'd like a more detailed review of specific sections or guidance on how to supplement your learning!
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!