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Course Spotlight Data Structures & Algorithms - A Comparison of Two Top MOOC Programs

Posted in 'Course Tracker' started by MoocLab, Oct 5, 2018.

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  1. MoocLab

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    A data structure is a way of storing data in a computer which can then be efficiently manipulated by an algorithm to solve computational problems. By using a combination of data structures and algorithms, computer scientists can drastically improve the performance of a computer program.

    All computers rely on fundamental data structures and algorithms so learning about them will make you a better programmer and you'll be more efficient at solving problems.

    Below, we look at two data structure and algorithm MOOC spaecilizations offered on Coursera.

    At a Glance

    CoursePlatformProviderLevelNº CoursesLengthCost*Skills
    1CourseraUC San DiegoIntermediate6133 Hours£38/monthGraph Algorithms, Dynamic Programming, Data Structure, Algorithms, Algorithms On Strings
    2CourseraStanford UniversityIntermediate476 Hours£38/monthDynamic Programming, Greedy Algorithm, Divide And Conquer Algorithms, Algorithms, Data Structure
    *Individual courses can be audited free of charge


    1. Data Structures and Algorithms Specialization
    Master Algorithmic Programming Techniques. Learn algorithms through programming and advance your software engineering or data science career

    About this course
    This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

    Course List
    1. Algorithmic Toolbox
    2. Data Structures
    3. Algorithms on Graphs
    4. Algorithms on Strings
    5. Advanced Algorithms and Complexity
    6. Genome Assembly Programming Challenge

    2. Algorithms Specialization
    Learn To Think Like A Computer Scientist. Master the fundamentals of the design and analysis of algorithms.

    About this course
    Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.

    About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.

    Course List
    1. Divide and Conquer, Sorting and Searching, and Randomized Algorithms
    2. Graph Search, Shortest Paths, and Data Structures
    3. Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
    4. Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
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