Data Structures and Algorithms Specialization

Master Algorithmic Programming Techniques. Advance your Software Engineering or Data Science Career by Learning Algorithms through Programming and Puzzle Solving. Ace coding interviews by implementing each algorithmic challenge in this Specialization. Apply the newly-learned algorithmic techniques to real-life problems, such as analyzing a huge social network or sequencing a genome of a deadly pathogen.

Instructors: Michael Levin +5 more

What you'll learn

  •   Play with 50 algorithmic puzzles on your smartphone to develop your algorithmic intuition!  Apply algorithmic techniques (greedy algorithms, binary search, dynamic programming, etc.) and data structures (stacks, queues, trees, graphs, etc.) to solve 100 programming challenges that often appear at interviews at high-tech companies. Get an instant feedback on whether your solution is correct.
  •   Apply the newly learned algorithms to solve real-world challenges: navigating in a Big Network  or assembling a genome of a deadly pathogen from millions of short substrings of its DNA.
  •    from UCSD...due to excellent course material and its practical approach.”
  •   If you decide to venture beyond Algorithms 101, try to solve more complex programming challenges (flows in networks, linear programming, streaming algorithms, etc.) and complete an equivalent of a graduate course in algorithms!

    • Skills you'll gain

  •   Software Testing
  •   Operations Research
  •   Network Analysis
  •   Network Model
  •   Network Routing
  •   Computational Thinking
  •   Graph Theory
  •   Bioinformatics
  •   Data Storage
  •   Data Structures
  •   Computer Programming
  •   Algorithms

    • Specialization - 6 course series

    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 shortest path algorithms you learn in the standard Algorithms 101 course! 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.

    A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!

    In this online course, you will first learn what a graph is and what are some of the most important properties. Then you'll learn several ways to traverse graphs and how you can do useful things while traversing the graph in some order. We will then talk about shortest paths algorithms — from the basic ones to those which open door for 1000000 times faster algorithms used in Google Maps and other navigational services. You will use these algorithms if you choose to work on our Fast Shortest Routes industrial capstone project. We will finish with minimum spanning trees which are used to plan road, telephone and computer networks and also find applications in clustering and approximate algorithms.

    World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome. In this online course you will learn key pattern matching concepts: tries, suffix trees, suffix arrays and even the Burrows-Wheeler transform.

    In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.

    To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.

    Data Structures

    Algorithms on Graphs

    Algorithms on Strings

    Advanced Algorithms and Complexity

    Genome Assembly Programming Challenge

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