About This Specialization
The Specialization covers algorithmic techniques for solving problems arising in computer science applications. It is a mix of theory and practice: you will not only design algorithms and estimate their complexity, but you will get a deeper understanding of algorithms by implementing them in the programming language of your choice (C, C++, C#, Haskell, Java, JavaScript, Python2, Python3, Ruby, and Scala).
This Specialization is unique, because you will have a choice between two Capstone Projects, developed in partnership with industry leaders. In the Shortest Paths Capstone, you'll deal with road network analysis and social network analysis. You'll learn how to compute the fastest route between New York and Mountain View thousands of times faster than classic algorithms and close to those used in Google Maps. In the Bioinformatics Capstone, you'll learn how to assemble genomes from millions of short pieces and how algorithms fuel recent developments in personalized medicine.
COURSE 1
Algorithmic Toolbox
Current session: Jul 25 — Sep 5.
Commitment
5 weeks of study, 4-8 hours/week
About the Course
The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).
COURSE 2
Data Structures
Upcoming session: Aug 1 — Sep 12.
Commitment
4 weeks of study, 5-10 hours/week
About the Course
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. 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!
COURSE 3
Algorithms on Graphs
Upcoming session: Aug 1 — Sep 12.
Commitment
5 weeks of study, 3-4 hours/week
About the Course
If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs. In this 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.
COURSE 4
Algorithms on Strings
Current session: Jul 26 — Aug 29.
Commitment
4 weeks of study, 4-8 hours/week
About the Course
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.
COURSE 5
Advanced Algorithms and Complexity
Starts August 2016
About the Course
You've learned the basic algorithms now and 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 obvious applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision or finding dense clusters in the advertiser-search query graphs at search engines. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements, call routing in telecommunications 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 approximately in a reasonable time. We finish with some applications to Big Data and Machine Learning which are heavy on algorithms right now.
CAPSTONE PROJECT
Assembling Genomes and Finding Disease-Causing Mutations
Starts September 2016
About the Capstone Project
Carsonella ruddii is a bacterium that lives symbiotically inside some insects. Its sheltered life has allowed it to reduce its genome to only about 160,000 base pairs. With only about 200 genes, it lacks some genes necessary for survival, but these genes are supplied by its insect host. In fact, Carsonella has such a small genome that biologists have conjectured that it is losing its “bacterial” identity and turning into an organelle, which is part of the host’s genome. This transition from bacterium to organelle has happened many times during evolutionary history; in fact, the mitochondrion responsible for energy production in human cells was once a free-roaming bacterium that we assimilated in the distant past. Given a collection of simulated error-free read-pairs, use the paired de Bruijn graph to reconstruct the Carsonella ruddii genome. Compare this assembly to the assembly obtained from the classic de Bruijn graph (i.e., when all we know is the reads themselves and do not know the distance between paired reads) in order to better appreciate the benefits of read-pairs. For each k, what is the minimum value of d needed to enable reconstruction of the entire Carsonella ruddii genome from its (k, d)-mer composition?
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