Data Structures and Algorithms Specialization

• What is the refund policy?

  If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

• Can I just enroll in a single course?

  Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

• Is financial aid available?

  Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

• Can I take the course for free?

  When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

• Is this course really 100% online? Do I need to attend any classes in person?

  This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

• What will I be able to do upon completing the Specialization?

  You will be able to apply the right algorithms and data structures in your day-to-day work and write programs that work in some cases many orders of magnitude faster. You'll be able to solve algorithmic problems like those used in the technical interviews at Google, Facebook, Microsoft, Yandex, etc. If you do data science, you'll be able to significantly increase the speed of some of your experiments. You'll also have a completed Capstone either in Bioinformatics or in the Shortest Paths in Road Networks and Social Networks that you can demonstrate to potential employers.

• What background knowledge is necessary?

  1. Basic knowledge of at least one programming language: C++, Java, Python, C, C#, Javascript, Haskell, Kotlin, Ruby, Rust, Scala.

  We expect you to be able to implement programs that: 1) read data from the standard input (in most cases, the input is a sequence of integers); 2) compute the result (in most cases, a few loops are enough for this); 3) print the result to the standard output. For each programming challenge in this course, we provide starter solutions in C++, Java, and Python. The best way to check whether your programming skills are enough to go through problems in this specialization is to solve two problems from the first week. If you are able to pass them (after reading our tutorials), then you will definitely be able to pass the course.

  2. Basic knowledge of discrete mathematics: proof by induction, proof by contradiction.

  Knowledge of discrete mathematics is necessary for analyzing algorithms (proving correctness, estimating running time) and for algorithmic thinking in general. If you want to refresh your discrete mathematics skills, we encourage you to go through our partner specialization — Introduction to Discrete Mathematics for Computer Science (https://www.coursera.org/specializations/discrete-mathematics). It teaches the basics of discrete mathematics in try-this-before-we-explain-everything approach: you will be solving many interactive puzzles that were carefully designed to allow you to invent many of the important ideas and concepts yoursel

• What is the difference between this course and other courses covering algorithms?

  We believe that learning the theory behind algorithms (like in most Algorithms 101 courses taught at 1000s universities) is important but not sufficient for a professional computer scientist today. This specialization combines the theory of algorithms with many programming challenges. In contrast with many Algorithms 101 courses, you will implement over 100 algorithmic problems in the programming language of your choice. And you will see yourself that the best way to understand an algorithm is to implement it!

• How long does it take to complete the Specialization?

  Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 6-8 months.

• How often is each course in the Specialization offered?

  Each course in the Specialization is offered on a regular schedule, with sessions starting about once per month. If you don't complete a course on the first try, you can easily transfer to the next session, and your completed work and grades will carry over.

• What background knowledge is necessary?

  Basic knowledge of at least one programming language (C/C++, Java, Python): loops, arrays, stacks, recursion. Basic knowledge of mathematics: proof by induction, proof by contradiction.

• Do I need to take the courses in a specific order?

  We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.

• Will I earn university credit for completing the Specialization?

  Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

• Do I need to buy a textbook for this specialization?

  The lectures in this specialization will be self-contained. Most lectures will be based on the bestselling textbook "Algorithms" co-authored by Sanjoy Dasgupta from University of California at San Diego as well as Christos Papadimitriou and Umesh Vazirani from University of California at Berkeley. In addition to UCSD and Berkeley, the textbook has been adopted in over 100 top universities and is available on Internet.

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