About this 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).
Who is this class for: Programmers with basic experience looking to understand the practical and conceptual underpinnings of algorithms, with the goal of becoming more effective software engineers. Computer science students and researchers as well as interdisciplinary students (studying electrical engineering, mathematics, bioinformatics, etc.) aiming to get more profound understanding of algorithms and hands-on experience implementing them and applying for real-world problems. Applicants who want to prepare for an interview in a high-tech company.
Created by: University of California, San Diego, Higher School of Economics
Taught by: Alexander S. Kulikov, Visiting Professor
Department of Computer Science and Engineering
Taught by: Michael Levin, Lecturer
Computer Science
Taught by: Neil Rhodes, Adjunct Faculty
Computer Science and Engineering
Taught by: Pavel Pevzner, Professor
Department of Computer Science and Engineering
Taught by: Daniel M Kane, Assistant Professor
Department of Computer Science and Engineering / Department of Mathematics
| Basic Info | Course 1 of 6 in the Data Structures and Algorithms Specialization |
| Commitment | 5 weeks of study, 4-8 hours/week |
| Language | English, Subtitles: Spanish |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Welcome
Welcome to the first module of Data Structures and Algorithms! Here we will provide an overview of where algorithms and data structures are used (hint: everywhere) and walk you through a few sample programming challenges. The programming challenges represent an important (and often the most difficult!) part of this specialization because the only way to fully understand an algorithm is to implement it. Writing correct and efficient programs is hard; please don’t be surprised if they don’t work as you planned—our first programs did not work either! We will help you on your journey through the specialization by showing how to implement your first programming challenges. We will also introduce testing techniques that will help increase your chances of passing assignments on your first attempt. In case your program does not work as intended, we will show how to fix it, even if you don’t yet know which test your implementation is failing on.
6 videos, 7 readings, 2 practice quizzes
- Video: Welcome!
- Reading: Overview
- Reading: Available Programming Languages
- Video: Solving the Problem (screencast)
- Reading: What's Up Next?
- Practice Quiz: Solving Programming Assignments
- Reading: Solving the Problem: Improving the Naive Solution, Testing, Debugging
- Video: Solving the Problem: Improving the Naive Solution, Testing, Debugging
- Reading: Stress Testing: the [Almost] Silver Bullet for Debugging
- Video: Stress Test - Implementation
- Video: Stress Test - Find the Test and Debug
- Video: Stress Test - More Testing, Submit and Pass!
- Reading: FAQ on Programming Assignments
- Practice Quiz: Solving Programming Assignments
- Reading: Acknowledgements
Graded: A plus B
Graded: Maximum Pairwise Product
WEEK 2
Introduction
In this module you will learn that programs based on efficient algorithms can solve the same problem billions of times faster than programs based on naïve algorithms. You will learn how to estimate the running time and memory of an algorithm without even implementing it. Armed with this knowledge, you will be able to compare various algorithms, select the most efficient ones, and finally implement them as our programming challenges!
12 videos, 3 readings
- Video: Why Study Algorithms?
- Video: Coming Up
- Video: Problem Overview
- Video: Naive Algorithm
- Video: Efficient Algorithm
- Reading: Resources
- Video: Problem Overview and Naive Algorithm
- Video: Efficient Algorithm
- Reading: Resources
- Video: Computing Runtimes
- Video: Asymptotic Notation
- Video: Big-O Notation
- Video: Using Big-O
- Reading: Resources
- Video: Course Overview
Graded: Logarithms
Graded: Big-O
Graded: Growth rate
Graded: Programming Assignment 1: Introduction
WEEK 3
Greedy Algorithms
In this module you will learn about seemingly naïve yet powerful class of algorithms called greedy algorithms. After you will learn the key idea behind the greedy algorithms, you may feel that they represent the algorithmic Swiss army knife that can be applied to solve nearly all programming challenges in this course. But be warned: with a few exceptions that we will cover, this intuitive idea rarely works in practice! For this reason, it is important to prove that a greedy algorithm always produces an optimal solution before using this algorithm. In the end of this module, we will test your intuition and taste for greedy algorithms by offering several programming challenges.
10 videos, 1 reading
- Video: Largest Number
- Video: Car Fueling
- Video: Car Fueling - Implementation and Analysis
- Video: Main Ingredients of Greedy Algorithms
- Video: Celebration Party Problem
- Video: Efficient Algorithm for Grouping Children
- Video: Analysis and Implementation of the Efficient Algorithm
- Video: Long Hike
- Video: Fractional Knapsack - Implementation, Analysis and Optimization
- Video: Review of Greedy Algorithms
- Reading: Resources
Graded: Greedy Algorithms
Graded: Fractional Knapsack
Graded: Programming Assignment 2: Greedy Algorithms
WEEK 4
Divide-and-Conquer
In this module you will learn about a powerful algorithmic technique called Divide and Conquer. Based on this technique, you will see how to search huge databases millions of times faster than using naïve linear search. You will even learn that the standard way to multiply numbers (that you learned in the grade school) is far from the being the fastest! We will then apply the divide-and-conquer technique to design two efficient algorithms (merge sort and quick sort) for sorting huge lists, a problem that finds many applications in practice. Finally, we will show that these two algorithms are optimal, that is, no algorithm can sort faster!
20 videos, 5 readings
- Video: Intro
- Video: Linear Search
- Video: Binary Search
- Video: Binary Search Runtime
- Reading: Resources
- Video: Problem Overview and Naïve Solution
- Video: Naïve Divide and Conquer Algorithm
- Video: Faster Divide and Conquer Algorithm
- Reading: Resources
- Video: What is the Master Theorem?
- Video: Proof of the Master Theorem
- Reading: Resources
- Video: Problem Overview
- Video: Selection Sort
- Video: Merge Sort
- Video: Lower Bound for Comparison Based Sorting
- Video: Non-Comparison Based Sorting Algorithms
- Reading: Resources
- Video: Overview
- Video: Algorithm
- Video: Random Pivot
- Video: Running Time Analysis (optional)
- Video: Equal Elements
- Video: Final Remarks
- Reading: Resources
Graded: Linear Search and Binary Search
Graded: Polynomial Multiplication
Graded: Master Theorem
Graded: Sorting
Graded: Quick Sort
Graded: Programming Assignment 3: Divide and Conquer
WEEK 5
Dynamic Programming
In this final module of the course you will learn about the powerful algorithmic technique for solving many optimization problems called Dynamic Programming. It turned out that dynamic programming can solve many problems that evade all attempts to solve them using greedy or divide-and-conquer strategy. There are countless applications of dynamic programming in practice: from maximizing the advertisement revenue of a TV station, to search for similar Internet pages, to gene finding (the problem where biologists need to find the minimum number of mutations to transform one gene into another). You will learn how the same idea helps to automatically make spelling corrections and to show the differences between two versions of the same text.
12 videos, 3 readings
- Video: Change Problem
- Reading: Resources
- Video: The Alignment Game
- Video: Computing Edit Distance
- Video: Reconstructing an Optimal Alignment
- Reading: Resources
- Video: Problem Overview
- Video: Knapsack with Repetitions
- Video: Knapsack without Repetitions
- Video: Final Remarks
- Reading: Resources
- Video: Problem Overview
- Video: Subproblems
- Video: Algorithm
- Video: Reconstructing a Solution
Graded: Change Money
Graded: Edit Distance
Graded: Knapsack
Graded: Maximum Value of an Arithmetic Expression
Graded: Programming Assignment 4: Dynamic Programming
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University of California, San Diego
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Higher School of Economics
National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more.
Learn more on www.hse.ru
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Course content generally was interesting, though explanations were sometimes hard to follow. Slides could contain more information to make the matter easier to grasp.
Nicely explained , enough for solving the assignments. Good for understanding various techniques used in solving different and almost all possible problems efficiently.
Lots of good problems. The bar to pass is very low, but I felt I actually learned by completing the whole thing. There is a forum with some helpful advice on some of the problems. The easiest problems are the ones you just copy the algorithm verbatim from the slides, but there are some more interesting problems with terrible edge cases, like the minimal distance among n points.
They follow the textbook pretty well, so you can pick up that book for cheap and read it too! I liked most of the lecturers, but a few I could have done without and ended up referencing other sources as a result. I learned a lot and will continue in the specialization.
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Great Work! thanks a lot to the Coursera and Course instructors.
Some test cases shows only wrong answer not any information about why and what is going wrong
A little hard to find what is wrong with your code specially when it gives the current answer with examples on the pdf file