Algorithms: Design and Analysis

Price:

Free

Difficulty Level:

Beginner

Duration:

(6-8 weeks)

Path:

Path 1

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ABOUT THE COURSE :

Welcome to the self paced course, Algorithms: Design and Analysis! 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.

Specific topics in the course include: "Big-oh" notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), randomized algorithms (QuickSort, contraction algorithm for min cuts), data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of BFS and DFS, connectivity, shortest paths).

Learners will practice and master the fundamentals of algorithms through several types of assessments. There are 6 multiple choice quizzes to test your understanding of the most important concepts. There are also 6 programming assignments, where you implement one of the algorithms covered in lecture in a programming language of your choosing. The course concludes with a multiple-choice final. There are no assignment due dates and you can work through the course materials and assignments at your own pace.

To succeed in this course, students should have:

Programming Experience: Proficiency in at least one programming language (e.g., Python, Java, C++) with a solid understanding of data structures such as arrays, linked lists, stacks, queues, and trees.
Mathematics: A strong foundation in discrete mathematics, including concepts like set theory, logic, combinatorics, and probability.
Understanding of mathematical proofs, including induction and proof by contradiction.
Basic Algorithms Knowledge: Familiarity with basic algorithmic concepts, such as sorting and searching algorithms, and basic understanding of time complexity and Big O notation.

Note: If you do not meet these prerequisites, it is recommended that you complete the following preparatory courses:

Introduction to Programming (e.g., CS50's Introduction to Computer Science)
Discrete Mathematics (e.g., Coursera’s Discrete Mathematics Specialization)
Data Structures and Algorithms (e.g., Coursera’s Data Structures and Algorithm Specialization)

To support your learning and provide additional guidance to master this field, the following resources are recommended:

Books
- Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein (CLRS): Often referred to as the "bible" of algorithms, this book covers a broad range of topics in algorithm design and analysis, with detailed explanations and mathematical rigor.
- Algorithm Design by Jon Kleinberg and Éva Tardos: This book provides a clear introduction to the design of algorithms, with a focus on understanding the principles behind algorithmic thinking and problem-solving.
- The Art of Computer Programming by Donald Knuth: A classic series that dives deep into the analysis of algorithms and data structures, ideal for those who want a comprehensive understanding of algorithm theory.
Online Tutorials
- Coursera's Algorithms Specialization by Stanford University: A series of courses that cover algorithmic techniques and their implementation, including sorting, searching, graph algorithms, and NP-completeness.
- GeeksforGeeks' Algorithms Tutorial: A comprehensive set of tutorials and practice problems on various algorithms, from basic to advanced topics.
- MIT OpenCourseWare: Introduction to Algorithms (6.006): A free online course offering lecture videos, notes, and assignments on algorithms, covering topics like dynamic programming, graph algorithms, and computational complexity.
Software Tools
- Python with libraries like NumPy and SciPy: These libraries can be used for implementing algorithms and running computational experiments to analyze their performance.
- Graphviz: A tool for visualizing graphs and networks, which can be very useful when studying graph algorithms.
- Jupyter Notebook: A web-based interactive computing environment for writing and testing algorithm implementations with live code, visualizations, and narrative text.
Communities and Forums
- Stack Overflow: Algorithms: A community where you can ask and answer questions related to algorithms, offering insights into both theoretical and practical aspects.
- LeetCode Discuss: A platform for discussing algorithmic problems and solutions, particularly useful for preparing for technical interviews and sharpening algorithm skills.
- Reddit's Algorithms Community: A place for sharing articles, asking questions, and discussing topics related to algorithm design, analysis, and implementation.
Supplementary Courses
- Algorithmic Thinking (Part 1 and Part 2) by Rice University on Coursera: These courses focus on the principles of algorithmic thinking and how to apply algorithms to solve real-world problems, making it a good complement to theoretical studies.
- Advanced Data Structures by UC San Diego on Coursera: This course delves into more complex data structures and their applications, which is crucial for mastering advanced algorithmic techniques.
- Approximation Algorithms by Stanford University on Coursera: A course that explores algorithms for NP-hard problems, providing insight into techniques for designing and analyzing approximation algorithms.

YOUR INSTRUCTOR

Tim Roughgarden

Professor of Computer Science at Stanford University

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