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Intro to Algorithms

Timeline Approx. 4 Months

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Course Summary

Learn the fundamentals of algorithms and data structures in this intro course. Gain an understanding of common algorithms and their efficiency, and apply your knowledge to solve coding challenges.

Key Learning Points

Gain a strong foundation in algorithms and data structures
Learn to analyze the efficiency of algorithms
Apply your knowledge to solve coding challenges

Learning Outcomes

Ability to design and analyze basic algorithms
Understanding of data structures and their applications
Experience solving coding challenges

Prerequisites or good to have knowledge before taking this course

Basic programming knowledge in a language such as Python or Java
Familiarity with fundamental computer science concepts

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

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Data Structures and Algorithms
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Description

This class will give you an introduction to the design and analysis of algorithms, enabling you to analyze networks and discover how individuals are connected.

Outline

lesson 1 A Social Network Magic Trick Become familiar with algorithm analysis. Eulerian Path and Correctness of Na. Russian peasants algorithm and more. lesson 2 Growth Rates in Social Networks Use mathematical tools to analyze how things are connected. Chain, ring and grid networks. Big Theta and more. lesson 3 Basic Graph Algorithms Find the quickest route to Kevin Bacon. Properties of social networks. Clustering coefficient and more. lesson 4 It's Who You Know Learn to keep track of your best friends using heaps. Degree centrality. Top K Via Partitioning and more. lesson 5 Strong and Weak Bonds Work with social networks that have edge weights. Make a tree and strength of connections. Weighted social networks and more. lesson 6 Hardness of Network Problems Explore what it means for a social network problem to be "harder" than other. Tetristan and Exponential Running Time Degrees of hardness and more. lesson 7 Review and Application Interview with Peter Winker (Professor, Dartmouth College) on names and boxes problem and puzzles and algorithms. Interview with Tina Eliassi-Rad (Professor, Rutgers University) on statistical measures in network and social networks in security and protests. Additional interviews with Andrew Goldberg (Microsoft Research), Vukosi Marivate (Rutgers University) and Duncan Watts (Microsoft).

Summary of User Reviews

Key Aspect Users Liked About This Course

Many users appreciated the comprehensive and well-structured content of the course.

Pros from User Reviews

Great course for beginners to learn algorithms
Excellent presentation of the material
Easy to follow and understand
Good combination of theory and practice
In-depth explanations and examples

Cons from User Reviews

Some users found the course too theoretical and abstract
Not enough programming exercises
Some lectures were too long and dry
The quizzes and exams could be more challenging
Some users felt that the course lacked real-world applications

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