Rice University - Algorithmic Thinking (Part 2)
- Offered byCoursera
- Public/Government Institute
Algorithmic Thinking (Part 2) at Coursera Overview
Duration | 12 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Algorithmic Thinking (Part 2) at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 6 of 7 in the Fundamentals of Computing Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 12 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Algorithmic Thinking (Part 2) at Coursera Course details
- Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.
- In part 2 of this course, we will study advanced algorithmic techniques such as divide-and-conquer and dynamic programming. As the central part of the course, students will implement several algorithms in Python that incorporate these techniques and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms.
- Once students have completed this class, they will have both the mathematical and programming skills to analyze, design, and program solutions to a wide range of computational problems. While this class will use Python as its vehicle of choice to practice Algorithmic Thinking, the concepts that you will learn in this class transcend any particular programming language.
Algorithmic Thinking (Part 2) at Coursera Curriculum
Module 3 - Core Materials
What is Algorithmic Thinking?
The sorting problem
A simple quadratic algorithm
Illustrating MergeSort
The recurrence for MergeSort
The Master Theorem and MergeSort efficiency
Linear vs. binary search
Efficiency of binary search
Class structure (from part 1)
Coding styles and standards - PoC
Testing and machine grading - PoC
Plotting data - PoC
Peer assessment - "We want a shrubbery!" - IIPP
Class notes
Coding notes
Homework #3
Module 3 - Project and Application
Project #3 Description
Tests and Tips for Implementing the Clustering Methods
Application #3 Description
Application #3 Solution
Module 4 - Core Materials
The RNA secondary structure problem
A dynamic programming algorithm
Illustrating the DP algorithm
Running time of the DP algorithm
DP vs. recursive implementation
Global pairwise sequence alignment
Local pairwise sequence alignment
Homework 4
Module 4 - Project and Application
Class wrap-up
Project #4 Description
Application #4 Description
Application #4 Solution
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