Algorithmic Thinking (Part 2)

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Beschrijving

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About this course: 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 se…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  Rice University
  • Taught by:  Luay Nakhleh, Associate Professor

    Computer Science; Biochemistry and Cell Biology
  • Taught by:  Scott Rixner, Professor

    Computer Science
  • Taught by:  Joe Warren, Professor

    Computer Science
Basic Info Course 6 of 7 in the Fundamentals of Computing Specialization Level Intermediate Commitment 4 weeks of study, 7-10 hours/week Language English, Subtitles: Spanish How To Pass Pass all graded assignments to complete the course. User Ratings 4.8 stars Average User Rating 4.8See what learners said Coursework

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Syllabus


WEEK 1


Module 3 - Core Materials
Sorting, searching, big-O notation, the Master Theorem


13 videos, 2 readings expand


  1. Video: What is Algorithmic Thinking?
  2. Video: The sorting problem
  3. Video: A simple quadratic algorithm
  4. Video: Illustrating MergeSort
  5. Video: The recurrence for MergeSort
  6. Video: The Master Theorem and MergeSort efficiency
  7. Video: Linear vs. binary search
  8. Video: Efficiency of binary search
  9. Video: Class structure (from part 1)
  10. Reading: Class notes
  11. Reading: Coding notes
  12. Video: Coding styles and standards - PoC
  13. Video: Testing and machine grading - PoC
  14. Video: Plotting data - PoC
  15. Video: Peer assessment - "We want a shrubbery!" - IIPP

Graded: Homework #3

WEEK 2


Module 3 - Project and Application
Closest pairs of points, clustering of points, comparison of clustering algorithms


4 readings expand


  1. Reading: Project #3 Description
  2. Reading: Tests and Tips for Implementing the Clustering Methods
  3. LTI Item: Project Submission History
  4. Reading: Application #3 Description
  5. Reading: Application #3 Solution

Graded: Assignment: Closest Pairs and Clustering Algorithms
Graded: Comparison of Clustering Algorithms

WEEK 3


Module 4 - Core Materials
Dynamic programming, running time of DP algorithms, local and global sequence alignment


7 videos expand


  1. Video: The RNA secondary structure problem
  2. Video: A dynamic programming algorithm
  3. Video: Illustrating the DP algorithm
  4. Video: Running time of the DP algorithm
  5. Video: DP vs. recursive implementation
  6. Video: Global pairwise sequence alignment
  7. Video: Local pairwise sequence alignment

Graded: Homework 4

WEEK 4


Module 4 - Project and Application
Computation of sequence alignments, applications to genomics and text comparison


1 video, 3 readings expand


  1. Reading: Project #4 Description
  2. Reading: Application #4 Description
  3. Reading: Application #4 Solution
  4. Video: Class wrap-up

Graded: Assignment: Computing Alignments of Sequences
Graded: Applications to Genomics and Beyond

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