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There are 4 modules in 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.
Sorting, searching, big-O notation, the Master Theorem
What's included
13 videos2 readings1 assignment
Show info about module content
13 videos•Total 147 minutes
What is Algorithmic Thinking? •9 minutes
The sorting problem •12 minutes
A simple quadratic algorithm •10 minutes
Illustrating MergeSort •13 minutes
The recurrence for MergeSort •10 minutes
The Master Theorem and MergeSort efficiency •12 minutes
Linear vs. binary search •12 minutes
Efficiency of binary search •11 minutes
Class structure (from part 1)•10 minutes
Coding styles and standards - PoC•12 minutes
Testing and machine grading - PoC•11 minutes
Plotting data - PoC•14 minutes
Peer assessment - "We want a shrubbery!" - IIPP•11 minutes
2 readings•Total 20 minutes
Class notes•10 minutes
Coding notes•10 minutes
1 assignment•Total 30 minutes
Homework #3•30 minutes
Module 3 - Project and Application
Module 2•5 hours to complete
Module details
Closest pairs of points, clustering of points, comparison of clustering algorithms
What's included
4 readings1 peer review2 app items
Show info about module content
4 readings•Total 40 minutes
Project #3 Description•10 minutes
Tests and Tips for Implementing the Clustering Methods•10 minutes
Application #3 Description•10 minutes
Application #3 Solution•10 minutes
1 peer review•Total 120 minutes
Comparison of Clustering Algorithms•120 minutes
2 app items•Total 120 minutes
Assignment: Closest Pairs and Clustering Algorithms•60 minutes
Project Submission History•60 minutes
Module 4 - Core Materials
Module 3•2 hours to complete
Module details
Dynamic programming, running time of DP algorithms, local and global sequence alignment
What's included
7 videos1 assignment
Show info about module content
7 videos•Total 87 minutes
The RNA secondary structure problem •16 minutes
A dynamic programming algorithm •14 minutes
Illustrating the DP algorithm •12 minutes
Running time of the DP algorithm •8 minutes
DP vs. recursive implementation •14 minutes
Global pairwise sequence alignment •15 minutes
Local pairwise sequence alignment •8 minutes
1 assignment•Total 30 minutes
Homework 4•30 minutes
Module 4 - Project and Application
Module 4•3 hours to complete
Module details
Computation of sequence alignments, applications to genomics and text comparison
What's included
1 video3 readings1 peer review1 app item
Show info about module content
1 video•Total 8 minutes
Class wrap-up •8 minutes
3 readings•Total 30 minutes
Project #4 Description•10 minutes
Application #4 Description•10 minutes
Application #4 Solution•10 minutes
1 peer review•Total 60 minutes
Applications to Genomics and Beyond•60 minutes
1 app item•Total 60 minutes
Assignment: Computing Alignments of Sequences•60 minutes
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Learner reviews
4.7
220 reviews
5 stars
80.45%
4 stars
10.90%
3 stars
4.54%
2 stars
2.27%
1 star
1.81%
Showing 3 of 220
R
RL
5·
Reviewed on Mar 24, 2018
Great class...Luay's lectures and problem sets were a great continuation to what Joe and Scott started. I suppose I will get started on Course 7 shortly.
J
JB
5·
Reviewed on Sep 22, 2016
You cannot get easy answers for homework and it pushes you to think hard.
J
JO
5·
Reviewed on Apr 28, 2018
Excellent class in the series. Even if computational biology is not your thing, the assignments are really interesting, fun and informative.
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