
Algorithmic Thinking (Part 2)

Algorithmic Thinking (Part 2)
This course is part of Fundamentals of Computing Specialization



Instructors: Luay Nakhleh
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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
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
Closest pairs of points, clustering of points, comparison of clustering algorithms
What's included
4 readings1 peer review2 app items
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
- Project Submission History•60 minutes
- Assignment: Closest Pairs and Clustering Algorithms•60 minutes
Dynamic programming, running time of DP algorithms, local and global sequence alignment
What's included
7 videos1 assignment
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
Computation of sequence alignments, applications to genomics and text comparison
What's included
1 video3 readings1 peer review1 app item
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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Reviewed on Sep 22, 2016
You cannot get easy answers for homework and it pushes you to think hard.
Reviewed on Jul 25, 2018
Course and assignments were very well thought out and informative.
Reviewed on May 26, 2016
Super good, challenging course that forces you to think.
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