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AD Become a Data Scientist datacamp. Start now for free! Overview Sign up to Coursera courses for free Learn how 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 course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems.
In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python 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.
Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
Tags python usa north america algorithms core. Browse More Coursera Articles. Browse More Algorithms and Data Structures courses. This course is the first part of a somewhat challenging conclusion to the specialization. The course is split into several two-week long components, during which you will have to complete a quiz and a machine-graded piece of code. Finally you will have to write additional code and answer somewhat challenging questions about the week's application.
This includes preparing graphs of the runtimes of algorithms or their effectiveness. As someone with a background in maths, I found this course an excellent way to apply my current knowledge to a programming environment. The applications are extremely interesting and have relevance. The code you write is short, but often you have to write it very thoughtfully. I'm somewhat ashamed to admit that for one of the applications I had to leave my computer running for a few hours because I failed to adequately optimize my code.
Sometimes the programming can be a little bit confusing, although perhaps this is due to my inexperience with Python. For example for one assignment they provide some code for a custom function that is several lines long, when I believe it is possible to perform the same task in one line.
The lectures help to some extent with some of these concerns, but
The best russian mature part1 not perfect. Was this review helpful to you? I took this course back when it was one course, more than a year ago so some things may have changed since.
This course is mainly built on understanding maths and not coding, unlike the previous courses in the specialization. The point of the course is understanding algorithmic efficiency of an algorithm. The professor Luay Nakhleh is not my favourite teacher. I understood what he taught in his videos, however often that wasn't enough to finish the exam. I should have learned or relearned math which wasn't part of the course. They provided some external links but fo….
They provided some external links but for me that wasn't enough, although I did well in the previous courses. The weekly exam had three parts. A test, some coding where "The best russian mature part1" algorithm was given, The best russian mature part1 we had to make a plot, with mathplotlib or the plotting library of our choice, from the efficiency of that algorithm. There were two weeks to finish these, so I suggest not to procrastinate because if you're like me you won't have enough time.
Good course - comparable to the same professors' IIPP, but a little rougher round the edges - with a fairly challenging final project.
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