![]() ![]() ![]() We are going to be using it and it's very important that you feel comfortable with reading pseudo-code. Okay? So pseudo-code is very important, you are going to be seeing it throughout the course. It gives you wiggle room for using structures or using English in them, but is not like English that it is ambiguous or not structured. It is somewhere between a formal programming language that has exact syntax that you have to use, and English or any other natural language, in the sense that it is not as formal as a programming language. So what is pseudo-code and why do we want to use and why not just stick to Python? So pseudo-code is a language that is very powerful for describing algorithms. However, you will notice that when I'm describing algorithms or when there are questions about algorithms the mathematical algorithms on the homework, you'll see that I will be using or requiring the use of a, of a certain program or language that we call pseudo code. So in this course, you and when we ask you to do the programming part you will be writing the code in Python. 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". Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. 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. 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. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. 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. Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. ![]()
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