About this course
By the end of this course, you'll be able to write programs in Python that leverage your more natural understanding of data structures by creating objects to represent the structures you work with most often. For example, if you were creating a class roster application, you'll learn how to create an object representing a student's name, ID number, and attendance record. Then, you'll be able to create applications that leverage sorting and searching algorithms to sort that roster alphabetically, search for a particular student, and evaluate the efficiency of both those operations.
Structurally, the course is comprised of several parts. Instruction is delivered via a series of short (2-3 minute) videos. In between those videos, you'll complete both multiple choice questions and coding problems to demonstrate your knowledge of the material that was just covered. These exercises count for 20% of your grade. Then, after each major chapter, you'll complete a problem set of collected, more challenging problems. These count for 40% of your grade. Finally, you'll complete a final course exam, which counts for the remaining 40% of your grade.
What you'll learn
- Working with instances of objects in Python.
- Creating new data structures using object-oriented programming.
- Using objects with earlier control and data structures.
- Writing common search algorithms, like linear and binary search.
- Writing common sorting algorithms, like bubble sort, insertion sort, and merge sort.
- Evaluating the computational complexity of algorithms using Big O notation.
Chapter 2. Algorithms. Creating complex code for searching in large lists or sorting lists of data, and analyzing code for its complexity.
Chapter 3. Course Recap. A comprehensive review of the Xseries as a whole, leading into the final exam.
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