Version: 0.3.38
Course Overview#
Are we going to go through a never-ending list of algorithms and data-structure? In a sense yes, but bear in mind that the point of the course is to practice solving problems. Algorithms and data structures are just a means to that.
Syllabus#
Figure 2 details the topics we will cover. My strategy is to start with the concepts and theories and then to go through the data structures and algorithms that have been the most relevant for me. The two first weeks focus on the basics: What is an algorithm, what is a program, how does a computer executes a program, how to measure time and memory consumption? etc. Then we apply these again and again on basic algorithms and data structures: Records, arrays, lists, recursion and sorting.
Fig. 1 The different modules we go through in 13 weeks#
Then, we push on to more advanced concepts, including hashing (Week 7), trees (Week 8, 9) and graphs (Week 10). These are all the data structures we will study but be sure there are many more. In the remainder we will explore two other topics where algorithms play a key role: Combinatorial search and regular expressions. Finally we will wrap up with looking at two other computation models: Parallel computing and Quantum computing. The knowledge we got is very general and apply there as well.
Caution
This course is hard: Learning to solve computational problems efficiently takes practice.
As you will see this course is “content-heavy”. I don’t think one can master the whole thing in a week or so before the final examination. A strongly recommend you spread your work over the 13 weeks.
Why to Study This?#
As you may already sense, this is the foundations of Computer Science, and in turn, of Software Engineering. Image processing, cryptography, compilers, networks, artificial intelligence, and other “branches” of Computer Science all develop algorithms and data structures. Consider image processing as an example: How to detect the contour of a shape in a bitmap? What data structure is the most suitable to represent an image in memory? Which algorithm is the fastest? Which consume the least memory? This course lay down the foundations to discuss, evaluate and compare algorithms and data structures.
Besides, as a software engineer, your daily work is to solve computation problems! Say we have to sort the score of the top players in your new mobile game, for instance. Shall we go for a quick sort or a radix sort? Should we roll out something of our own? It is critical to know what already exists and where we it shines and where it falls short.
Learning Outcomes#
How will this course contribute to your career? By developing competencies, skills and knowledge.
Competence#
Again, the point is to develop a single competence: Solving computational problems, by designing algorithms and programs that a machine can execute. This is difficult. This is not something we learn and recite: One must practice.
Skills#
As we practice, we will build three core skills:
Design algorithms and data structures tailored for a specific problem.
Argue for their correctness and be confident that the solution we come up with actually solves the problem at hand.
Compare the time and memory consumed by our algorithms to alternative solutions.
Knowledge#
This course contains a lot of knowledge, especially in the form of known problems and their accepted solutions, which combine algorithms and data structures. But this course is not exhaustive at all! It is just an introduction, whose aim is only to establish the shared vocabulary and culture to be effective as a professional. We will cover the basics: Arrays, lists, searching, sorting, trees, graphs, hashing, etc.
Important
The single objective of this course is to train you at solving computational problems. This is hard enough.