Compsci 311 Course Syllabus and Overview

Compsci 311 is a comprehensive course that covers the fundamental concepts of computer science, including data structures, algorithms, and software engineering. The course is designed to help students develop problem-solving skills and learn how to write efficient and effective code.

The course syllabus outlines the key topics that will be covered, including data structures such as arrays, linked lists, and stacks, as well as algorithms like sorting and searching. These topics will be explored through a combination of lectures, assignments, and projects.

Throughout the course, students will work on a series of projects that focus on software engineering principles, including design patterns, testing, and debugging. By the end of the course, students will have a solid understanding of computer science concepts and be able to apply them to real-world problems.

The course is taught by experienced instructors who are passionate about computer science and committed to helping students succeed.

Compsci 311 is a challenging course, but it's designed to help you develop a strong foundation in data structures and algorithms.

The course is the second half of a two-course sequence, building on the concepts learned in COSC-211, Data Structures. You can expect to cover a range of topics, including sorting and selection, divide-and-conquer algorithms, greedy algorithms, dynamic programming, network flow, and NP-completeness.

Much of the course content is theoretical, focusing on understanding what it means for an algorithm to be efficient, learning how to analyze algorithms' efficiency, and exploring what to do when we cannot design efficient algorithms.

The course goals are ambitious, but achievable with dedication and hard work.

Here are the specific goals you can expect to achieve by the end of the course:

This course, Compsci 311, is a continuation of the first half, Data Structures. It covers topics like sorting and selection, divide-and-conquer algorithms, and NP-completeness. The course is theoretical, focusing on understanding what makes an algorithm efficient and how to analyze its efficiency.

The course goals are to recognize standard computational problems and their algorithms, develop familiarity with key algorithmic paradigms, and gain experience applying these paradigms to unfamiliar problems. You'll also advance your ability to communicate complex technical ideas clearly.

Students can expect to learn about greedy algorithms, dynamic programming, and network flow. These topics are essential for designing efficient algorithms, which is a crucial skill in computer science. By the end of the course, you'll understand the basics of efficient algorithm design and be able to apply them in specific instances.

Here are the key areas of focus in this course:

Algorithms are recipes for solving computational problems, like finding the cheapest ticket from Boston to Bombay or programming a robot to navigate in an environment filled with obstacles.

The course will cover a variety of topics, including sorting, searching, dynamic programming, greedy algorithms, graph algorithms, string processing, computational geometry, and NP-completeness.

You'll learn the basic principles underlying the field and be able to apply them in specific instances upon completion of the course.

Given the time limitations, the course will only be able to skim the surface of each subject, but that's okay - you'll still get a solid understanding of the basics.

Algorithms are used to solve real-world problems, and the course will focus on introducing the basic techniques used to design and analyze efficient algorithms.

The goal of the course is to give you a broad understanding of the field, so you can apply what you've learned to a wide range of applications.

In Compsci 311, you'll be expected to present your results clearly and concisely, making effective use of the Jupyter Notebook environment to provide explanatory text alongside code and outputs.

Correctness is necessary, but not sufficient, as you'll be judged on how well you tell a compelling story about the data. This means your notebook should explain why a given bit of analysis is done, and what it means, without leaving the reader to guess.

You'll be expected to adapt to unexpected complications that may arise with the data, and propose a path forward. This could involve explaining the complications and proposing a plan to achieve the original goals, or suggesting alternative goals that are achievable and align with the spirit of the original goals.

Here are the key implications for assessment in Compsci 311:

The grading policy in this course is quite straightforward. You'll be assessed based on four main components: a midterm exam, a final exam, homeworks, and a project.

Each of these components carries a specific weightage: homeworks account for 20% of your grade, the project is worth 10%, the midterm exam is worth 30%, and the final exam is worth 40%.

To give you a better idea of how your grades will be calculated, here's a breakdown of the grading scale:

Your final grade will be determined based on this absolute grading scale, so make sure to aim for the highest scores possible!

Collaboration is allowed in labs, but copying code or text is not. You can discuss lab assignments with classmates, but your code and text must be original.

Some labs are done individually, while others are done in pairs. In pair labs, any collaboration between pair members is permissible, but both members must understand and explain their submission in detail.

Pair programming is recommended to avoid splitting tasks and independent completion. This ensures both pair members can explain their submission.

Looking at someone else's code can lead to accidental identical code, so it's best to avoid it or wait at least 30 minutes before writing your own code.

You can prove understanding of a solution by explaining it in your notebook.

In a professional setting, correctness is not enough. You need to present your results clearly and concisely.

A data scientist's job is to extract insights from real data, and that means communicating those insights effectively. This is where the Jupyter Notebook environment comes in, allowing you to provide explanatory text alongside code and outputs.

To tell a compelling story about the data, your notebook should explain why a given bit of analysis is done and what it means. This is not just about getting the right answer, but also about making it easy for others to understand your process.

Expand your knowledge: Compsci 61b

If reality doesn't align with what your boss or advisor has asked of you, don't be afraid to explain the complications you've encountered with the data. Propose a path forward, whether that's a plan to achieve the original goals with more time or a different approach, or a proposal for alternative goals that are achievable and align with the spirit of the original goals.

In any real-world setting, you'll need to adapt to changing circumstances and unexpected challenges. This is where your ability to think critically and propose solutions will be essential.

Here are some key implications of this approach to assessment:

The final project is a significant component of our assessment and evaluation process. It will be completed in groups of 2 or 3 students.

You'll be working together with your peers to deliver a comprehensive project that includes a proposal, milestone reports, a final report, and a presentation. Presentations will take place during our scheduled final exam slot.

Compsci 311 is a comprehensive course that covers the fundamentals of computer science. It's designed to be completed in one semester.

The course structure is divided into four main modules: Data Structures, Algorithms, Computer Systems, and Software Engineering. Each module is carefully crafted to build upon the previous one.

You can expect to spend around 12 weeks on the course, with two hours of lectures and one hour of tutorials per week. This will give you a good balance of theory and practical application.

The course is designed to be challenging, but manageable with regular effort. You'll need to dedicate around 10 hours per week to complete the coursework and assignments.

The coursework is divided into 40% individual assignments, 30% group projects, and 30% final exam. This will help you develop your problem-solving skills, work in teams, and demonstrate your knowledge under timed conditions.

You'll have access to a range of resources, including online tutorials, discussion forums, and office hours with the instructor. These will help you stay on top of the coursework and get support when you need it.