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CIT310Sciences2 Unitsintermediate

Algorithms and Complexity Analysis

This course provides an overview of computer algorithms and complexity analysis. Emphasis is placed on understanding computer algorithms, algorithm development, and testing before translation into programs. Students will learn algorithm design paradigms, including divide-and-conquer, greedy techniques, and dynamic programming. The course covers basic algorithm analysis, searching and sorting algorithms, and other algorithm techniques, including binary search trees and approximate algorithms. The course aims to equip students with the skills to design and analyze efficient algorithms.

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182h

Study Time

13

Weeks

14h

Per Week

intermediate

Math Level

Course Keywords

AlgorithmsComplexity AnalysisSortingSearchingData Structures

Course Overview

Everything you need to know about this course

Course Difficulty

Intermediate Level

Builds on foundational knowledge

65%

intermediate

📊

Math Level

Moderate Math

🔬

Learning Type

Hands-on Practice

Course Topics

Key areas covered in this course

1

Algorithm Concepts

2

Complexity Analysis

3

Algorithm Design Techniques

4

Searching Algorithms

5

Sorting Algorithms

6

Binary Search Trees

7

Dynamic Programming

8

Computational Complexity

9

Approximate Algorithms

Total Topics9 topics

Ready to Start

No specific requirements needed

This course is designed to be accessible to all students. You can start immediately without any prior knowledge or specific preparation.

Assessment Methods

How your progress will be evaluated (3 methods)

Self-Assessment Exercises

Comprehensive evaluation of course material understanding

Written Assessment

Tutor-Marked Assignments

Comprehensive evaluation of course material understanding

Written Assessment

Final Examination

Comprehensive evaluation of course material understanding

Written Assessment

Career Opportunities

Explore the career paths this course opens up for you

Software Developer

Apply your skills in this growing field

Algorithm Engineer

Apply your skills in this growing field

Data Scientist

Apply your skills in this growing field

Computer Scientist

Apply your skills in this growing field

Software Architect

Apply your skills in this growing field

Industry Applications

Real-world sectors where you can apply your knowledge

Software DevelopmentData ScienceArtificial IntelligenceMachine LearningDatabase ManagementNetwork Optimization

Study Schedule Beta

A structured 13-week journey through the course content

Week

1

Module 1: Basic Algorithm Analysis

1.5h

Unit 1: Basic Algorithm Concepts

1.5 study hours

•Read Unit 1: Basic Algorithm Concepts, focusing on the definition and characteristics of algorithms.
•Understand the advantages and disadvantages of algorithms.
•Differentiate between algorithms and pseudocode.
•Solve self-assessment exercises to reinforce understanding of basic concepts.

Week

2

Module 1: Basic Algorithm Analysis

2h

Unit 2: Analysis and Complexity of Algorithms

2 study hours

•Study Unit 2: Analysis and Complexity of Algorithms, paying attention to time and space complexity.
•Differentiate between worst-case, average-case, and best-case time complexity.
•Understand typical complexities of algorithms (constant, logarithmic, linear, etc.).
•Work through examples to approximate the time taken by an algorithm.

Week

3

Module 1: Basic Algorithm Analysis

2h

Unit 3: Algorithm Design Techniques

2 study hours

•Review Unit 3: Algorithm Design Techniques, focusing on divide-and-conquer, greedy techniques, and dynamic programming.
•Understand asymptotic notations (Big O, Big Omega, Big Theta).
•Apply asymptotic notations to analyze algorithm efficiency.
•Practice problems to identify appropriate algorithm design techniques for different scenarios.

Week

4

Module 1: Basic Algorithm Analysis

2h

Unit 4: Recursion and Recursive Algorithms

2 study hours

•Study Unit 4: Recursion and Recursive Algorithms, focusing on base cases and recursive calls.
•Understand different types of recursion (direct, indirect, tail, head).
•Compare recursion with iteration.
•Implement recursive algorithms for reversing an array and generating Fibonacci sequences.

Week

5

Module 1: Basic Algorithm Analysis

2h

Unit 5: Recurrence Relations

2 study hours

•Study Unit 5: Recurrence Relations, focusing on methods for resolving recurrence relations.
•Understand the guess-and-verify, iteration, recursion tree, and master methods.
•Apply recurrence relations to analyze the Tower of Hanoi problem.
•Solve recurrence relations to determine algorithm complexity.

Week

6

Module 2: Searching and Sorting Algorithms

2h

Unit 1: Bubble Sort and Selection Sort Algorithm

2 study hours

•Study Unit 1: Bubble Sort and Selection Sort Algorithm, focusing on the working principles of each algorithm.
•Analyze the time and space complexity of bubble sort and selection sort.
•Compare the advantages and disadvantages of each algorithm.
•Implement bubble sort and selection sort in a programming language of your choice.

Week

7

Module 2: Searching and Sorting Algorithms

2h

Unit 2: Insertion Sort and Linear Search Algorithms

2 study hours

•Study Unit 2: Insertion Sort and Linear Search Algorithms, focusing on the working principles of each algorithm.
•Analyze the time and space complexity of insertion sort and linear search.
•Compare the advantages and disadvantages of each algorithm.
•Implement insertion sort and linear search in a programming language of your choice.

Week

8

Module 2: Searching and Sorting Algorithms

2h

Unit 3: Radix Sort and Stability in Sorting

2 study hours

•Study Unit 3: Radix Sort and Stability in Sorting, focusing on the working principles of radix sort.
•Understand the concept of stability in sorting algorithms.
•Analyze the time and space complexity of radix sort.
•Compare the advantages and disadvantages of radix sort.

Week

9

Module 2: Searching and Sorting Algorithms

2h

Unit 4: Divide-and-Conquer Strategies I: Binary Search

2 study hours

•Study Unit 4: Divide-and-Conquer Strategies I: Binary Search, focusing on the divide-and-conquer paradigm.
•Understand the working principle of binary search.
•Analyze the time and space complexity of binary search.
•Compare the advantages and disadvantages of binary search.

Week

10

Module 2: Searching and Sorting Algorithms

2h

Unit 5: Divide-and-Conquer Strategies II: Merge Sort and Quicksort Algorithms

2 study hours

•Study Unit 5: Divide-and-Conquer Strategies II: Merge Sort and Quicksort Algorithms, focusing on the working principles of merge sort and quicksort.
•Analyze the time and space complexity of merge sort and quicksort.
•Compare the advantages and disadvantages of each algorithm.
•Implement merge sort and quicksort in a programming language of your choice.

Week

11

Module 3: Other Algorithm Techniques

2h

Unit 1: Binary Search Trees

2 study hours

•Study Unit 1: Binary Search Trees, focusing on the properties of binary search trees.
•Understand different traversal methods (inorder, preorder, postorder).
•Learn how to query a binary search tree (search, minimum, maximum, successor, predecessor).
•Study insertion and deletion operations in binary search trees.

Week

12

Module 3: Other Algorithm Techniques

4h

Unit 2: Dynamic Programming

2 study hours

•Study Unit 2: Dynamic Programming, focusing on the principles of dynamic programming.
•Understand top-down and bottom-up approaches.
•Compare dynamic programming with divide-and-conquer.
•Apply dynamic programming to solve optimization problems.

Unit 3: Computational Complexity

2 study hours

•Study Unit 3: Computational Complexity, focusing on deterministic and non-deterministic algorithms.
•Understand P and NP problems.
•Learn about NP-hard and NP-complete problems.
•Differentiate between tractable and intractable problems.

Week

13

Module 3: Other Algorithm Techniques

4h

Unit 4: Approximate Algorithms I

2 study hours

•Study Unit 4: Approximate Algorithms I, focusing on the concept of approximation algorithms.
•Understand performance ratios.
•Learn about the vertex cover and traveling salesman problems.
•Apply approximation algorithms to find near-optimal solutions.

Unit 5: Approximate Algorithms II

2 study hours

•Study Unit 5: Approximate Algorithms II, focusing on methods for finding minimum spanning trees (Kruskal's and Prim's algorithms).
•Understand the steps for implementing Kruskal's and Prim's algorithms.
•Apply these algorithms to find minimum spanning trees in graphs.
•Compare the advantages and disadvantages of Kruskal's and Prim's algorithms.

This study schedule is in beta and may not be accurate. Please use it as a guide and consult the course outline for the most accurate information.

Course PDF Material

Read the complete course material as provided by NOUN.

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Study Tips & Exam Preparation

Expert tips to help you succeed in this course

1

Review all units, focusing on key concepts and definitions.

2

Practice solving problems related to algorithm analysis and complexity.

3

Implement and test searching and sorting algorithms.

4

Work through examples of dynamic programming problems.

5

Understand the differences between P, NP, NP-hard, and NP-complete problems.

6

Study the steps involved in Kruskal's and Prim's algorithms.

7

Create concept maps linking different algorithm design techniques.

8

Practice solving recurrence relations to determine algorithm complexity.

9

Review all Tutor-Marked Assignments (TMAs) and self-assessment exercises.

10

Allocate specific time slots for focused study sessions each week.

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