Course Summary
The Algorithmic Toolbox course on Coursera is designed to teach students the fundamental principles of algorithm design and analysis. It covers a wide range of topics, including data structures, dynamic programming, and graph algorithms.Key Learning Points
- Learn how to design and analyze algorithms
- Explore a variety of data structures and algorithms
- Gain practical experience through programming assignments
Job Positions & Salaries of people who have taken this course might have
- USA: $95,000 - $120,000
- India: ₹500,000 - ₹1,200,000
- Spain: €30,000 - €50,000
- USA: $95,000 - $120,000
- India: ₹500,000 - ₹1,200,000
- Spain: €30,000 - €50,000
- USA: $100,000 - $150,000
- India: ₹600,000 - ₹2,000,000
- Spain: €35,000 - €60,000
- USA: $95,000 - $120,000
- India: ₹500,000 - ₹1,200,000
- Spain: €30,000 - €50,000
- USA: $100,000 - $150,000
- India: ₹600,000 - ₹2,000,000
- Spain: €35,000 - €60,000
- USA: $80,000 - $120,000
- India: ₹400,000 - ₹1,200,000
- Spain: €25,000 - €45,000
Related Topics for further study
Learning Outcomes
- Understand the fundamental principles of algorithm design and analysis
- Develop practical skills in designing and analyzing algorithms
- Apply your knowledge to real-world programming problems
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge in any language
- Knowledge of basic mathematics such as calculus and discrete math
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Interactive
Similar Courses
- Introduction to Algorithms
- Algorithmic Thinking (Part 1)
- Data Structures and Algorithms Specialization
Related Education Paths
Notable People in This Field
- Donald Knuth
- Jon Kleinberg
Related Books
Description
The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).
Knowledge
- Essential algorithmic techniques
- Design efficient algorithms
- Practice solving algorithmic interview problems
- Implement efficient and reliable solutions
Outline
- Programming Challenges
- Welcome!
- Solving the Sum of Two Digits Programming Challenge (screencast)
- Solving the Maximum Pairwise Product Programming Challenge: Improving the Naive Solution, Testing, Debugging
- Stress Test - Implementation
- Stress Test - Find the Test and Debug
- Stress Test - More Testing, Submit and Pass!
- Companion MOOCBook
- What background knowledge is necessary?
- Optional Videos and Screencasts
- Alternative testing guide in Python
- Maximum Pairwise Product Programming Challenge
- Using PyCharm to solve programming challenges
- Acknowledgements
- Solving Programming Challenges
- Algorithmic Warm-up
- Why Study Algorithms?
- Coming Up
- Problem Overview
- Naive Algorithm
- Efficient Algorithm
- Problem Overview and Naive Algorithm
- Efficient Algorithm
- Computing Runtimes
- Asymptotic Notation
- Big-O Notation
- Using Big-O
- Course Overview
- Resources
- Resources
- Resources
- Logarithms
- Big-O
- Growth rate
- Greedy Algorithms
- Largest Number
- Car Fueling
- Car Fueling - Implementation and Analysis
- Main Ingredients of Greedy Algorithms
- Celebration Party Problem
- Efficient Algorithm for Grouping Children
- Analysis and Implementation of the Efficient Algorithm
- Long Hike
- Fractional Knapsack - Implementation, Analysis and Optimization
- Review of Greedy Algorithms
- Resources
- Puzzle: Largest Number
- Puzzle: Car Fueling
- Greedy Algorithms
- Fractional Knapsack
- Puzzle: Balls in boxes
- Puzzle: Activity Selection
- Puzzle: Touch All Segments
- Divide-and-Conquer
- Intro
- Linear Search
- Binary Search
- Binary Search Runtime
- Problem Overview and Naïve Solution
- Naïve Divide and Conquer Algorithm
- Faster Divide and Conquer Algorithm
- What is the Master Theorem?
- Proof of the Master Theorem
- Problem Overview
- Selection Sort
- Merge Sort
- Lower Bound for Comparison Based Sorting
- Non-Comparison Based Sorting Algorithms
- Overview
- Algorithm
- Random Pivot
- Running Time Analysis (optional)
- Equal Elements
- Final Remarks
- Resources
- Resources
- Resources
- Resources
- Resources
- Linear Search and Binary Search
- Puzzle: 21 questions game
- Puzzle: Two Adjacent Cells of Opposite Colors
- Polynomial Multiplication
- Master Theorem
- Sorting
- Quick Sort
- Puzzle: Local Maximum
- Dynamic Programming 1
- Change Problem
- The Alignment Game
- Computing Edit Distance
- Reconstructing an Optimal Alignment
- Resources
- Resources
- Additional Slides
- Change Money
- Puzzle: Number of Paths
- Puzzle: Two Rocks Game
- Puzzle: Three Rocks Game
- Edit Distance
- Puzzle: Primitive Calculator
- Dynamic Programming 2
- Problem Overview
- Knapsack with Repetitions
- Knapsack without Repetitions
- Final Remarks
- Problem Overview
- Subproblems
- Algorithm
- Reconstructing a Solution
- Resources
- Knapsack
- Maximum Value of an Arithmetic Expression
Summary of User Reviews
Learn algorithmic toolbox from Coursera and improve your skills in programming, algorithms, and computational thinking. Users highly recommend this course for its comprehensive content and interactive exercises.Pros from User Reviews
- Comprehensive content that covers a wide range of topics
- Interactive exercises and assignments to practice and apply concepts
- Great course structure and pacing
- Engaging and knowledgeable instructors
- Suitable for beginners and intermediate learners
Cons from User Reviews
- Some users found the course challenging and required more time to complete
- Some lectures were too theoretical and lacked practical examples
- Some users experienced technical difficulties with the platform
- Not suitable for advanced learners looking for more in-depth content
- Some users found the course too basic
Alexander S. Kulikov
4.6 Raiting
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Implement searching and sorting Algorithms in Python and learn how they work through engaging animations and projects. Learn how to implement Searching and Sorting algorithms in Python....
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The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming....
