An Introduction to Algorithmics from Pluralsight

An introductory guided tour to the field of data structures, algorithms, and complexity analysis. This course is loaded with a ton of practical examples, and focuses on intuition, rather than formulas and mathematical proofs.

The phrase "Get Great Performance for Free!" sounds like a quote from bad commercial, but when it comes to algorithms and data structures, that may actually be the case. This introductory course shows how the use of common data structures may simplify and even significantly impact performance of solutions to typical real-life everyday programming problems. The course gently introduces the viewer for "complexity analysis" which makes it possible to spot a poorly (and a great) performing program, even without the need for executing it. Complexity analysis is an invaluable tool or "language" for discussing performance with colleagues - and it's not even difficult. After having covered the most common data structures, the course continues to describe some general strategies (algorithms) to efficiently solve more high-level problems. Like with data structures, it is shown how a careful choice of problem solving strategy can dramatically reduce computation time. The last part of the course shifts the focus a bit and shortly teases a few popular theoretical subjects and explains, at a purely intuitive level, what the complexity classes P, NP, and the famous problem, P = NP, is all about.

What's inside

Syllabus

Introduction to Algorithms

Measuring Performance

Organizing Data Efficiently with Common Data Structures

Operating on Data Efficiently with Common Algorithms

Looking Ahead to Some Very Hard Problems

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Focuses on the practical application of data structures and algorithms, making them accessible to everyday programming problems

Provides a clear understanding of complexity analysis, a crucial tool for evaluating performance in real-world scenarios

Intuition-based approach makes the subject more approachable for learners with varying levels of mathematical experience

Introduces fundamental concepts and algorithms, building a solid foundation for further exploration in the field

Focuses on key data structures and algorithms, providing a practical foundation for implementing efficient solutions

Taught by experienced instructor Rasmus Amossen, known for his expertise in algorithms and data structures

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in An Introduction to Algorithmics with these activities:

Review Binary Trees and Recursion

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Refresh your understanding of Binary Trees and Recursion before taking this course to ensure a smooth transition.

Browse courses on Binary Trees

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Read through the notes and practice problems from your previous data structures course.
Solve problems relating to recursion and binary trees on LeetCode or similar platforms.
Go through online tutorials or video lectures on binary trees and recursion.

Join a Study Group

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Engage with peers in study groups or online forums to discuss concepts, share insights, and reinforce your understanding.

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Find or create a study group with classmates or online learners.
Schedule regular meetings to discuss course material, solve problems, and provide support.

Read 'Introduction to Algorithms'

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Supplement your knowledge by reading 'Introduction to Algorithms' to gain a deeper understanding of core concepts.

View Introduction to Algorithms, fourth edition on Amazon

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Read chapters 1-4 to familiarize yourself with basic data structures and algorithms.
For a deeper dive, cover chapters 5-9 on algorithm design techniques.

Five other activities

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Watch 'Algorithms Illuminated' video series

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Enhance your understanding by watching 'Algorithms Illuminated' videos, which provide clear and intuitive explanations of algorithms.

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Watch the videos corresponding to topics covered in each module of the course.
Take notes and pause the videos to reflect on the concepts being explained.

Practice LeetCode Problems

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Solve problems on LeetCode to hone your problem-solving skills and reinforce concepts covered in the course.

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Select problems tagged with 'Easy' or 'Medium' difficulty.
Focus on understanding the problem statement and devising an efficient algorithm.
Implement your solution and submit it for evaluation.

Attend Algorithm Design Workshops

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Attend workshops to learn specific algorithm design techniques and enhance your problem-solving abilities.

Browse courses on Algorithm Design

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Research and identify workshops that focus on algorithm design and problem-solving.
Register for workshops that align with your interests and skill level.
Actively participate in the workshops and engage with experts and peers to maximize learning.

Develop a Visual Aid

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Create visual aids such as diagrams, mind maps, or flowcharts to enhance your understanding and retention of key concepts.

Browse courses on Visual Aids

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Choose a topic or concept that you find challenging.
Design a visual aid that effectively illustrates the concept.
Refer to your visual aid while studying and reviewing material.

Contribute to Open Source Projects

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Gain practical experience and contribute to the community by participating in open-source projects related to algorithms and data structures.

Browse courses on Open Source

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Identify open-source projects that align with your interests and skill level.
Review the project documentation and contribute bug fixes or feature enhancements.
Engage with the project community to learn from others and share your knowledge.

Career center

Learners who complete An Introduction to Algorithmics will develop knowledge and skills that may be useful to these careers:

Algorithm Engineer

Algorithm Engineers design and analyze algorithms. They work on a wide range of problems, including sorting, searching, and optimization. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for all Algorithm Engineers to understand. Additionally, the course's focus on complexity analysis can help Algorithm Engineers understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Algorithm Engineer

Database Administrator

Database Administrators design and manage databases. They work with a variety of database technologies to ensure that databases are available, reliable, and secure. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for working with large datasets. Additionally, the course's focus on complexity analysis can help Database Administrators understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Database Administrator

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. They work with a variety of industries to help them make decisions about how to improve efficiency and productivity. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for working with large datasets. Additionally, the course's focus on complexity analysis can help Operations Research Analysts understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Operations Research Analyst

Data Engineer

Data Engineers design and build systems for storing and managing data. They work with a variety of data sources and technologies to ensure that data is available, reliable, and secure. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for working with large datasets. Additionally, the course's focus on complexity analysis can help Data Engineers understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They work with a variety of data sources and technologies to identify investment opportunities and manage risk. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for working with large datasets. Additionally, the course's focus on complexity analysis can help Quantitative Analysts understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk. They work with a variety of insurance and financial companies to help them make decisions about how to manage risk. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for working with large datasets. Additionally, the course's focus on complexity analysis can help Actuaries understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Actuary

Computer Scientist

Computer Scientists conduct research and design new computer technologies. They work on a wide range of topics, including algorithms, data structures, operating systems, and programming languages. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for all Computer Scientists to understand. Additionally, the course's focus on complexity analysis can help Computer Scientists understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Computer Scientist

Software Developer

Software Developers design and build software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for writing efficient and effective code. Additionally, the course's focus on complexity analysis can help Software Developers understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Software Developer

Data Scientist

Data Scientists use data to solve problems and make predictions. They work with large and complex datasets, and they use a variety of statistical and machine learning techniques to extract insights from the data. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for working with large datasets. Additionally, the course's focus on complexity analysis can help Data Scientists understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and build machine learning models. These models are used to make predictions and decisions, and they are used in a wide variety of applications, such as self-driving cars, facial recognition, and medical diagnosis. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for building efficient and effective machine learning models. Additionally, the course's focus on complexity analysis can help Machine Learning Engineers understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make better decisions. They use a variety of statistical and machine learning techniques to identify trends and patterns in the data. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for working with large datasets. Additionally, the course's focus on complexity analysis can help Data Analysts understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Data Analyst

Management Consultant

Management Consultants help businesses solve problems and improve performance. They work with a variety of industries to help them make decisions about how to improve strategy, operations, and technology. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for working with large datasets. Additionally, the course's focus on complexity analysis can help Management Consultants understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Management Consultant

Software Engineer

Software Engineers design and build the software that we use every day. These professionals are responsible for the code that powers websites, apps, and even self-driving cars. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for writing efficient and effective code. Additionally, the course's focus on complexity analysis can help Software Engineers understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Software Engineer

Business Analyst

Business Analysts help businesses analyze their operations and identify opportunities for improvement. They work with a variety of stakeholders to define the business need, gather data, and develop recommendations. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for working with large datasets. Additionally, the course's focus on complexity analysis can help Business Analysts understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers lead the development and launch of new products and features. They work with a variety of stakeholders to define the product vision, set priorities, and track progress. This course in An Introduction to Algorithmics may be useful for this career path because it teaches the fundamentals of algorithms and data structures, which are essential for understanding the technical aspects of product development. Additionally, the course's focus on complexity analysis can help Product Managers understand the performance trade-offs of different algorithms and data structures.

See salaries and explore the career path for Product Manager

An Introduction to Algorithmics

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