This MicroMasters program is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems through implementing over one hundred algorithmic coding problems in a programming language of your choice.
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This MicroMasters program is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems through implementing over one hundred algorithmic coding problems in a programming language of your choice.
This MicroMasters program is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems through implementing over one hundred algorithmic coding problems in a programming language of your choice.
No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we have invested thousands of hours designing challenges as an alternative to multiple choice questions that you usually find in MOOCs. We believe in learning through application, especially when it comes to learning algorithms.
For each algorithm you develop and implement, we have designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming.
What's inside
Eight courses
Algorithmic Design and Techniques
(54 hours)
Algorithmic Design and Techniques
(54 hours)
Data Structures Fundamentals
(54 hours)
Data Structures Fundamentals
(54 hours)
Graph Algorithms
(54 hours)
Graph Algorithms
(54 hours)
NP-Complete Problems
(27 hours)
NP-Complete Problems
(27 hours)
String Processing and Pattern Matching Algorithms
(36 hours)
String Processing and Pattern Matching Algorithms
(36 hours)
Dynamic Programming: Applications In Machine Learning and Genomics
(36 hours)
Dynamic Programming: Applications In Machine Learning and Genomics
(36 hours)
Graph Algorithms in Genome Sequencing
(27 hours)
Graph Algorithms in Genome Sequencing
(27 hours)
Algorithms and Data Structures Capstone
(36 hours)
Algorithms and Data Structures Capstone
(36 hours)Learning objectives
- Understand essential algorithmic techniques and apply them to solve algorithmic problems
- Implement programs that work in less than one second even on massive datasets
- Test and debug your code even without knowing the input on which it fails
- Formulate real life computational problems as rigorous algorithmic problems
- Prove correctness of an algorithm and analyze its running time
- Understand essential algorithmic techniques and apply them to solve algorithmic problems
- Implement programs that work in less than one second even on massive datasets
- Test and debug your code even without knowing the input on which it fails
- Formulate real life computational problems as rigorous algorithmic problems
- Prove correctness of an algorithm and analyze its running time
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