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).


Read more.

Career Relevance by Data Role

The techniques and tools covered in Algorithmic Toolbox are most similar to the requirements found in Data Scientist job advertisements.


Similarity Scores (Out of 100)

Subscribe for updates and new courses
Or create a DataKwery.com account
Fast Facts
Techniques
AlgorithmsDatabasesOptimizationProgramming

Similar Opportunities
NP-Complete Problems

edX - University of California, San Diego

Algorithms, Part II

Coursera - Princeton University

Advanced Algorithms and Complexity

Coursera - National Research University Higher School of Economics

Algorithmic Design and Techniques

edX - University of California, San Diego

Algorithms and Data Structures Capstone

edX - University of California, San Diego

Data Structures

Coursera - National Research University Higher School of Economics

Approximation Algorithms Part I

Coursera - École normale supérieure

Computational Thinking for Problem Solving

Coursera - University of Pennsylvania