Gain insights into dynamic programming, explore recursion basics, and delve into advanced techniques like Bottom-Up optimization. Discover ways to solve complex problems more efficiently with hands-on coding challenges.

Dynamic programming is something every developer should have in their toolkit. It allows you to optimize your algorithm with respect to time and space — a very important concept in real-world applications.

In this course, you’ll start by learning the basics of recursion and work your way to more advanced DP concepts like Bottom-Up optimization. Throughout this course, you will learn various types of DP techniques for solving even the most complex problems. Each section is complete with coding challenges of varying difficulty so you can practice a wide range of problems.

By the time you’ve completed this course, you will be able to utilize dynamic programming in your own projects.

Dynamic Programming in Python: Optimizing Programs for Efficiency

## Dynamic programming, the magic wand

Do you remember the run time complexity of the Fibonacci algorithm from [earlier lessons](https://www.educative.io/courses/dynamic-programming-in-python/is-plain-recursion-good-enough#recursions-inefficiency-visualized)? We even saw how bad that algorithm performed as we tried to calculate a slightly higher (~50) Fibonacci number. We have modified that algorithm to remember the values it has already evaluated to avoid re-evaluations. Run this code with different values to see how much time the algorithm takes. For comparison, we have reproduced the simple recursion-based solution below as well as in the other tab.


# Dynamic programming, the magic wand

Do you remember the run time complexity of the Fibonacci algorithm from [earlier lessons](https://www.educative.io/courses/dynamic-programming-in-python/is-plain-recursion-good-enough#recursions-inefficiency-visualized)? We even saw how bad that algorithm performed as we tried to calculate a slightly higher (~50) Fibonacci number. We have modified that algorithm to remember the values it has already evaluated to avoid re-evaluations. Run this code with different values to see how much time the algorithm takes. For comparison, we have reproduced the simple recursion-based solution below as well as in the other tab.


In this lesson, we will learn about some problem characteristics in dynamic programming.


Chapter 1: From Recursion to Dynamic Programming

Chapter 2: Top-Down Dynamic Programming with Memoization

Chapter 3: Bottom-Up Dynamic Programming with Tabulation

Chapter 4: Practice Problems

Where to Use Dynamic Programming

Dynamic programming, the magic wand