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Recursion, Backtracking and Dynamic Programming in Python

Learn Competitive Programming, Recursion, Backtracking, Divide and Conquer Methods and Dynamic Programming in Python

  1. Topics
  2. Development
  3. Python for Data Analysis

Recursion, Backtracking and Dynamic Programming in Python

InstructorHolczer Balazs
Duration15h 46m
Students11.6K
Rating4.6 (929)

More Related Topics

  • Recursion Techniques31
  • Backtracking Algorithms15
  • Dynamic Programming (DP)28
  • Competitive Programming26
  • Data Science Fundamentals964
  • Machine Learning (ML)2001
  • Pandas for Data Science253
  • Python Fundamentals436
  • Django Web Framework408
  • NumPy for Data Science138
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Comidoc Analysis

Algorithmic Breadth via Python Implementation

Strengths

Wide Algorithmic Scope

The curriculum spans diverse areas including bit manipulation, selection algorithms, and substring search techniques.

Editorial course preview

What the public course preview actually shows

This view highlights a concrete, legible example from the course presentation.

Preview 1 of 1

This slide illustrates recursion using the Tower of Hanoi problem, showing disk movement on pegs alongside the corresponding Python function definition and execution stack.

Selected from the course's public promotional preview. These images document visible presentation material only; they do not represent the complete paid curriculum.

Foundational Memory Concepts

Instruction includes simulations of stack and heap memory to explain how recursion interacts with system memory.

Limitations

Theory-Heavy Delivery

One signal from 2020 suggests the instruction focuses heavily on theoretical discussion rather than hands-on coding-along; this signal predates the displayed course update, and while the update may have addressed it, the update label does not prove that it was corrected.

Reported Implementation Errors

A signal from 2022 identified technical inaccuracies in recursive function logic and naming; however, the 2025 update label does not prove these were corrected.

Language Inconsistency

One sampled review suggests that a signal from 2022 noted the presence of Java code in a section intended for Python learners; the 2025 update label does not prove this was addressed.

Best suited to

  • Learners seeking a wide survey of algorithmic strategies
  • Students preparing for technical interviews
  • Those interested in the theoretical foundations of recursion and memory

Less suited to

  • Learners desiring a code-along or notebook-based approach
  • Those looking for highly polished, error-free implementation guides

Comidoc Score

6.5/10

Worth considering
Defined audience

Comidoc verdict

The curriculum moves from fundamental recursion and memory management into complex topics like backtracking, dynamic programming, and substring search algorithms. It concludes with a complexity theory appendix to provide mathematical context for algorithm analysis.

High volumes of non-trivial algorithms are provided through step-by-step visualizations of how these processes operate. However, the experience is characterized by a heavy emphasis on theory, which may feel disconnected from practical, hands-on coding-along for some learners. There are also isolated reports of technical errors in the implementation logic.

This trade-off makes sense for learners who want a broad conceptual overview of many algorithmic strategies to prepare for interviews, provided they are comfortable supplementing the theory with their own independent practice.

Score breakdown

Curriculum depth
8.0

The curriculum covers a wide range of topics including memory management and complexity theory.

Applied learning
7.3

Includes specific implementation tasks, though some signals suggest a heavy theoretical lean.

Clarity & experience
5.0

Instructional signals are mixed, ranging from detailed explanations to reports of technical errors and pacing issues. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.

Currency & reliability
5.0

A signal noted a language mismatch in one section, though the update label does not confirm a fix. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.

Audience fit
5.8

The curriculum aligns with the target of interview preparation and basic Python knowledge.