DS-Algo
Data Structures and Algorithms in Python
Learn to generate insights using Python-powered data structures and algorithms.
- Practice in 35 Laboratorios prácticos — nothing to install
- 17 Lecciones interactivas y 102 topics mapped to the official exam objectives
- 257 Preguntas del examen de práctica
Intermediate A tu propio ritmo · 1 año de acceso 4.2/5 (334 Revisar)
35 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
17Lecciones interactivas
102Topics
35Laboratorio en vivo
257Preguntas del examen de práctica
89Tarjetas didácticas
89Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
Try Free →
No se requiere tarjeta de crédito
Is a DS/Algo problem stumping you? This Python data structures and algorithms course has your back!
We’ll take you from Python basics to advanced data structures, sorting, and graph algorithms – all while breaking down recursion and memory management.
So, ditch the old chunky textbooks and attempt hands-on labs to write code that’s clever, efficient, and functional.
- Master Python programming, including control flow, functions, and exception handling.
- Structure and execute advanced data structures like linked lists, trees, graphs, and hash tables.
- Analyze algorithms for efficiency using techniques like asymptotic analysis and experimental studies.
- Design and optimize recursive solutions to solve complex problems effectively.
- Implement sorting, searching, and graph traversal algorithms with real-world applications.
- Develop problem-solving strategies using dynamic programming and text-processing techniques.
Course Highlights
-
17 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
-
35 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
-
257 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
-
1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
17 Lecciones interactivas · 102 topics01 Python Primer 12 topics · 15 Laboratorio en vivo +
- Python Overview
- Objects in Python
- Expressions, Operators, and Precedence
- Control Flow
- Functions
- Simple Input and Output
- Exception Handling
- Iterators and Generators
- Additional Python Conveniences
- Scopes and Namespaces
- Modules and the Import Statement
- Exercises
15 Laboratorio en vivo in this lesson — see the labs panel →
02 Object-Oriented Programming 7 topics · 2 Laboratorio en vivo +
- Goals, Principles, and Patterns
- Software Development
- Class Definitions
- Inheritance
- Namespaces and Object-Orientation
- Shallow and Deep Copying
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
03 Algorithm Analysis 5 topics +
- Experimental Studies
- The Seven Functions Used in This Course
- Asymptotic Analysis
- Simple Justification Techniques
- Exercises
04 Recursion 7 topics · 2 Laboratorio en vivo +
- Illustrative Examples
- Analyzing Recursive Algorithms
- Recursion Run Amok
- Further Examples of Recursion
- Designing Recursive Algorithms
- Eliminating Tail Recursion
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
05 Array-Based Sequences 7 topics · 6 Laboratorio en vivo +
- Python's Sequence Types
- Low-Level Arrays
- Dynamic Arrays and Amortization
- Efficiency of Python's Sequence Types
- Using Array-Based Sequences
- Multidimensional Data Sets
- Exercises
6 Laboratorio en vivo in this lesson — see the labs panel →
06 Stacks, Queues, and Deques 4 topics · 1 Laboratorio en vivo +
- Stacks
- Queues
- Double-Ended Queues
- Exercises
1 Laboratorio en vivo in this lesson — see the labs panel →
07 Linked Lists 8 topics · 4 Laboratorio en vivo +
- Singly Linked Lists
- Circularly Linked Lists
- Doubly Linked Lists
- The Positional List ADT
- Sorting a Positional List
- Case Study: Maintaining Access Frequencies
- Link-Based vs. Array-Based Sequences
- Exercises
4 Laboratorio en vivo in this lesson — see the labs panel →
08 Trees 6 topics +
- General Trees
- Binary Trees
- Implementing Trees
- Tree Traversal Algorithms
- Case Study: An Expression Tree
- Exercises
09 Priority Queues 6 topics +
- The Priority Queue Abstract Data Type
- Implementing a Priority Queue
- Heaps
- Sorting with a Priority Queue
- Adaptable Priority Queues
- Exercises
10 Maps, Hash Tables, and Skip Lists 6 topics · 2 Laboratorio en vivo +
- Maps and Dictionaries
- Hash Tables
- Sorted Maps
- Skip Lists
- Sets, Multisets, and Multimaps
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
11 Search Trees 7 topics +
- Binary Search Trees
- Balanced Search Trees
- AVL Trees
- Splay Trees
- (2,4) Trees
- Red-Black Trees
- Exercises
12 Sorting and Selection 8 topics · 2 Laboratorio en vivo +
- Why Study Sorting Algorithms?
- Merge-Sort
- Quick-Sort
- Studying Sorting through an Algorithmic Lens
- Comparing Sorting Algorithms
- Python's Built-In Sorting Functions
- Selection
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
13 Text Processing 6 topics · 1 Laboratorio en vivo +
- Abundance of Digitized Text
- Pattern-Matching Algorithms
- Dynamic Programming
- Text Compression and the Greedy Method
- Tries
- Exercises
1 Laboratorio en vivo in this lesson — see the labs panel →
14 Graph Algorithms 8 topics +
- Graphs
- Data Structures for Graphs
- Graph Traversals
- Transitive Closure
- Directed Acyclic Graphs
- Shortest Paths
- Minimum Spanning Trees
- Exercises
15 Memory Management and B-Trees 5 topics +
- Memory Management
- Memory Hierarchies and Caching
- External Searching and B-Trees
- External-Memory Sorting
- Exercises
16 Appendix A: Character Strings in Python +
17 Appendix B: Useful Mathematical Facts +
Laboratorios prácticos Our edge
35 Laboratorio en vivos- Using the Bitwise Operator
- Using the Equality Operator and the list Class
- Using Arithmetic Operators
- Performing Bitwise Operations
- Using the Comparison Operator
- Using the if-elif-else Statement - Part 1
- Using the if-elif-else Statement - Part 2
- Using the if-else Statement
- Determining the Armstrong Number
- Rectifying Errors
- Finding LCM of Two Numbers
- Creating a Function with its Default Value
- Handling Exception
- Using the dir Function
- Using the math Module
- Understanding the init Method
- Understanding Numeric Progressions
- Calculating the Product of Two Positive Integers
- Finding the Minimum Element
- Using the getsizeof Function
- Implementing a Dynamic Array
- Adding Elements to a List
- Using the extend Method
- Removing Elements from a List
- Constructing the Caesar Cipher Algorithm
- Using Stack Abstract Data Type Method
- Implementing a Stack
- Implementing a Queue
- Implementing a Queue with a Circular Linked List
- Implementing a Deque with a Doubly Linked List
- Adding Elements to a Set
- Performing Set Operations
- Using a Sorting Function
- Using the len() Built-In Function
- Performing Pattern Matching
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
Is this course suitable for beginners?+
Absolutely! We start with the Python essentials and gradually move to more advanced topics. Whether you’re new to programming or just new to Python, you’ll feel right at home.
What are the prerequisites for this Python DSA course?+
All you need is a curious mind and a basic understanding of programming concepts to take this Python DS/Algo course. As long as you can write a basic 'Hello, World!' program in Python, you're ready to start.
Will this course help in competitive programming?+
Definitely! You’ll learn how to build algorithms and data structures using Python. It is an in-demand skill required to solve competitive programming challenges.
How will this course help in my career?+
From cracking technical interviews to excelling in software development, this data structures and algorithms in Python course equips you with foundational skills that are highly valued in the tech industry.
Can I use this knowledge in other programming languages?+
Yes! The concepts of data structures and algorithms are universal. Once you learn them in Python, you can easily apply them in languages like Java, C++, or JavaScript.
Is this course relevant to data science or machine learning?+
It is! Understanding algorithms and data structures is crucial for optimizing data analysis and building efficiency models in data science and machine learning.
Keep going
You might also like
Problems, Python, and Solutions
Learn to write smart, clean Python code to solve data analytic problems using our data algorithm and structures courses.
- 1 año de acceso completo
- 35 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito