Learn Python with Data Structures and Algorithms

Master Python programming while diving deep into Data Structures and Algorithms. This comprehensive course combines Python fundamentals with essential computer science concepts, preparing you for technical interviews and advanced programming challenges. Learn to write efficient code, solve complex problems, and understand the algorithmic thinking required for software engineering roles.

What You Will Learn:

  • Foundations of Python: Understand the basics of Python programming, including syntax, variables, and data types. Learn how to write, debug, and execute Python scripts.
  • Data Structures and Algorithms: Master Python's built-in data structures such as lists, dictionaries, and sets. Implement algorithms for sorting, searching, and manipulating data efficiently.
  • Object-Oriented Programming (OOP): Gain proficiency in OOP concepts like classes, objects, inheritance, and polymorphism, which are crucial for developing complex and modular programs.
  • File Handling and I/O Operations: Learn how to read from and write to files, manage file directories, and handle exceptions for robust file operations.
  • Libraries and Frameworks: Explore essential Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization. Get an introduction to web frameworks like Flask and Django.
  • Data Science and Machine Learning: Dive into data analysis and visualization. Use Scikit-learn for building and evaluating machine learning models.
  • Project Development: Apply your skills in real-world scenarios with hands-on projects. Develop a comprehensive capstone project that showcases your mastery of Python.

Why Choose This Course:

  • Expert Instruction: Learn from Dr. Jane Smith, an experienced software developer and educator, who brings real-world insights and practical knowledge to the classroom.
  • Hands-On Learning: Engage in interactive exercises and projects that reinforce your learning and provide practical experience.
  • Flexible Learning: Study at your own pace with lifetime access to all course materials, including video lectures, coding exercises, and downloadable resources.
  • Comprehensive Curriculum: Cover all essential aspects of Python programming, from the basics to advanced topics, ensuring a well-rounded understanding of the language.
  • Supportive Community: Benefit from a supportive learning environment with access to the course forum, where you can ask questions, share insights, and collaborate with fellow learners.
Ideal For:
  • Beginners with no prior programming experience looking to start a career in software development or data science.
  • Intermediate programmers who want to deepen their understanding of Python and explore advanced topics.
  • Professionals seeking to automate tasks, analyze data, or develop web applications using Python.
Enroll Today:
  • Take the first step towards mastering Python. Enroll now and start your journey to becoming a proficient Python programmer!

This detailed overview should give potential students a clear understanding of what they can expect from the course and the benefits of enrolling. Feel free to adjust any part of it to better fit your specific course offerings. Let me know if you need further customization or additional details!

Curriculum

Overview of Python programming language30:25🔒
Setting up Python environment (installation, IDEs)25:40🔒
Python basics: Syntax, variables, data types20:15🔒
Control structures: Conditionals (if, else, elif), loops (for, while)35:30🔒
Functions: Definition, parameters, return values, and scope28:45🔒
Basic input/output operations15:30🔒

Creating and manipulating lists35:15🔒
List comprehensions28:40🔒
Common operations and methods22:30🔒
Understanding immutability18:25🔒
Tuple operations and methods20:15🔒
Set operations and methods25:50🔒
Set comprehensions22:30🔒
Creating and manipulating dictionaries32:15🔒
Dictionary comprehensions24:30🔒
Common operations and methods26:45🔒

Overview of data structures and algorithms30:25🔒
Big O notation and time complexity analysis25:40🔒
Understanding space complexity20:15🔒

Operations and implementation30:25🔒
Problems and applications25:40🔒
Singly linked list: Creation, insertion, deletion, traversal35:30🔒
Doubly linked list: Creation, insertion, deletion, traversal32:15🔒
Stack operations (push, pop, peek)28:45🔒
Implementation using lists and linked lists24:20🔒
Applications and problems (e.g., balanced parentheses)26:15🔒
Queue operations (enqueue, dequeue, peek)29:30🔒
Implementation using lists and linked lists22:45🔒
Circular queues and deque20:15🔒

Binary trees: Creation, traversal (in-order, pre-order, post-order)45:30🔒
Binary search trees: Insertion, deletion, search38:20🔒
AVL trees and self-balancing trees42:15🔒
Understanding heap properties (min-heap, max-heap)35:45🔒
Heap operations (insert, delete, heapify)40:30🔒
Applications (e.g., priority queues)28:15🔒
Graph representations (adjacency matrix, adjacency list)36:45🔒
Graph traversal algorithms (BFS, DFS)44:20🔒
Shortest path algorithms (Dijkstra's, Bellman-Ford)48:30🔒

Bubble Sort, Selection Sort, Insertion Sort42:30🔒
Merge Sort, Quick Sort, Heap Sort38:45🔒
Comparing sorting algorithms (time and space complexity)35:20🔒
Linear Search, Binary Search28:15🔒
Applications and optimizations25:30🔒
Understanding dynamic programming principles45:20🔒
Classic problems (e.g., Fibonacci sequence, Knapsack problem)40:45🔒
Understanding greedy algorithm principles32:30🔒
Classic problems (e.g., Coin Change, Activity Selection)36:15🔒

Implementing and practicing data structures and algorithms through coding challenges50:20🔒
LeetCode, HackerRank, CodeSignal, and other platforms for practice35:45🔒
Working on real-world problems and projects to apply concepts42:30🔒

Overview of Python and its features25:30🔒
Setting up the Python development environment: installation, IDEs (PyCharm, VSCode)30:45🔒
Basic Python syntax: variables, data types, operators28:20🔒
Control flow statements: if, elif, else22:15🔒
Looping constructs: for, while loops26:30🔒
Functions: defining and calling functions, arguments, return values32:45🔒
Basic error handling: try, except20:15🔒

Lists: creation, indexing, slicing, methods (append, extend, pop, remove)35:45🔒
Tuples: creation, indexing, immutability25:20🔒
Dictionaries: creation, accessing values, methods (get, items, keys, values)32:30🔒
Sets: creation, operations (union, intersection, difference)28:15🔒

String operations: indexing, slicing, methods (split, join, replace, find)35:20🔒
Regular expressions: basics, pattern matching40:15🔒
File handling: reading from and writing to files, handling file exceptions32:45🔒

Sorting algorithms: Bubble sort, Insertion sort, Selection sort38:30🔒
Searching algorithms: Linear search, Binary search32:15🔒

Stacks32:15🔒
Queues28:40🔒
Linked Lists35:20🔒
Doubly Linked Lists41:30🔒
Trees47:45🔒

Sorting algorithms: Merge sort, Quick sort, Heap sort45:30🔒
Searching algorithms: Advanced binary search, interpolation search38:25🔒
Hashing: hash tables, collision resolution techniques (chaining, open addressing)42:15🔒

Understanding recursion: base case, recursive case35:15🔒
Common recursive algorithms: factorial, Fibonacci sequence28:30🔒
Introduction to backtracking: solving problems using recursion32:45🔒
Backtracking algorithms: N-Queens problem, subset sum problem41:20🔒

Introduction to graphs: representation (adjacency matrix, adjacency list)39:45🔒
Graph traversals: Depth-First Search (DFS), Breadth-First Search (BFS)42:20🔒
Shortest path algorithms: Dijkstra's algorithm, Bellman-Ford algorithm48:35🔒
Minimum spanning tree: Prim's algorithm, Kruskal's algorithm44:10🔒

Heaps: implementation (min-heap, max-heap), heap operations (insert, extract-min/max)45:25🔒
Trie: implementation, use cases (word search, autocomplete)38:15🔒
Segment Trees: implementation, range queries, lazy propagation52:50🔒
Fenwick Tree (Binary Indexed Tree): implementation, range queries41:40🔒

Introduction to dynamic programming: memoization vs. tabulation42:30🔒
Common dynamic programming problems: Knapsack problem, Longest Common Subsequence, Matrix Chain Multiplication55:15🔒
Optimization techniques: space optimization, solving overlapping subproblems38:40🔒

Advanced graph algorithms: Topological sorting, Strongly Connected Components (SCC), Kosaraju's algorithm51:20🔒
Advanced string algorithms: KMP algorithm, Rabin-Karp algorithm43:35🔒
Computational geometry: Convex hull, Line intersection39:15🔒
Bit manipulation: techniques, applications32:45🔒

Analyzing time and space complexity: Big O notation, Big Theta, Big Omega46:30🔒
Complexity classes: P, NP, NP-Complete, NP-Hard41:20🔒
Algorithm optimization techniques: greedy algorithms, approximation algorithms38:45🔒
Price
From

$99.99

Courses Title
Web Development
Lessons
16 Videos
Language
English
Course Level
Beginner
Reviews
4.7(5.5k)
Quizzes
08
Duration
7 Weeks
Students
2.5k
Certifications
Yes
Pass Percentage
88%
Deadline
01 Jun, 2024
Instructor
Denial Lie
See All Reviews
FAQs

The duration is of 2 months for recorded & 3 months for live.

Yes at the end of the course completion you will get certificates.

ABCPanda team will arrange a doubt clearance session accordingly.

Yes for recorded sessions access duration 1 year. Live session access duration 2 years.

No our mentors will teach from basic. If you have experience, it would add an advantage.