Python Data Structures and Algorithms Course by LinkedIn Learning

Course Certificate

Stacks

• using class to represent stack
• learning push and pop
• using list allows others to use inster, remove , etc.
• LIFO (Last In, First Out)
• add item to top of stack: append()
• retrieve item from top of stack: pop()
• pop() removes a value and returns it, whereas peek() only returns a value, without removing it.

2D lists

• lists of lists.
• Graph theory and preparing mazes.
• Nodes and Edges.
• Undirected and Directed graphs.
• Weighted Graphs.
• (5,7) => row 5 and column 7.

Depth-First Search:

• Based on Stack DS
• Applications:
  • Optimization for criteria.
  • Pathfinding.
  • Scheduling algorithms
  • Assessing investment decision trees.
  • Ordering of formula cell evaluation in spreadsheets.
  • Data serialization.
  • Determining the order of compilation tasks for software builds.
  • Resolving symbol dependencies.
• Algorithm:
  • Pop the stack.
  • Is this the goal?
  • if so, we are done.
  • Otherwise, push undiscovered neighbors onto the stack and add update predecessors/discovered.
  • Repeat this until no more items in the stack. (Repeate until stack is empty)

Queue:

• CPU scheduling.
• FIFO (First in, First Out)
• enqueue(): add item to the queue.
• dequeue(): remove item from queue.

Breadth-First Search:

• Based on Queue DS.
• Uses the shortest path.
• Applications:
  • GPS systems.
  • Flight reservations systems.
  • Finding neighbor nodes in peer-to-peer networks.
  • Social networking sites to find connctions between users.
  • Web crawlers.
  • Many applications in artificial intelligence.
  • Electronics and communication engineering.
  • Scientific modeling.
• Algorithm:
  • enqueue.
  • is this the goal?
  • if so, we are done.
  • otherwise, enqueue undiscovered neighbors and update predessors.
  • repeat until queue is empty.

Priority Queue:

• Applications:
  • AI => A* search algorithm.
  • Optimizing algorithms.
  • Operating system process scheduling.
  • Bandwidth management.
  • Statistical analysis.
  • Spam filtering
• Operations:
  • get(): retrieve the item with the heighest priority.
  • put(item): add item to priority queue.
  • is_empty(): determine if the priority queue is empty.

A* Search:

• Applications:
  • Traffic navigational system (GPS).
  • Social netwrok analysis.
  • Natural language processing.
  • Machine learning.
  • Puzzle solutions and puzzle analogous problems.
  • Algorithmic trading.
  • Robotics.
  • Video games.
• It uses Heuristic to be implemented.
• Manhanten Search.
• G value: best distance from start to current cell.
• H value: heuristic distance from current cell to destination.
• F value: the sum of the G value and H value (representing the probable optimal value or minimum distance based on the heuristic used)
• V and G values are stored in dictionaries.
• Algorithm:
  • Get heighest priority item from priority queue (min F-value):
  • is it the goal?
  • if so, we are done
  • otherwise: put undiscovered neighbours, calculate f-values, update predecessors
  • repeat until queue is empty.

Stick with foundations until u understand the whole idea.