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Python Data Structures and Algorithms

  • Course Code: Data Analysis / BI - Python Data Structures and Algorithms
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to implement classic and functional data structures and algorithms using Python

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Python Data Structures and Algorithms skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to implement classic and functional data structures and algorithms using Python  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Data structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this course, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The course will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the course, you will learn how to build components that are easy to understand, debug, and use in different applications. 

Working in a hands-on learning environment, led by our Python Data Structures and Algorithms expert instructor, students will learn about and explore: 

  • A step by step guide, which will provide you with a thorough discussion on the analysis and design of fundamental Python data structures. 
  • Get a better understanding of advanced Python concepts such as big-o notation, dynamic programming, and functional data structures. 
  • Explore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Gain a solid understanding of Python data structures. 
  • Build sophisticated data applications. 
  • Understand the common programming patterns and algorithms used in Python data science. 
  • Write efficient robust code. 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Implement classic and functional data structures and algorithms using Python. 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  
  • experienced with spreadsheet software who know the basics of Python. 

Course Agenda / Topics 

  1. Python Objects, Types, and Expressions 
  • Python Objects, Types, and Expressions 
  • Understanding data structures and algorithms 
  • Python for data 
  1. Python Data Types and Structures 
  • Python Data Types and Structures 
  • Operations and expressions 
  • Built-in data types 
  • Sets 
  • Modules for data structures and algorithms 
  1. Principles of Algorithm Design 
  • Principles of Algorithm Design 
  • Algorithm design paradigms 
  • Recursion and backtracking 
  • Runtime analysis 
  • Amortized analysis 
  1. Lists and Pointer Structures 
  • Lists and Pointer Structures 
  • Arrays 
  • Pointer structures 
  • Nodes 
  • Finding endpoints 
  • Singly linked lists 
  • A faster append operation 
  • Getting the size of the list 
  • Improving list traversal 
  • Deleting nodes 
  • Clearing a list 
  • Doubly linked lists 
  • Circular lists 
  1. Stacks and Queues 
  • Stacks and Queues 
  • Stacks 
  • Queues 
  1. Trees 
  • Trees 
  • Terminology 
  • Tree nodes 
  • Binary trees 
  1. Hashing and Symbol Tables 
  • Hashing and Symbol Tables 
  • Hashing 
  • Hash table 
  1. Graphs and Other Algorithms 
  • Graphs and Other Algorithms 
  • Graphs 
  • Directed and undirected graphs 
  • Weighted graphs 
  • Graph representation 
  • Graph traversal 
  • Other useful graph methods 
  • Priority queues and heaps 
  • Selection algorithms 
  1. Searching 
  • Searching 
  • Linear Search 
  • Binary search 
  • Interpolation search 
  1. Sorting 
  • Sorting 
  • Sorting algorithms 
  • Bubble sort 
  • Insertion sort 
  • Selection sort 
  • Quick sort 
  1. Selection Algorithms 
  • Selection Algorithms 
  • Selection by sorting 
  • Randomized selection 
  • Deterministic selection 
  1. Design Techniques and Strategies 
  • Design Techniques and Strategies 
  • Classification of algorithms 
  • Technical implementation 
  • Complexity classes 
  1. Implementations, Applications, and Tools 
  • Implementations, Applications, and Tools 
  • Tools of the trade 
  • Data preprocessing 
  • Machine learning 
  • Data visualization 
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