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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Those with basic Python and advanced spreadsheet software experience

Who should attend & recommended skills

  • Python experienced developers, analysts or others with Python skills who want to implement classic and functional data structures and algorithms using Python.
  • Skill-level: Foundation-level Python Data Structures and Algorithms skills for Intermediate skilled team members. This is not a basic class.
  • Python skills: Basic (1-2 years’ experience)
  • Spreadsheet software: Advanced (6+ years’ experience)

About this course

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.

Skills acquired & topics covered

  • A step-by-step guide, which will provide you with a thorough discussion on the analysis and design of fundamental Python data structures
  • Getting a better understanding of advanced Python concepts such as big-o notation, dynamic programming, and functional data structures
  • Exploring illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner
  • Gaining a solid understanding of Python data structures
  • Building sophisticated data applications
  • Understanding the common programming patterns and algorithms used in Python data science
  • Writing efficient robust code

Course breakdown / modules

  • Understanding data structures and algorithms
  • Python for data

  • Operations and expressions
  • Built-in data types
  • Sets
  • Modules for data structures and algorithms

  • Algorithm design paradigms
  • Recursion and backtracking
  • Runtime analysis
  • Amortized analysis

  • 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

  • Stacks
  • Queues

  • Terminology
  • Tree nodes
  • Binary trees

  • Hashing
  • Hash table

  • Graphs
  • Directed and undirected graphs
  • Weighted graphs
  • Graph representation
  • Graph traversal
  • Other useful graph methods
  • Priority queues and heaps
  • Selection algorithms

  • Linear Search
  • Binary search
  • Interpolation search

  • Sorting algorithms
  • Bubble sort
  • Insertion sort
  • Selection sort
  • Quick sort

  • Selection by sorting
  • Randomized selection
  • Deterministic selection

  • Classification of algorithms
  • Technical implementation
  • Complexity classes

  • Tools of the trade
  • Data preprocessing
  • Machine learning
  • Data visualization