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      • Data Structures And Algorithms Using Python

      Data Structures And Algorithms Using Python

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      Data structures are basically just that are structures which can hold some data together. In other words, they are used to store a collection of related data. There are four built-in data structures in Python – list, tuple, dictionary and set.

      Algorithm is a step-by-step procedure, which defines a set of instructions to be executed in a certain order to get the desired output. Algorithms are generally created independent of underlying languages, i.e. an algorithm can be implemented in more than one programming language.

      Python has four basic inbuilt data structures namely Lists, Dictionary, Tuple and Set. These almost cover 80% of the our real world data structures.

      This course will help you  to implement the algorithms from scratch.

      Course Features

      • Lectures 45
      • Quizzes 0
      • Duration 50 hours
      • Skill level All levels
      • Language English
      • Students 66
      • Assessments Yes
      ITET CoursesProgrammingData Structures And Algorithms Using Python
      • Chapter 1 7

        • Lecture1.1
          Algorithms and programming: simple gcd 28 min
        • Lecture1.2
          Improving naive gcd 14 min
        • Lecture1.3
          Euclid’s algorithm for gcd 24 min
        • Lecture1.4
          Downloading and installing python 22 min
        • Lecture1.5
          Assignment statement, basic types – int, float, bool 22 min
        • Lecture1.6
          Strings 21 min
        • Lecture1.7
          Lists 29 min
      • Chapter 2 8

        • Lecture2.1
          Control flow 20 min
        • Lecture2.2
          Functions 19 min
        • Lecture2.3
          Examples 12 min
        • Lecture2.4
          More about range() 11 min
        • Lecture2.5
          Manipulating lists 19 min
        • Lecture2.6
          Breaking out of a loop 13 min
        • Lecture2.7
          Arrays vs lists, binary search 25 min
        • Lecture2.8
          Efficiency 12 min
      • Chapter 3 7

        • Lecture3.1
          Selection sort 13 min
        • Lecture3.2
          Insertion sort 11 min
        • Lecture3.3
          Recursion 18 min
        • Lecture3.4
          Mergesort 25 min
        • Lecture3.5
          Mergesort, analysis 11 min
        • Lecture3.6
          Quicksort 15 min
        • Lecture3.7
          Quicksort analysis 13 min
      • Chapter 4 8

        • Lecture4.1
          Tuples and dictionaries 16 min
        • Lecture4.2
          Function definitions 13 min
        • Lecture4.3
          List comprehension 19 min
        • Lecture4.4
          Exception handling 18 min
        • Lecture4.5
          Standard input and output 17 min
        • Lecture4.6
          Handling files 24 min
        • Lecture4.7
          String functions 17 min
        • Lecture4.8
          Formatting printed output 08 min
      • Chapter 5 7

        • Lecture5.1
          Pass, del() and none 08 min
        • Lecture5.2
          Backtracking, n queens 36 min
        • Lecture5.3
          Global scope, nested functions 11 min
        • Lecture5.4
          Generating permutations 08 min
        • Lecture5.5
          Sets, stacks, queues 19 min
        • Lecture5.6
          Priority queues and heaps 22 min
        • Lecture5.7
          Abstract datatypes, classes and objects 16 min
      • Chapter 6 8

        • Lecture6.1
          Classes and objects in python 20 min
        • Lecture6.2
          User defined lists 24 min
        • Lecture6.3
          Search trees 24 min
        • Lecture6.4
          Memoization and dynamic programming 19 min
        • Lecture6.5
          Grid paths 19 min
        • Lecture6.6
          Longest common subsequence 25 min
        • Lecture6.7
          Matrix multiplication 17 min
        • Lecture6.8
          Wrap-up, python vs other languages 28 min
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