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NOC:Programming, Data Structures and Algorithms in Python

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About This Course

Data structures serve as the basis for abstract data types (ADT). The ADT defines the logical form of the data type. The data structure implements the physical form of the data type.[5] Different types of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks. For example, relational databases commonly use B-tree indexes for data retrieval,[6] while compiler implementations usually use hash tables to look up identifiers.Data structures provide a means to manage large amounts of data efficiently for uses such as large databases and internet indexing services. Usually, efficient data structures are key to designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Data structures can be used to organize the storage and retrieval of information stored in both main memory and secondary memory.

  • NOC:Programming, Data Structures and Algorithms in Python Total 9 Modules
  • NOC:Programming, Data Structures and Algorithms in Python 45 Videos
  • NOC:Programming, Data Structures and Algorithms in Python Downloadable on mobile
  • NOC:Programming, Data Structures and Algorithms in Python Published on 08 July, 2019

Course Curriculum

Week 1: Introduction

NOC:Programming, Data Structures and Algorithms in Python
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    Lecture 1: Algorithms and programming: simple gcd

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    Lecture 2: Improving naive gcd

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    Lecture 3: Euclid's algorithm for gcd

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    Lecture 4: Downloading and installing Python

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Week 2: Basics of Python

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    Lecture 6: Examples

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    Lecture 5: Functions

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    Lecture 4: Control Flow

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    Lecture 3: Lists

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    Lecture 2: Strings

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    Lecture 1: Assignment statement, basic types - int, float, bool

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Week 3: Lists, inductive function definitions, sorting

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    Lecture 8: Recursion

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    Lecture 7: Insertion Sort

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    Lecture 6: Selection Sort

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    Lecture 5: Efficiency

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    Lecture 4: Arrays vs lists, binary search

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    Lecture 3: Breaking out of a loop

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    Lecture 2: Manipulating lists

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    Lecture 1: More about range()

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Week 4: Sorting, Tuples, Dictionaries, Passing Functions, List Comprehension

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    Lecture 7: List Comprehension

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    Lecture 6: Function definitions

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    Lecture 5: Tuples and dictionaries

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    Lecture 4: Quicksort analysis

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    Lecture 3: Quicksort

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    Lecture 2: Mergesort, analysis

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    Lecture 1: Mergesort

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Week 5: Exception handling, input/output, file handling, string processing

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    Lecture 6: pass, del() and None

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    Lecture 5:Formatting printed output

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    Lecture 4: String functions

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    Lecture 3: Handling files

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    Lecture 2:Standard input and output

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    Lecture 1: Exception Handling

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Week 5: Exception handling, input/output, file handling, string processing

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    Lecture 6: pass, del() and None

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    Lecture 5:Formatting printed output

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    Lecture 4: String functions

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    Lecture 3: Handling files

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    Lecture 2:Standard input and output

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    Lecture 1: Exception Handling

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Week 6: Backtracking, scope, data structures; stacks, queues and heaps

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    Lecture 5: Priority queues and heaps

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    Lecture 4: Sets, stacks, queues

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    Lecture 3: Generating permutations

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    Lecture 2: Global scope, nested functions

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    Lecture 1: Backtracking, N queens

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Week 7: Classes, objects and user defined datatypes

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    Lecture 4: Search trees

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    Lecture 3: User defined lists

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    Lecture 2: Classes and objects in Python

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    Lecture 1: Abstract datatypes, classes and objects

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Week 8: Dynamic programming, wrap-up

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    Lecture 5: Wrap-up, Python vs other languages

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    Lecture 4: Matrix multiplication

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    Lesson 3: Longest common subsequence

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    Lecture 2: Grid paths

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    Lecture 1: Memoization and dynamic programming

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