NOC:Algorithms for Big Data - Study24x7

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# NOC:Algorithms for Big Data

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"Big data is data so large that it does not fit in the main memory of a single machine, and the need to process big data by efficient algorithms arises in Internet search, network traffic monitoring, machine learning, scientific computing, signal processing, and several other areas. This course will cover mathematically rigorous models for developing such algorithms, as well as some provable limitations of algorithms operating in those models.

• Total 8 Modules
• 48 Videos
• Published on 01 July, 2019

## Week 1

• Lesson 1 - Basic definitions

11m
• Lesson 2 - Conditional probability

13m
• Lesson 3 - Example problems

5m
• Lesson 4 - Karger's mincut algorithm

10m
• Lesson 5 - Analysis of Karger's mincut algorithm

15m
• Lesson 6 - Random variables

12m
• Lesson 7 - Randomized quicksort

42m
• Problem solving video - The rich get richer

13m
• Problem solving video - Monty Hall problem

10m

## Week 2

• Lesson 1 - Bernoulli, Binomial, and Geometric distributions

17m
• Lesson 2 - Tail Bounds

56m
• Lesson 3 - Application of Chernoff bound

14m
• Lesson 4 - Application of Chebyshev's inequality

24m

## Week 3

• Lesson 1 - Intro to Big Data Algorithms

39m
• Lesson 2 - SAT Problem

39m
• Lesson 3 - Classification of States

18m
• Lesson 4 - Stationary Distribution of a Markov Chain

18m
• Lesson 5 - Celebrities Case Study

17m
• Lesson 6 - Random Walks on Undirected Graphs

22m
• Lesson 8 - Reservoir Sampling

12m
• Lesson 7 - Intro to Streaming, Morris Algorithm

12m
• Lesson 9 - Approximate Median

10m

## Week 4

• Lesson 2 : Balls, bins, hashing

9m
• Lesson 1 : Overview

9m
• Lesson 3 : Chain hashing, SUHA, Power of Two choices

19m
• Lesson 4 : Bloom filter

32m
• Lesson 5 : Pairwise independence

23m
• Lesson 6 : Estimating expectation of continuous function

23m

## Week 5

• Lesson 2 - Perfect hashing

16m
• Lesson 1 - Universal hash functions

16m
• Lesson 3 - Count-min filter for heavy hitters in data streams

5m
• Problem solving video - Doubly Stochastic Transition Matrix

5m
• Problem solving video - Random Walks on Linear Structures

5m
• Problem solving video - Cat And Mouse

6m
• Problem solving video - Lollipop Graph

6m

## Week 6

• Lesson 1 - Estimating frequency moments

36m
• Lesson 2 - Property testing framework

16m
• Lesson 3 - Testing Connectivity

9m
• Lesson 4 - Enforce & Test Introduction

9m
• Lesson 5 - Testing if a graph is a biclique

15m
• Lesson 6 - Testing bipartiteness

23m

## Week 7

• Lesson 1 - Property testing and random walk algorithms

26m
• Lesson 2 - Testing if a graph is bipartite (using random walks)

40m
• Lesson 3 - Graph streaming algorithms: Introduction

23m
• Lesson 4 - Graph streaming algorithms: Matching

29m
• Lesson 5 - Graph streaming algorithms: Graph sparsification

29m

## Week 8

• Lesson 1 - MapReduce

1h 65 min
• Lesson 2 - K-Machine Model (aka Pregel Model)

1h 63 min

# Review & Ratings

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01 Jul 2022 12:51 PM

Excellent

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