About this Course

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Learner Career Outcomes

25%

got a tangible career benefit from this course

33%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 20 hours to complete
English

Skills you will gain

GraphsDistributed ComputingBig DataMachine Learning

Learner Career Outcomes

25%

got a tangible career benefit from this course

33%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 20 hours to complete
English

Offered by

Placeholder

University of Illinois at Urbana-Champaign

Start working towards your Master's degree

This course is part of the 100% online Master in Computer Science from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

Week
1

Week 1

3 hours to complete

Course Orientation

3 hours to complete
1 video (Total 26 min), 4 readings, 1 quiz
4 readings
Syllabus10m
About the Discussion Forums10m
Updating Your Profile10m
Social Media10m
1 practice exercise
Orientation Quiz30m
2 hours to complete

Module 1: Spark, Hortonworks, HDFS, CAP

2 hours to complete
13 videos (Total 108 min), 1 reading, 1 quiz
13 videos
1.1.2 Apache Spark11m
1.1.3 Spark Example: Log Mining9m
1.1.4 Spark Example: Logistic Regression7m
1.1.5 RDD Fault Tolerance4m
1.1.6 Interactive Spark4m
1.1.7 Spark Implementation4m
1.2.1 Introduction to Distros3m
1.2.2 Hortonworks23m
1.2.3 Cloudera CDH2m
1.2.4 MapR Distro2m
1.3.1 HDFS Introduction15m
1.3.2 YARN and MESOS9m
1 reading
Module 1 Overview10m
1 practice exercise
Module 1 Quiz30m
Week
2

Week 2

6 hours to complete

Module 2: Large Scale Data Storage

6 hours to complete
24 videos (Total 303 min), 1 reading, 1 quiz
24 videos
2.1.1 Introduction to MapReduce with Spark3m
2.1.2 MapReduce: Motivation15m
2.1.3 MapReduce Programming Model with Spark9m
2.1.4 MapReduce Example: Word Count9m
2.1.5 MapReduce Example: Pi Estimation & Image Smoothing15m
2.1.6 MapReduce Example: Page Rank13m
2.1.7 MapReduce Summary4m
2.2.1 Eventual Consistency – Part 110m
2.2.2 Eventual Consistency – Part 220m
2.2.3 Consistency Trade-Offs4m
2.2.4 ACID and BASE19m
2.2.5 Zookeeper and Paxos: Introduction10m
2.2.6 Paxos17m
2.2.7 Zookeeper16m
2.3.1 Cassandra Introduction27m
2.3.2 Redis7m
2.3.3 Redis Demonstration14m
2.4.1 HBase Usage API15m
2.4.2 HBase Internals - Part 117m
2.4.3 HBase Internals - Part 29m
2.4.4 Spark SQL8m
2.5.5 Spark SQL Demo8m
2.5.1 Kafka17m
1 reading
Module 2 Overview10m
1 practice exercise
Module 2 Quiz30m
Week
3

Week 3

4 hours to complete

Module 3: Streaming Systems

4 hours to complete
18 videos (Total 216 min), 1 reading, 1 quiz
18 videos
3.1.1 Streaming Introduction9m
3.1.2 "Big Data Pipelines: The Rise of Real-Time"7m
3.1.3 Storm Introduction: Protocol Buffers & Thrift15m
3.1.4 A Storm Word Count Example3m
3.1.5 Writing the Storm Word Count Example10m
3.1.6 Storm Usage at Yahoo3m
3.2.1 Anchoring and Spout Replay17m
3.2.2 Trident: Exactly Once Processing10m
3.3.1 Inside Apache Storm9m
3.3.2 The Structure of a Storm Cluster4m
3.3.3 Using Thrift in Storm10m
3.3.4 How Storm Schedulers Work12m
3.3.5 Scaling Storm to 4000 Nodes14m
3.3.6 Q&A with Bobby Evans (Yahoo) on Storm32m
3.4.1 Spark Streaming18m
3.4.2 Lambda and Kappa Architecture4m
3.4.3 Streaming Ecosystem24m
1 reading
Module 3 Overview10m
1 practice exercise
Module 3 Quiz30m
Week
4

Week 4

4 hours to complete

Module 4: Graph Processing and Machine Learning

4 hours to complete
18 videos (Total 173 min), 1 reading, 1 quiz
18 videos
4.1.2 Pregel - Part 17m
4.1.3 Pregel - Part 211m
4.1.4 Pregel - Part 36m
4.1.5 Giraph Introduction6m
4.1.6 Giraph Example4m
4.1.7 Spark GraphX15m
4.2.1 Big Data Machine Learning Introduction13m
4.2.2 Mahout: Introduction8m
4.2.3 Mahout kmeans5m
4.2.4 Mahout: Naïve Bayes9m
4.2.5 Mahout: fpm6m
4.2.6 Spark Naïve Bayes2m
4.2.7 Spark fpm2m
4.2.8 Spark ML/MLlib11m
4.2.9 Introduction to Deep Learning20m
4.2.10 Deep Neural Network Systems17m
4.3.1 Closing Remarks1m
1 reading
Module 4 Overview10m
1 practice exercise
Module 4 Quiz30m

Reviews

TOP REVIEWS FROM CLOUD COMPUTING APPLICATIONS, PART 2: BIG DATA AND APPLICATIONS IN THE CLOUD

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About the Cloud Computing Specialization

Cloud Computing

Frequently Asked Questions

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