Learn how big data is driving organisational change and essential analytical tools and techniques, including data mining and PageRank algorithms. 

The Program

1. The basics of working with big data. Understand the four V’s of Big Data (Volume, Velocity, and Variety); Build models for data; Understand the occurrence of rare events in random data.
2. Web and social networks. Understand characteristics of the web and social networks; Model social networks; Apply algorithms for community detection in networks.
3. Clustering big data. Clustering social networks; Apply hierarchical clustering; Apply k-means clustering.
4. Google web search. Understand the concept of PageRank; Implement the basic; PageRank algorithm for strongly connected graphs; Implement PageRank with taxation for graphs that are not strongly connected.
5. Parallel and distributed computing using Map. ReduceUnderstand the architecture for massive distributed and parallel computing; Apply MapReduce using Hadoop; Compute PageRank using MapReduce.
6. Computing similar documents in big data. Measure importance of words in a collection of documents; Measure similarity of sets and documents; Apply local sensitivity hashing to compute similar documents.
7. Products frequently bought together in stores. Understand the importance of frequent item sets; Design association rules; Implement the A-priori algorithm.
8. Movie and music recommendations. Understand the differences of recommendation systems; Design content-based recommendation systems; Design collaborative filtering recommendation systems.
9. Google’s AdWordsTM System. Understand the AdWords System; Analyse online algorithms in terms of competitive ratio; Use online matching to solve the AdWords problem.
10. Mining rapidly arriving data streams. Understand types of queries for data streams; Analyse sampling methods for data streams; Count distinct elements in data streams; Filter data streams.

What you’ll learn

• Knowledge and application of MapReduce.
• Understanding the rate of occurrences of events in big data.
• How to design algorithms for stream processing and counting of frequent elements in Big Data.
• Understand and design PageRank algorithms.
• Understand underlying random walk algorithms.