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Big Data Fundamentals
Intermediate
Online
10 Weeks
Free Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 16679 | yes | The University of Adelaide, Adelaide |
The Syllabus
- Pre-course survey
- Course outline
- BigDataX requirements
- The four Vs of big data
- Quiz 1: The four Vs
- Rare events in big data
- Quiz 2: Rare events in random data
- Models of data
- Characteristics of web and social networks
- Models of social networks
- Quiz 3: Models of social networks
- Community detection in social networks
- Quiz 4: Betweenness
- Introduction to clustering
- Clustering methods for social networks
- Quiz 5: HCS, SNN and MCL clustering algorithms
- Hierarchical clustering of data
- Quiz 6: Hierarchical clustering of data
- K-means clustering
- The concept of PageRank
- PageRank for strongly connected graphs
- Quiz 7: Calculating PageRank
- General PageRank using taxation
- Quiz 8: Applying PageRank with Taxation
- The basics of MapReduce
- Quiz 9: The basics of MapReduce
- MapReduce implementation
- Computing PageRank using MapReduce
- Major assignment 1
- The importance of words in a collection of documents
- Quiz 10: The basics of word importance
- Measuring similarity and characterising documents
- Quiz 11: Computing Jaccard similarities
- Applying local sensitivity hashing to find similar documents
- Quiz 12: Finding MinHash signatures
- Motivation of frequent itemsets in supermarkets
- Quiz 13: Online advertising
- Design of association rules for advertisement
- Quiz 14: Confidence and association rules
- A-priori algorithm for computing frequent itemsets
- Quiz 15: Applying the A-priori algorithm
- Activity 9: Implementation of the A-priori algorithm
- Motivation of recommendation systems
- Collaborative filtering recommendation systems
- Quiz 16: Collaborative filtering
- Content-based recommendation systems
- Quiz 17: Content-based systems
- Effectiveness of recommendation systems
- The Google Ads problem
- Quiz 18: The Google Ads problem
- Online algorithms and competitive ratio
- Quiz 19: The Google Ads problem and competitive ratio
- The balance algorithm to solve the Google Ads problem
- Quiz 20: The balance algorithm
- Activity 11: The greedy and the balance algorithms
- Challenges and queries for data streams
- Quiz 21: Queries
- Sampling streams
- Quiz 22: Sampling
- Determining distinct users of web pages
- Quiz 23: Counting distinct elements
- Activity 12: Counting distinct elements
- Spam filtering using hashing
- Quiz 24: Impact of number of hash functions
- Major assignment 2
- Course summary
- Post-course survey
Instructors
Articles
27 Aug'21, 04:04 AM
27 Aug'21, 04:04 AM