Upon successful completion of this course student will learn;
- Understanding the rate of occurrences of events in big data and a better understanding of the various applications of big data methods in industry and research.
- How to design algorithms for stream processing and counting of frequent elements in Big Data
- Knowledge and application of MapReduce
- Key technologies and techniques, including R and Apache Spark, to analyze large-scale data sets to uncover valuable business information.
- Develop your knowledge of big data analytics and enhance your programming and mathematical skills.
- Approach large-scale data science problems with creativity and initiative.
- How to use fundamental principles used in predictive analytics.
- Understand the role of Knowledge Management (KM) practitioners in creating business value
- How to use Cloud Services to derive new values and business models
- Identifying the computational tradeoffs in a Spark application, performing data loading and cleaning using Spark
- Modeling data through statistical and machine learning methods.
- How to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning.
- Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems.
Big Data Analytics: Practicum (400 hours)