Course 1: Essentials of Big Data Analytics:
This theoretical course provides a solid background on big data and big data analytics. It discusses the history of big data and evolution of big data analytics, the related tools, infrastructures and technologies. The participants will learn how big data analytics has helped to solve some of the more concrete real-world problems along with offering enhanced insights into clients' big data lakes. More importantly, the course conveys the future of big data and what managers need to cater for now, to tap into the big data of the future.
Course 2: Big Data Management Essentials
This hands-on course introduces NoSQL databases, which are the hallmark data silos for big data management. It shows how different types of NoSQL databases are used to cater for different big data management needs of the industries. A strong focus is given to Apache Hadoop ecosystem, and its contribution in ETL and analytics. Participants will conduct intensive hands-on sessions related to big data management with document (MongoDB, Couchbase), Key-Value (Redis, Aerospike), and Wide Columnar (HBase, Cassandra) NoSQL stores.
Course 3: Machine Learning for Big Data
This hands-on course introduces the basic concepts of Machine Learning. It discusses the history of the field, its evolution and its relationship to big data analytics. The course will discuss all machine learning techniques related to predictive analytics, I.e., classification, regression, and cluster analysis. Hands-on with these techniques will be conducted in Python language, on latest machine learning APIs for big data, e.g., Apache Mahout, Spark Machine Learning, and Apache H2O.
Course 4: Business Intelligence (BI) and Big Data Visualization
This hands-on course will unleash the power of data visualization in business analytics of big data. Participants will learn about the evolution of BI from small to big data use cases. Hands-on session will be conducted with state of the art BI tools (Tableau, PowerBI, Qlikview etc.) with NoSQL databases at backend. Participants will actually learn how to translate big data to value through simple, interactive, and action-oriented visualizations.
Course 5: Real-Time Big Data Analytics
This important hands-on course will describe several critical technologies for real-time big data analytics, I.e., Apache Spark, Apache Flink, Apache Storm, and Apache Kafka. In hands-on session, participants will learn how to setup real-time pipelines with Storm+Kafka, as well as individually with Apache Spark, and how valuable analytical information can be extracted through these pipelines.
Course 6: Implementing Big Data Infrastructures:
This is a critical, use-case based course which conveys the primary guiding principles for designing and implementing a big data infrastructure within an organization. The core problems related to such an initiative will be highlighted and discussed in detail, along with methods to address these problems. In this regard, several use case scenarios will also be presented.