Data Science

Leverage Predictive Analytics, machine learning, and deep learning technologies to gather insights from available data and take data driven decisions. Life Cycle of Data Science project involves business domain understanding, getting required data from sources, data Preparation, modelling using machine Learning or deep learning algorithms, evaluation of model, fine tuning of model, deployment in production.

• Machine Learning: Scalable and extra ordinary fast machine learning computations with Apache Spark ML, MLlib, Apache FlinkML, Apache Mahout.
• Graph Processing: Iterative and parallel graph computations with Spark GraphX, Flink Gelly.
• Structured big data analytics: SQL like interface and processing of large scale data with Spark SQL, Flink Table APIs.
• Advanced Analytics: Recommender Systems and Predictive Analytics using machine learning algorithms.
• Statistical Computing and Analysis: Use of R, Python, Scala programming languages for statistical analysis, predictive modelling and validations.
• Stream data analytics: Kafka, Spark Streaming, Storm, Flink