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Raju is a passionate Data Scientist and has over 8 years of Industry experience across various domains and vertical. To add to this he has more than 5 years of experience in training and consulting. He has conducted more than 250 training programs across various tools of Data Science.
Raju has completed his B.Tech from IIT (ISM), Dhanbad and M. Tech from IISc, Bangalore.
Hadoop , Spark, Big Data, Data Analysis, Machine Learning and Data Mining, R Programming, Rattle and R Commander, Java J2ee, Python, Perl, MongoDB, Cassandra , C++ , Matlab, GNU Octave, MPI (Message Passing Interface), VTK (Visualization Tool Kit), Tensor Flow and many more..
> Developer Certification for Apache Spark (O'Reilly School of Technology and DataBriks) certification number 1.1.0–0149
> Hortonworks Certified Apache Hadoop Java Developer ( Hortonworks ) Certificate Number :006- 000311
> Oracle Certified Associate for Java
> Revolution R Enterprise Certified Specialist ( Revolution Analytics)
"Exploration of huge amount of data, understanding trends and teaching about same is my passion . Looking at different activities and forecasting future is what I like most. World of programming also intrigued me a lot. Because these are the tools which help you to get insight of the world through data or simulating a fictitious world or replicating the behavior of reality. How delighted we are when we replicate real world using codes and understand simplicity in complexity of the system and nature."
Functional data structures have the power to improve the codebase of an application and improve efficiency. With the advent of functional programming and with powerful functional languages such as Scala, Clojure and Elixir becoming part of important enterprise applications, functional data structures have gained an important place in the developer toolkit. Immutability is a cornerstone of functional programming. Immutable and persistent data structures are thread safe by definition and hence very appealing for writing robust concurrent programs.
Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! PySpark Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will learn to apply RDD to solve day-to-day big data problems. Python and NumPy are included and make it easy for new learners of PySpark to understand and adopt the model.