Data Science (IT-702 (A)) - Important Questions

What is big data? Explain its main features / characteristics in terms of volume, variety and velocity.

Explain the types of data in details.

Discuss the various sources of data.

Describe the data storage and management in brief.

Write about challenges with Big Data.

Extrapolate big data analytics and develop a summary of various applications in the real world scenario.

Discuss data cleaning and sampling.

Describe the additional clustering algorithm with an example.

How the text can be analyze? Also explain text analysis steps.

Explain the classification of decision trees in detail.

Explain Naive Bayes classification in detail.

Explain Association rules and write applications of Association rules.

Explain the building blocks / core components of Hadoop with a neat sketch.

Distinguish between Hadoop and traditional database.

Explain the basic architecture of HDFS

Write short notes on YARN and Decision tree.

Discuss the different types and formats of MapReduce with an example of each.

Explain the various components of Hive and the Hive query processor.

Explain the key components of HBase.

Explain how Sqoop is different from Flume.

Explain the procedure to write user-defined functions (UDFs) in Hive.

Draw and explain the architecture of Apache Pig in detail.

Discuss the Pig Latin application flow / execution flow.

Compare R and Python programming languages for predictive modeling.

Explain the data visualization basics.

Describe how to operationalize an analytics project.

Describe the features of the R programming language.

Describe the major components of the R environment.

Explain different operations on vectors in R.