Data Analytics (CS-503 (A)) - Important Questions

Suppose the weights of 800 male students are normally distributed with mean 68.8 kg and standard deviation 2.06 kg. Find the number of students whose weights are (i) between 68 kg and 72 kg and (ii) above 72 kg.

A discrete random variable X has the following probability function with unknown constant k: P(X=0)=k, P(X=1)=2k, P(X=2)=3k, P(X=3)=4k. Determine (i) the value of k (ii) mean and (iii) variance of X.

Explain normal, binomial and Poisson probability distributions with examples. State their properties and applications in data analytics.

Discuss the trends in big data generation and acquisition.

Explain the following in detail with examples: (i) Drivers for Big Data (ii) Predictive analytics and its applications.

With an example, explain the term social media analytics.

What are the various stages in big data analytics life cycle? Illustrate with a figure, explaining each of them.

Explain the MapReduce programming model and its main components.

What is Hadoop? Describe the role of Hadoop in big data analysis. Also explain its architecture and core components.

Describe the structure of HDFS in a Hadoop ecosystem using a diagram. Explain the architecture and data storage process with an example.

Why to choose Hadoop for processing Big Data in detail and explain the concept of distributed and parallel computing challenges?

Explain in detail the interacting process with Hadoop ecosystem. List out various big data processing technologies.

What are the key drivers for Big Data? Explain in detail with suitable examples.

Explain Pig Data Model in detail and discuss how it will help for effective data flow.

Draw and explain architecture of Apache Hive. Explain various data insertion techniques in Hive with example.