What do data scientists do?

In general terms, Data Scientists gets data and convert it in to information/predicts results.

Data Scientists collects data from real world(generally BigData from internet), process that data and convert it in to dataset that can be analyzed. They analyze this dataset based on statistical models or machine learning and create results/reports which can be useful for data driven products(even for general public).

Step by step process they do with data :

• Define the question
• Define the ideal dataset
• Determine what data you can access
• Obtain the data
• Reading data - Excel, XML,JSON, Web,..
• Merging data
• Clean the data
• Reshaping data
• Summarizing data
• Exploratory data analysis
• Graphs
• Plotting systems
• Clustering
• Statistical prediction/modeling
• Extracting generalization information from data
• Interpret results
• Challenge results
• Synthesize/Write up results
• Create reproducible code
• Completely reproduce all the documents such that you can communicate it with other people
• Distribute results to other people

Why R?

R programming language has become the single most important tool for computational statistics, visualization and data science.

• It is free
• It has comprehensive set of packages
• Data Access
• Data Cleaning
• Analysis
• Data reporting
• It has one of the best development environment - R Studio (in terms of data science)
• It has amazing ecosystem of developers - large collection of packages developed and published by larger R community
• Packages are easy to install and "nicely play together"

Difference between Pig and Hive

Apache Pig and Hive are two projects that layer on top of Hadoop, and provide a higher-level language for using Hadoop's MapReduce library. 

Pig

• Apache Pig provides a scripting language for describing operations like reading, filtering, transforming, joining, and writing data -- exactly the operations that MapReduce was originally designed for. 
• Rather than expressing these operations in thousands of lines of Java code that uses MapReduce directly, Pig lets users express them in a language not unlike a bash or perl script. Pig is excellent for prototyping and rapidly developing MapReduce-based jobs, as opposed to coding MapReduce jobs in Java itself. 

If Pig is "scripting for Hadoop", then Hive is "SQL queries for Hadoop".

Hive

• Apache Hive offers an even more specific and higher-level language, for querying data by running Hadoop jobs, rather than directly scripting step-by-step the operation of several MapReduce jobs on Hadoop. 
• The language is, by design, extremely SQL-like. Hive is still intended as a tool for long-running batch-oriented queries over massive data; it's not "real-time" in any sense. 
• Hive is an excellent tool for analysts and business development types who are accustomed to SQL-like queries and Business Intelligence systems; it will let them easily leverage your shiny new Hadoop cluster to perform ad-hoc queries or generate report data across data stored in storage systems mentioned above.