BigData club committee:

Chairman: GuruPrasad M S

Staf coordinators: Prithviraj jain, Chintesh R

Student Secretaries: Prateek Kumar Jha, H N Swati

Lecturer and Hands-on session 2:

Installation of R terminal for linux

 Introduction to R language, hands-on session on basic R programs

Topics Covered:

1. How to Install R terminal for linux

2. How to install R studio for ubuntu and windows.

3. Introduction to R language- R variables, R operators, conditional statements, looping and related programs.

4. obtaining a structured data from an unstructured file.

 Date:01/09/2018

time:2.00 to 5.00pm

Lecturer session 1: Introduction to bigdata

 Date: 27/08/2018

time: 4.00 to 5.00pm

Big data is data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are a number of concepts associated with big data: originally there were 3 concepts volume, variety, velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value. Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated. Based on an IDC report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.