Big Data Analytics

Need of Big Data Analytics

• Nowadays, data storage became cheaper and cheaper. So, every organisation is showing interest to store all types of data about customers including Transactional history, user browsing behavior, user logs etc.
• To process such a huge data we need a cutting-edge frame work.

What is Big data?

• Volume - Size of the data Ex. one Terabyte, 10 Terabyte
• Variety - Different formats of data Ex. .txt, .xml, .jpg, .mp4
• Velocity - Generation of data per unit time Ex: one terabyte per day

Structured Data

• Data that reside in a relational database in the form of tables.
• Structured data represents 10% of entire data
• Example: SQL Tables

Semi Structured Data

• Data that doesn't reside in a relational database
• Semi Structured data represents 10% of entire data
• Example: CSV, XML, JSON files

Unstructured Data

• Data that doesn't have proper format
• Unstructured data represents 80% of entire data
• Example: Text messages,images, videos, social media data,website content,

Big Data Analytics

• The process of converting large amounts of unstructured raw data, retrieved from different sources to a data product useful for organizations forms the core of Big Data Analytics.
• To make sense out of large voluminous data.

Data Scientist

• The role of a data scientist is normally associated with tasks such as predictive modeling, developing segmentation algorithms, recommender systems and often working with raw unstructured data.
• The nature of their work demands a deep understanding of mathematics, applied statistics and programming.

Here is a set of skills a data scientist normally need to have −

• Programming in a statistical package such as: R, Python, SAS, SPSS
• Able to clean, extract, and explore data from different sources
• Research, design, and implementation of statistical models
• Deep statistical, mathematical, and computer science knowledge