The volume of data that one has to deal has exploded to unimaginable levels in the past
decade, and at the same time, the price of data storage has systematically reduced.
Private companies and research institutions capture terabytes of data about their users’
interactions, business, social media, and also sensors from devices such as mobile phones
and automobiles. The challenge of this era is to make sense of this sea of data. This is
where big data analytics comes into picture.

Big Data Analytics largely involves collecting data from different sources, munge it in a
way that it becomes available to be consumed by analysts and finally deliver data products
useful to the organization business.

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.
In this tutorial, we will discuss the most fundamental concepts and methods of Big Data
Analytics.

Course Overview

Course Overview
Big Data Analytics – Home
Big Data Analytics – Overview
Big Data Analytics – Data Life Cycle
Big Data Analytics – Methodology
Core Deliverables
Key Stakeholders
Big Data Analytics – Data Analyst
Big Data Analytics – Data Scientist

Big Data Analytics Project

Data Analytics – Problem Definition
Big Data Analytics – Data Collection
Big Data Analytics – Cleansing data
Big Data Analytics – Summarizing
Big Data Analytics – Data Exploration
Data Visualization

Big Data Analytics Methods

Big Data Analytics – Introduction to R
Data Analytics – Introduction to SQL
Big Data Analytics – Charts & Graphs
Big Data Analytics – Data Tools
Data Analytics – Statistical Methods

Advanced Methods

Machine Learning for Data Analysis
Naive Bayes Classifier
K-Means Clustering
Association Rules
Big Data Analytics – Decision Trees
Logistic Regression
Big Data Analytics – Time Series
Big Data Analytics – Text Analytics
Big Data Analytics – Online Learning

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