Big Data Analytics

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 Content:

• 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