- Data Analytics - Home
- Data Analytics - Overview
- Data Analytics - Data Life Cycle
- Data Analytics - Methodology
- Data Analytics - Core Deliverables
- Data Analytics - Key stakeholders
- Data Analytics - Data Scientist
- Data Analytics Problem Definition
- Data Analytics - Data Collection
- Data Analytics - Cleansing data
- Data Analytics - Summarizing
- Data Analytics - Data Exploration
- Data Analytics - Data Visualization
- Data Analytics - Introduction to R
- Data Analytics - Charts & Graphs
- Data Analytics - Data Tools
- Data Analytics Statistical Methods
- Data Analytics - Correlation
- Data Analytics - variance
- Data Analytics - Chi-squared test
- Data Analytics - T-test
- Advanced Methods
- Data Analytics - Machine Learning
- Naive Bayesian Classifier
- Data Analytics - K-Means Clustering
- Data Analytics - Association Rules
- Data Analytics - Decision Trees
- Data Analytics - Logistic Regression
- Data Analytics - Time Series
- Data Analytics - Text
- Market Basket Analysis
- MapReduce Unstructured Data
- MADlib & Advanced SQL Techniques
- Data Analytics Useful Resources
- Data Analytics - Quick Guide
- Data Analytics - Useful Resources
- Data Analytics - Discussion
Data Analytics - Overview
The amount of data in the past decade has been exploding. Private companies and research institutions captureterabytes of data about their users interactions, business, social media and also sensors from devices such as mobile phones, automobiles.
The price of data storage in systematically being reduced, the challenge of this era is making sense of this data. This is where big data analytics comes in, the task at hand consists of collecting data from different sources, munge it in a way it is made 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, that often is retrieved from different sources to a data product useful for organizations is the core of this work.