Data Analytics Basics

Big data analytics:-  The process of examining large data sets containing a variety of data types.

 Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.

Big Data Analytics Tools- Helping your organization through thick & thin:

Big data analytics are adopted by many companies today. The analytics process, including the use of big data analytics tools, can help companies improve operational efficiency, drive new income and also gain viable benefits over business opponents.

Some companies specialize in providing big data analytics products, classified as Angoss, Predixion, Alteryx, Alpine Data Labs, Pentaho, KNIME, and RapidMiner.

Big Data Analytics tools

Big data analytics tools are nothing but the software products that hold up predictive and prescriptive analytics applications organizations on big data computing platforms.

There are thousands of Big Data tools available,I have given some examples

Data Storage and Management:-Hadoop ,CloudEra

Data Cleaning :- OpenRefine,DataCleaner

Data Mining:- RapidMiner,Teradata

Data Analysis:- Qubole,BigML

Data Visualization:-Tableau, Datawrpper

Data Integration:-Blockspring,Pentahoa

Organizations that are taking on big data analytics need to accommodate a variety of user types, such as:

The data scientist:-who performs more complicated analyses that involve complex data types and is familiar with how underlying models are designed and implemented to assess inherent dependencies or biases.

The business analyst:- who is likely a more casual user looking to use the tools for proactive data discovery or visualization of existing information, as well as some predictive analytics.

The business manager:-who looks to understand the models and conclusions.

IT developers:- as they support all the prior categories of users.

Here are the Data Analytics Trends of 2016 and Future

Five trends that will have the significant impact on the world of machine data analytics:

->Prescriptive Analytics

->Automated Machine Learning

->Validation and Explanation

->Visualization

->Privacy Legislation