DATA ANALYTICS

Data is today, a word with which everyone is familiar with. It's quite easy to read and understand data, until and unless it is too small. But the problem arises in the case when it's too elaborated or present substantially.

 Data analytics is a process of performing an inspection, cleaning of data, applying transformations and modeling with a motive of extracting important information from raw data, proposing conclusions and decisions.

For performing data analytics, we go ahead through various tactics and techniques, which are useful in different domains like businesses, Scientific applications, hospitals, traffic, nature, etc.

Nowadays, one can go with a more efficient approach to get benefits by smartly deciding more logically and scientifically. This ensures a more successful business operation.

Raw data produces insights by making the use of algorithms. These are the stored old records of the company or the current data produced.

We can encompass the current trends, vogue, and drifts in the business. In what ways can we generate more throughput, by improving which policies.

These insights help the companies to manage their stock, analyze the product-specific demands and prevent workload at the eleventh hour.

We can understand data analytics with a daily life example. If there is a particular road on which traffic is found almost daily. This is a total wastage of time and management.

Traffic is analyzed and indicated to the designated people so that they can beforehand divert the traffic on another road to prevent blocking of roads for hours. This would also reduce accidents in the place.

Data analytics generally use Raw Data, Business Intelligence, Online Analytical Processing servers (OLAP) to enhance the business revenues, optimize the costs and resources. 

Quantitative and Qualitative data analytics are based on the statistical, numeric data analysis and interpreting the text, audio, videos, and images.

Data analytics include a more refined approach that performs statistical calculations and Knowledge Discovery from Data, which is more of prediction and not description. 

Various statistical analysis is:

• Descriptive Analytics perform mining and come up with descriptive data.

• Predictive Analytics takes the support of statistical models.

• Confirmatory Data Analytics, which confirms the current existing hypothesis.

• Exploratory Data Analytics, which follows discovering features of data.

The data analytics team involves data analysts, data engineers, and data scientists. Data integration converts it into a uniform environment for ease of use. These systems are one of the NoSQL databases, Hadoop, clusters, warehouses of data.

Data Cleaning removes inconsistencies and redundant data. Manipulations, keeping in mind the data governance policies make sure that corporate standards aren't violated.

Tools, software, and programming languages are into work and analysis generates final results and conclusions. Some tools are

• R programming, Scala, Python, SAS as languages


• Tableau Software, Qlikview, OpenRefine MS Excel for processing data.


• Konstanz Information Miner (KNIME) & RapidMiner platforms for data modeling.


• Apache Spark as a data processing engine.

Data analysts anticipate an average Payscale in lakhs. In return competition and the company expects industry trends from the analysts. The target is to infer based on what is already known by a researcher.

Vast applications are travel, health services, detection of traffic, rare species, and forest fires, game campaigning, business and stock management, etc. which involve data visualization and intuitions.

With a worldwide acceptance of the fact that after torturing the data, confessions made out, no organization can resist that data analytics has now become an inseparable part of businesses.