data analytics course in hyderabad
It’s almost every day that we read about “data” these days. Data has become such an important resource that new career opportunities and fields of academia have emerged, particularly to handle the huge bulk of data. Even processing speeds in the order of a few thousand gigahertz do not seem to tackle the problem of “overflow of data”. IBM published a report saying that about 2.5 million gigabytes of data was generated in the US, per day, in the year 2012. Certain global institutes even consider ‘data’ to overtake ‘oil’ as the hottest commodity of the century. Amidst the recent buzz, one term that we often come to hear is ‘data analytics’. It is high-time that we ‘bust the myth’ and understand what this term actually refers to, what are the capabilities of this particular approach, and nonetheless, its limitations.
Data analytics is the process of examining data sets in order to draw logical conclusions and reveal information and statistics which the data is likely to contain, but may not be comprehensible apparently. It particularly focuses on inference – the process of deriving conclusions from existing pieces of information and predicting the likelihood of its future occurrence, based on a certain set of predefined parameters. Before we dive deeper into this astounding field of data analytics, it’s imperative to understand the difference between data science and data analytics.
There are various kinds of analytics; some prominent ones being-
● Prescriptive Analytics: This kind of analytics is based on existing rules and recommendations. It is specific and concise, and under most circumstances, it generates the most precise result. However, this category of analytics is particularly applicable when the data under question has been investigated a handful of times already, and the kind of insights that can be drawn from the data is well-known and verified. It is seldom applied to new and unknown datasets.
● Predictive Analytics: This category of analytics is quite similar to prescriptive analytics, with the exception being that apart from recommending the possibilities based on existing courses of action, it can also be used to chart out a future course of action.
● Diagnostic Analytics: This category of analytics is particularly used when a certain course of events has already occurred, and the individual or organization needs to retrace their steps and diagnose the causes and implications of the event. One instance, might be the situation where an organization needs to review the reach of a social media campaign and draw information of its success based on page reviews and other metrics.
● Descriptive Analytics: This is an extensive method of analytics, and is used with raw data that has not likely been encountered before. It enables the organization to research all permutations and combinations of information and statistics that is likely to be drawn from the dataset. As evident, this process is time-consuming and often ineffective, considering that a majority of the information might not have any viable use. However, it enables the individuals working on the data to gain the greatest amount of insight, which is not likely to happen in any other approach of analytics, thus enabling them to combat future inconvenience.
Join a data analytics course to know more.
Contact
360DigiTMG
2-56/2/19, 6th floor, Vijaya towers near Meridian school, Ayyappa Society Rd, Madhapur, Hyderabad, Telangana 500081
9989994319
For more info Click here
For more Details Click here
Source link : Click here
