Data Science is a very broad spectrum and all its domains need data handling in unique way which get many analysts and data scientists into confusion. If you want to be pro-active in finding the solution to these issues, then you must be quick in making decision in choosing the right tools for your business as it will have a long-term impact. This article will help you have a clear idea while choosing the best tool as per your requirements. Let's start with the tools which helps in reporting and doing all types of analysis of data analytic and getting over to dashboarding. Some of the most common tools used in reporting and business intelligence (BI) are as follows:
Apart from all these tools, there is one more which you cannot exclude from this tool's list, and that tool is
Now let's get to the part where most of the data scientists deal with. The following predictive analytics and machine learning tools will help you solve forecasting, statistical modelling, neural networks and deep learning.
Apart from all these widely used tools, there are some other tools of the same category that are recognized as industry leaders.
Now let's discuss about the data science tools for Big Data. But to truly understand the basic principles of big data, we will categorize the tools by 3 V's of big data:
Firstly, let's list the tools as per the volume of the data. Following tools are used if data range from 1GB to 10GB approx.:
Secondly, let's discuss about the tools for handling Variety In Variety, different types of data are considered. In all, data are categorized as Structured and Unstructured data. Structured data are those with specified field names like the employee details of a company or a school database or the bank account details. Unstructured data are those type of data which do not follow any trend or pattern. They are not stored in a structured format. For example, the customer feedbacks, image feed, video fee, emails etc. It becomes really a difficult task while handling these types of data. Two most common databases used in managing these data are SQL and NoSQL. SQL has been a dominant market leader from a long time. But with the emergence of NoSQL, it has gained a lot of attention and many users have started adopting NoSQL because of its ability to scale and handle dynamic data. Thirdly, there are tools for handling velocity. It basically means the velocity at which the data is captured. Data could be both real time and non-real time. A lot of major businesses are based on real-time data. For example, Stock trading, CCTV surveillance, GPS etc. Other options include the sensors which are used in cars. Many tech companies have launched the self-driven cars and there are many high-tech prototypes in cue to be launched. Now these sensors need to be in real-time and very quick to dynamically collect and process data. The data could be regarding the lane, it could be regarding the GPS location, it could be regarding the distance from other vehicles, etc. All these data need to be collected and processed at the same time. So, for these types of data following tools are helping in managing them:
We have discussed about almost all the popular tools available in the market. But it’s always advisable to contact some data science consulting services to better understand the requirements and which tool will be best suitable for you.
Look for the best data science consulting company which would best suit in your requirements list. Also Read: Tag Integration – How to utilize it gain deep insights…
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