Tools For Data Science In 2020 And Beyond

Everyone knows, tools make our work easier. And the best part is, it reduces the work making the work process effective and to the point in less time. That’s why professionals focus more on tools than more on work. Thanks to the technology we are living in today, we are all advanced enough that we can certainly understand where we spend most of our time, and how we can become more productive. Whoever had said tools decreases productivity has said it wrong. Tools always add more value to products and make it more shining and very much concise.

Data Science itself is very vast because data are very vast. Without tools it's highly unlikely you will get control over it, and there are many tools you will learn in your journey to make yourself one of the most productive professionals in your organization. When you learn tools, all your complex work will become more simple and less time consuming, and on the other hand, you become more productive.

Data Science is a very vast spectrum, and it has many domains that require handling of data in a unique way that leads most of the data scientists into the confusion. And if you are a newcomer to data science, I will always recommend you to enroll for the data science course in Hyderabad with ExcelR Solutions, you can choose either from “Diploma in Data Science and PG in Data Science” in partnership with Steinbeis University - Germany and start your data science journey with us.
Tools for Data Science in 2020 and beyond

Tools for data science fall under 3V’s. They are volume, velocity, and variety.

Tools to handle the volume of data.

As you know, when it comes about the volume of data, the talk always comes to space as data are huge and vast just like mother earth and when data ranges from 1 GB space to 10GB space, the tools we use adhere are MS Excel, MS Access, My SQL, Hadoop, and Hive.

Tools to handle the velocity of data

The velocity of data tells about the speed at which we collect the data. It includes both real-time and non-real-time data. Some of the best examples of data velocity are - CCTV, Stock trading, Fraud detection in banking and transaction, and lastly, the data from social media (Facebook, Twitter, Linkedin, Instagram, and more).

And for the variety of data, it comes in two ways - structured data and unstructured data.
The examples that come under structured data are the employee table, tabular data, payout table, and loan application table. Examples of unstructured data are email, social media, customer feedback, video feed, and satellite image feed. There are two tools that we use for a variety of data, and they are SQL and NoSQL.

When you join ExcelR Solutions for the most advanced level data science training in Hyderabad, you will learn about data science and its application using many tools. The best part is you can identify where to use which tools and scale your productivity learning data science from the industrial leaders who have more than 15+ years of experience.

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