The scope and demand of Big Data has seen tremendous growth in the last few years. The reason is quite simple to understand and i.e. the growing needs of enterprises. With respect to the increase in the demand, the users also have reasons to worry about the storage. Until 2011, it was one among the major challenges for organizations. One of the prime focuses of the organizations was to build solutions and frameworks for the purpose of storing data. Now when the storing problems have already been solved by the technologies such as Hadoop, the focus has been shifted to the processing of the same.
Data Science is the matter of concern here. A lot of things which are generally around you are actually the results of this approach. It is widely regarded as the future of AI. It is because of no other reason it would be good for you to under what exactly Data Science is and how exactly it can benefit your business. Before considering the tools, it is necessary for one and all to know what exactly it is and why it is required
Basically, it is a diverse array of various algorithms, tools as well as some important learning principles to achieve some justified outcomes from the raw data. It largely depends on predictions and explanations.
The modern Data Science is quite different from that of traditional one. The traditional one was having a limited scope and it was quite small in size. It was possible to analyze the same with the common BI tools which in the present time is not possible. The problem is present day data is highly unstructured and doesn’t have a specific rule to derive outcomes from the same. Also, the sources are multiple and the users have a lot of worry about the things. Traditional tools are not able to process the information and the modern data needs a lot of tools to be managed and handed especially when it comes to processing and raw information.
Well, that is not the only reason why Data Science ha gained popularity. The fact is it has a very large number of tools that can be utilized for a very large number of tasks. This is the actual reason why it is popular than ever before. Also, it’s the tools of Data Science that simply enable it to have applications in multiple domains. Let us dig a bit deeper to understand the same.
Related Article: Big Data Science Overview
These tools have wide applications and deployment in the major tasks that can be accomplished with the help of Data Science. Check them out below.
Algorithms.io is basically a reputed organization try provides the machine learning as a SOS for the connected devices. The raw data can easily be converted into the useful and wonderful events with the help of this tool. It actually enables organizations to deploy the machine learning for the purpose of data streaming. There are certain good things about this tool and a few of them are spotlighted below.
It is basically a graph processing system that has been provided with the high scalability. With this tool, the users are free to make the outcomes more superior and understandable through graphs. When it comes to unleashing the potential of a complex database with complex structure, this tool can be deployed easily. Even if the development is required on a very large scale, the tool faces no issues. Here are some cool features of this tool along with its applications.
Apache Hadoop is basically a well-known open source approach that is known for its distributed computing. The users are also free to keep up the pace with the scalability, as well as the reliability. Apache Hadoop is considered as one of the powerful tools that simply make sure of processing of large datasets. Even if the data is present across the cluster of computers, the users have no reason to worry about anything. The programming models of this tool are quite simple and this tool simply enables Data Scientists to go beyond their imagination in the research and production domain. Check out below some of the best features of this tool.
Related Article: Using Hadoop for Data Science
Apache HBase is basically a tool to manage the big data store and is known for its scalable approach. When it comes to getting the real-time access for writing, editing or accessing the Big Data, the developers can simply go ahead with it without worrying about anything. Most of the features of this tool are similar to that of BigTable and are widely preferred. Other facts that make these tools simply the best are spotlighted below.
Well, you might have no idea but the fact is Excel is one of the very useful and powerful weapons when it comes to dealing with the data. The users are always free to get the data filtered, sorted, as well as managed in the real time applications. Excel is a part of almost every machine and thus the good thing is scientists can work from anywhere without worrying about anything. The Excel has some real-time applications in the Data Science domain and the users are always able to derive outcomes in a manner they always want.
It is basically considered as powerful tool for interactive visualization. There are many tasks related to the library that can easily be accessed with this approach and the users are quite free to keep up the pace simply in no time. The users can simply make sure of a wide support on web browsers tasks and while deriving the data from various apps. Bokeh comes with some useful features that are widely adopted in Data Science, check it out below
Basically it is a popular platform for application development for all the experts in Data Science. It can even enable them to build Big Data applications. Almost all the issues either complex or simple which are related to the data can easily be solved through the Cascading and the prime reason for this is it simply boasts computation engine. At the same time, the users are free to get quick results on the integration framework, scheduling capabilities as well as on processing of raw data and other information with this tool. More features include:
When it comes to machine learning, this is the tool that is quite helpful in Data Science. It simply makes sure of learning of the same in a very reliable and simple manner. This is the biggest advantage of using this tool in t he Data Science. It is possible to operate this tool even in a cloud and that is the best things about it. It has dedicated features for automating the classification and solves them simply. Moreover, the other tasks that can be accomplished with this tool are detecting the anomalies, association discovery, regression and congestion control, as well as managing some tasks related to modeling.
When it comes to building the core software for real-time Data Science applications, this is the tool that can easily be considered. There are already a very large number of organizations which are using the same. Thus, the users can simply make sure of a wide support available with them all the time. Thus, using this tool simply make sure of quality outcomes in no time. The Data Science teams can simply be made more productive with this tool and the users can consider additional deployments. This tool is simply amazing in generating more revenues for the organizations. Check out some cool features of this tool
It is another powerful tool that enables users to have a solid machine learning platform. The skills of the Data Science team really don’t matters when it comes to using this tool. This is because it has been known already to generate outcomes that are simply amazing. Predictive models of any level can be building using this approach and there are many users who are free to get the things back on track. This is actually a tool that is considered in domains where the experts in Data Science already lacks. The tool is actually based on parallel processing and the libraries which it has been provided with help users to keep up the pace simply. Some more features of this tool are:
It is an approach basically that aims to make the data-driven insights accessible and reliable to the users. This tool is capable to self manage the data and enable users to get the agility level which is always required. The data can also be optimized with this approach easily
One of the prime aims of this tool is to simply let the users to analyze the data in a very reliable manner. There are so many complex issues in the data cycle and wrangling that can easily be solved with this approach. It has capabilities to make the data more elegant in every aspect. It is basically one of the powerful tools to consider when it comes to importing the datasets. This approach is also useful for converting an unstructured data into structured one. The processes can be made faster and the outcomes can be expected on actual time with the help of this tool.
It is actually known as one of the pioneer programming language that has a strong bond with the Data Science. Actually, it is one of the practical tools that can easily be considered for getting an interactive development in domains dealing with scripting language. The users are always free to make sure of multithreaded programming. The biggest fact about this tool is it remains dynamic with every feature that the users make sure of.
It is basically a tool that is useful in end-to-end data science solution management. A lot of intelligent services, as well as the products can easily be developed with this domain. It has some vast application in the Data Science and the experts can simply consider the products and the solutions for the accomplishment of tasks that are mandatory. Some of the key features of this tool that makes it a useful in Data Science are:
It is basically a well-known cloud based service for data management. The users are free to make a lot of emphasis on collaboration and visualizations. It can easily be deployed for visualizations web application assessment.
It is basically an OS that let the users to use a PC without software. This approach has large scale applications in Data Science. With the help of Gawk, the users can handle data formatting in a simple manner. There are a lot of features that are good enough to be considered in this tool and they are:
There are a lot of other tools which are good enough to be considered and probably the future belongs to Data Scientists. In the next coming years, there will be more tools as the demand and scope of this domain is widely blooming.
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