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

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What is Data Science 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. 

17 Essential Data Science Tools

Data science tools box - tools which are used for data science

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.

1. 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. 

  • Experts working with the connected devices are simply in a position to make the machine learning processes adoptable and useful
  • All the problems related to the cloud infrastructure such as security, scalability, as well as reliability can easily be managed. Also, the compatibility issues can be resolved in a very reliable manner. 
  • The users are totally free to create a large set of API’s for integrating the machine learning with the World Wide Web as well as for the mobile apps
  • This tool promises numerous applications in safety and proper utility of the raw data

2. Apache Giraph

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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.

  • It is capable to handle all the tasks that needs master computation
  • When it comes to sharing the aggregators, the users are always free to keep up the pace with the same
  • The developers can simply make sure of out of the core computation
  • All the tasks having the edge oriented inputs can be managed reliably
  • It has a constant development cycle and the growing community of users always make sure to offer support to all other users

3. Apache Hadoop

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.

  • When it comes to scalability, the users can simply go ahead with the thousands of databases and can even handle them when they are too small
  • There is a dedicated library which simply detects and handles all the failures that often occurred at the Application Layer. This makes sure that the developers don’t have to depend largely on the hardware to get quality outcomes. 
  • There are several sub modules in this tool that can be trusted for support and accomplishment of the task in a desired time frame.

4. Apache HBase

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.

  • Apache HBase is actually an Open Source approach which gives users the access to mold the things as per need
  • It is largely versioned, non-relational and in fact, a strong storage system for both structured, as well as unstructured data
  • There is automatic shading of tables in this tool
  • It is quite modular and liner to use for practical applications
  • All the tasks related to reading and writing the data can be performed with this tool in error-free manner and in the shortest possible time.

5. Excel

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.

  • The ranges can easily be tagged or named and the same further helps in creating a makeshift database which is useful for the users
  • When it comes to exploring the dataset, the features such as filtering and sorting are very useful in saving time and locating the data
  • There are Pivot tables that are useful for tabulate data management. 
  • Several important metrics can easily be managed 
  • A lot of creative and effective solutions can easily be derived

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6. Bokeh

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

  • Similar to that of D3.js, Bokeh simply make sure of development of elegant graphics that add more to the creativity and skills of a developer
  • When it comes to managing the streaming datasets, the overall abilities and the interactivity of the users can easily be enhanced with this tool
  • Interactive plots, data applications, as well as dashboards can easily be created with it

7. Cascading

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:

  • Desired level of abstraction can easily be assured with this tool in the Data Science. There are large scaling options available that let the users come with a balanced outcome
  • It can easily be made run on many other ported platforms to get the desired outcomes
  • With this tool, the project can be integrated with another one simply in no time

8. BigML

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.

  • The users are able to get the sophisticated machine-learning solutions without making bulk investments
  • It always make sure of offering users the most intelligent applications that are easy to use and are practically useful
  • The private deployments, fully automations, as well as managing machine learning solutions are the tasks for which it has some dedicated features and thus the users can take the maximum benefits of the same.

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9. RapidMiner

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

  • The users are free to automate the predefined connections. There are more than 1500 functions that can be deployed for the same
  • The Data mining process can be made easy with this tool as there are a lot of integration options available
  • Advanced Queuing mechanisms can be opted with this approach simply and the tasks can be accomplished faster
  • A lot of complex tasks related to data preparation can be made simple

10. DataRobot

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 can provide the outcome of a very large number of possible combinations of different algorithms that are vital in the Data Science
  • The parameters can easily be tuned or transformed
  • The world class prediction models can be developed in no time with this tool

11. Qubole

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

12. Trifacta

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.

  • Accurate analysis can also be performed with this tool
  • It is possible to get the outcomes from the data which has different sources and even locations on a network with this tool

13. Clojure

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 has been provided with a very vast group of immutable data structures that are good enough to be considered
  • Java Frameworks can easily be accessed by the users and with a dedicated approach
  • It is simply capable to provide strength to the core skills of the users
  • The users can easily develop a software transactional memory system and the same is exactly what that make sure of multithreaded designs

14. D3.Js

It is actually a JavaScript library that seems to be very basic but has features far beyond the expectation and imagination of those who work in Data Science domain. All sort of documents can easily be manipulated and without compromising with the data. The users can simply add life to their data and can make the same superior and load with more applications with this tool. Some other benefits that users can have from this tool in Data Science are:

  • A complete emphasis can be given on the web standards and this to get the capabilities of all the latest browsers. The users need not to worry about tiding their outcome to the other frameworks and especially those which are complex
  • The visualization components with useful applications can be combined with this tool and the users are free to get a data-driven approach
  • The arbitrary can easily be binded to a document object model and all the transformations can be made to the document easily.

15. Feature Labs

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 possible to integrate the data which can help developers, experts and business managers to save time and efforts
  • A lot of tasks about the data forecasting can be accomplished in no time. 
  • The tasks can be accomplished in different sessions with this tool

16. Fusion Tables

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 simply let the users to share tables
  • It is possible to combine the data with the other data on the World Wide Web
  • The users are free to make a map within a minutes
  • Data can be visualized and can be imported
  • The users can search a very large number of Fusion Tables
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17. Gawk

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:

  • The documents can be searched text within the shortest possible time
  • It become easy for the users to easily read, as well as writes programs 

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.