Top Data Science Tools Everyone Should Know

By Tech-Act    
11/03/2020  428 Views

Top Data Science Tools Everyone Should Know

What are tools? Tools are the devices that help in accomplishing your task. Complex task require those tools which are designed in a detailed manner. For instance, the knife used for cutting vegetables cannot be used for cutting metals.

When we talk about data science field, tools play a very vital role. Data scientist can save on their research time by using right set of data science tools. These tools also help the data scientist in organizing, storing & processing the data. AI and Machine learning task can be executed effortlessly. There are pre-defined algorithms and functions in these tools which aids in building customized machine learning models that have no knowledge of programming. It enables data scientist to invent such techniques that can improve machine learning models and which helps in making better speculations. So if you are aiming to make a career as a data scientist then check out the below mentioned tools which will turn out to be useful in your task as a data scientist.

Data Science Tools:


1. Rapid Miner: It is a data science software platform. It provides an integrated environment for data preparation, machine learning, deep learning, text mining and predictive analysis. It provides advanced analytical solution by using template based framework resulting in faster delivery and reduction in errors. Its graphical user interface is user-friendly also it assists in connecting the blocks quickly.

2. Talend: It is an open source tool. It gives software solutions for data preparation, integration, quality, and management & it integrates the application. Since it is an open source therefore it is quite affordable. It helps in deployment of task and maintains the database accordingly.

3. Data Robot: It is a very well-known tool among the data scientist and IT professionals for automated machine learning. Platform’s easy deployment and parallel processing are the USPs of data robot.

4. Amazon Redshift: It is a part of Amazon Web Services. Amazon Redshift is a data warehousing tool. It enables the users in analyzing large gigabytes of data, this helps in extricating its in-depth understanding & all this is done by using petabyte-scale data warehouse service. This can be used for massive database migration as this problem is generally experienced in data science.

5. Qubole: It is an open source, simple and secured multi cloud data lake platform which facilitates machine learning, data exploration, streaming analytics & ad-hoc analytics. End-user tools which are user friendly like SQL query tools, dashboards are provided by Qubole.

6. SAS: Full Form of SAS is Statistical Analysis Software. It is very effective and efficient in automating user task and in running SQL queries using macros. It helps in data mining and predictive modeling by using interactive dashboards and powerful visualization. It is quite expensive therefore only big corporates can afford it.

7. BigML: Not a master in coding and programming? Then as a data scientist you don’t need to worry because we have BigML, it is a cloud based GUI environment which assists in processing machine learning algorithms in order to bring an effect in different sections of the business. Risk analysis, product innovation & sales forecast in single software are its features. BigML is a very interactive platform because of its wide variety of algorithms like clustering and time series forecasting. It has user-friendly web interface. It offers an option of free account for those whose data analysis needs are smaller.

8. MATLAB: MATLAB is a multi-paradigm programming language and numerical computer environment which is developed to process mathematical information. It allows matrix manipulation, functions and data plotting & algorithms implementation. It is utilized in image and single processing. It is highly adaptable & it is being used at a large scale for scientific disciplines as it can handle all the problems right from data cleaning and analysis to further matured deep learning algorithms.

9. Excel: Though it seems the most basic tool yet it aids data scientist in organizing the data in the form of spread sheet and can perform complex & complicated calculations just at a blink of an eye. It also allows adding custom functions. However, excel will not be ideal for big data processing and calculation.

10. Tableau: It is interactive data visualization software. It helps in manual data analysis and decision making by providing a clear and accurate visuals. It provides a platform which converts raw data into comprehensible format. It assists in inventing strategies by rapidly speculating and by detecting patterns easily through clear visuals. Tableau creates visualization that helps in understanding the dependencies between the predictor and variables. It has analytics tools which enables businesses to observe the changing patterns and trends for making quick inferences.

11. Natural Language ToolKit: We have witnessed a tremendous increase in Natural Language processing which has reduced the language barrier between the machines and humans to an extent. Best examples are ALEXA, SIRI & Google Now. Through the development of statistical model NLP understands human language. Natural Language Tool Kit is a suite of libraries and programs that employ language processing methods like tagging, ML, stemming, parsing & tokenization.

12. Apache Hadoop: The fundamental responsibility of data scientist is storing the data. Well, Apache Hadoop provides a framework which can store & manage massive amount of data without putting in much efforts. YARN & Hadoop MapReduce are the data processing modules which are provided by Apache Hadoop for integrated functionality.

13. Azure HDInsight: It provides a complete software solution for data processing, storage and analysis. To handle data smoothly, Azure HDInsight integrates with Apache Hadoop. For development of models for powerful machine learning and statistical analysis, Azure HDInsight provides Microsoft R server.


Summary:


The above mentioned data science tools are the ones which are currently in trend. Well, it is not a very comprehensive list as new upgrades will keep coming in the market and data scientist will keep looking for enhanced software’s that can store, process and interpret data more precisely.


What are tools? Tools are the devices that help in accomplishing your task. Complex task require those tools which are designed in a detailed manner. For instance, the knife used for cutting vegetables cannot be used for cutting metals.

When we talk about data science field, tools play a very vital role. Data scientist can save on their research time by using right set of data science tools. These tools also help the data scientist in organizing, storing & processing the data. AI and Machine learning task can be executed effortlessly. There are pre-defined algorithms and functions in these tools which aids in building customized machine learning models that have no knowledge of programming. It enables data scientist to invent such techniques that can improve machine learning models and which helps in making better speculations. So if you are aiming to make a career as a data scientist then check out the below mentioned tools which will turn out to be useful in your task as a data scientist.

Data Science Tools:


1. Rapid Miner: It is a data science software platform. It provides an integrated environment for data preparation, machine learning, deep learning, text mining and predictive analysis. It provides advanced analytical solution by using template based framework resulting in faster delivery and reduction in errors. Its graphical user interface is user-friendly also it assists in connecting the blocks quickly.

2. Talend: It is an open source tool. It gives software solutions for data preparation, integration, quality, and management & it integrates the application. Since it is an open source therefore it is quite affordable. It helps in deployment of task and maintains the database accordingly.

3. Data Robot: It is a very well-known tool among the data scientist and IT professionals for automated machine learning. Platform’s easy deployment and parallel processing are the USPs of data robot.

4. Amazon Redshift: It is a part of Amazon Web Services. Amazon Redshift is a data warehousing tool. It enables the users in analyzing large gigabytes of data, this helps in extricating its in-depth understanding & all this is done by using petabyte-scale data warehouse service. This can be used for massive database migration as this problem is generally experienced in data science.

5. Qubole: It is an open source, simple and secured multi cloud data lake platform which facilitates machine learning, data exploration, streaming analytics & ad-hoc analytics. End-user tools which are user friendly like SQL query tools, dashboards are provided by Qubole.

6. SAS: Full Form of SAS is Statistical Analysis Software. It is very effective and efficient in automating user task and in running SQL queries using macros. It helps in data mining and predictive modeling by using interactive dashboards and powerful visualization. It is quite expensive therefore only big corporates can afford it.

7. BigML: Not a master in coding and programming? Then as a data scientist you don’t need to worry because we have BigML, it is a cloud based GUI environment which assists in processing machine learning algorithms in order to bring an effect in different sections of the business. Risk analysis, product innovation & sales forecast in single software are its features. BigML is a very interactive platform because of its wide variety of algorithms like clustering and time series forecasting. It has user-friendly web interface. It offers an option of free account for those whose data analysis needs are smaller.

8. MATLAB: MATLAB is a multi-paradigm programming language and numerical computer environment which is developed to process mathematical information. It allows matrix manipulation, functions and data plotting & algorithms implementation. It is utilized in image and single processing. It is highly adaptable & it is being used at a large scale for scientific disciplines as it can handle all the problems right from data cleaning and analysis to further matured deep learning algorithms.

9. Excel: Though it seems the most basic tool yet it aids data scientist in organizing the data in the form of spread sheet and can perform complex & complicated calculations just at a blink of an eye. It also allows adding custom functions. However, excel will not be ideal for big data processing and calculation.

10. Tableau: It is interactive data visualization software. It helps in manual data analysis and decision making by providing a clear and accurate visuals. It provides a platform which converts raw data into comprehensible format. It assists in inventing strategies by rapidly speculating and by detecting patterns easily through clear visuals. Tableau creates visualization that helps in understanding the dependencies between the predictor and variables. It has analytics tools which enables businesses to observe the changing patterns and trends for making quick inferences.

11. Natural Language ToolKit: We have witnessed a tremendous increase in Natural Language processing which has reduced the language barrier between the machines and humans to an extent. Best examples are ALEXA, SIRI & Google Now. Through the development of statistical model NLP understands human language. Natural Language Tool Kit is a suite of libraries and programs that employ language processing methods like tagging, ML, stemming, parsing & tokenization.

12. Apache Hadoop: The fundamental responsibility of data scientist is storing the data. Well, Apache Hadoop provides a framework which can store & manage massive amount of data without putting in much efforts. YARN & Hadoop MapReduce are the data processing modules which are provided by Apache Hadoop for integrated functionality.

13. Azure HDInsight: It provides a complete software solution for data processing, storage and analysis. To handle data smoothly, Azure HDInsight integrates with Apache Hadoop. For development of models for powerful machine learning and statistical analysis, Azure HDInsight provides Microsoft R server.


Summary:


The above mentioned data science tools are the ones which are currently in trend. Well, it is not a very comprehensive list as new upgrades will keep coming in the market and data scientist will keep looking for enhanced software’s that can store, process and interpret data more precisely.



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