Data science has proven to be a boon to both IT and business. The innovation incorporates acquiring value from information, understanding the data and its patterns and afterward anticipating or producing results from it. The field has arisen as an advantageous choice for those keen on removing, controlling and producing experiences from tremendous data volumes. There is a monstrous demand for data scientists across industries, which has pulled numerous non-IT experts and non-programmers to this field. Not just having suitable qualifications and education, a successful data scientist must be skilled at a specific set of tools. This article lists the top data science tools where you don't need any programming or coding abilities to work with these tools.
Data robot is the platform for automated machine learning. It can be used by data scientists, executives, software engineers and IT professionals. Data Robot is an amazing data science tool as it provides an easy deployment process, model optimization, allows parallel processing and also has a Python SDK and APIs.
Xplenty is an information combination, ETL and an ELT stage that can bring all of your information sources together. It is a complete toolbox for building information pipelines. This flexible and versatile cloud stage can coordinate, process, and plan information for investigation on the cloud. It gives answers for promoting, deals, client assistance and designers.
KNIME for data scientists will help them in blending tools and data types. It is an open-source platform. It will allow you to use the tools of your choice and expand them with additional capabilities. KNIME is very useful for the repetitive and time-consuming aspects and it can work with many data sources and different types of platforms. Experiments and expands to Apache Spark and big data.
Apache Hadoop is an open-source framework. Simple programming models that are created using Apache Hadoop can perform distributed processing of large data sets across computer clusters. It is a scalable platform that can be used to detect and handle failure at the application layer. Also, it has many modules like Hadoop Common, HDFS, Hadoop Map Reduce, Hadoop Ozone and Hadoop YARN.
RapidMiner is one of the best data science tools for the complete life-cycle of prediction modelling. It has all the functionalities for information arrangement, model structure, approval and organization. It gives a GUI to interface the predefined blocks.
Trifacta is one of the best data science tools as it provides three products for data wrangling and data preparation. It can be used by individuals, teams, and organizations. Trifacta Wrangler will help you in exploring, transforming, cleaning and joining the desktop files together and Trifacta Wrangler Pro is an advanced self-service platform for data preparation. Trifacta Wrangler Enterprise is for empowering the analyst team.
SPSS is a family of software for managing and analyzing complex statistical data. It includes two primary products: SPSS Statistics, a statistical analysis, data visualization, reporting tool, and SPSS Modeler, a data science and predictive analytics platform with a drag-and-drop UI and machine learning capabilities.
Alteryx provides a platform to discover, prep and analyze the data. It will also help you to find deeper insights by deploying and sharing the analytics at scale. It provides the features to discover the data and collaborate across the organization. Alteryx has functionalities to prepare and analyze the model, and it is a platform that will allow you to centrally manage users, workflows, data assets, embed R, Python and Alteryx models into your processes.
MS Excel can be used as a tool for data science. It is an easy-to-use tool for non-technical persons. It is good for analyzing data. As you know, MS Excel has good features for organizing and summarizing the data and conditional formatting features and most importantly it allows you to sort and filter the data.
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open-source deep-learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform.
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