It’s 2021, and all of us are constantly surrounded by data. With the internet connecting everyone in the world, data is always being exchanged. Whether you are running a small business or are a professional data scientist, you are always dealing in data. Participating in the production and spread of data is as simple as connecting to Cox internet. This is why everyone should learn to use tools to effectively process data.
Whether you’re already a data science expert or a simply need tool to process everyday data, here are some of the best tools to use in 2021.
Keras is a free open-source software library that uses Python. It plays the role of an interface for TensorFlow. With its quick application, Keras is a flexible and user-friendly tool for data scientists. Moreover, it supports TensorFlow, Microsoft Cognitive Toolkit, or Theano. A useful tool for developing and developing deep learning models, Keras is one of the leading high-level neural network APIs today.
TensorFlow is a great tool for advanced machine learning. Whether you’re a learner or researcher, you can easily use it. It is an end-to-end open-source platform that trains and infers deep neural networks. Data scientists can use TensorFlow for a number of functions. For example, prediction, classification, and creation.
Weka is another great open-source big data tool. Written in Java, Weka uses many machine learning algorithms to mine data. Moreover, it has tools to carry out a large number of functions. For instance, it can help with data analysis, development, clustering, and regression. Hence, it is an ideal tool for managing large sets of data.
OpenRefine is an essential tool to master in 2021. A standalone open-source desktop application, OpenRefine lets users study various file setups of big data. Using this, data scientists can also convert files into other formats through data wrangling. Also, OpenRefine can be used to edit blocks with added values and prolong web services. Moreover, this tool offers high data privacy.
For data scientists, this is one of the most powerful tools. An open-source visual framework, Seahorse allows users to build Spark applications using fast and easy methods. With this tool, data scientists can easily create ETL (Extract, Transform, & Load) dataflows, and machine learning. Moreover, Seahorse has a clean and simple user interface, which makes it great for beginners.
DataRobot is an AI-based automation platform. A portable system that data scientists can run on either cloud platforms, on-premise data centers, or even as an AI service, it is useful for generating precise predictive patterns. With this, users can simply perform a broad range of Machine Learning algorithms. For example, clustering, analysis, and regression types. Also, it allows for efficient model analysis. Moreover, it has a growing library of algorithms and prototypes that users can benefit from. It is also great for fast and efficient machine learning.
#7 Apache Hadoop
This is an open-source, Java-based framework that stores and processes big data sets and applications. It does this by dividing large-scale data over many nodes in computing clusters. Apache Hadoop is popular for its very high processing capacity. Some of its other useful features include scalability, flexibility, and resilience.
MongoDB is a source-available data-based program. It is classified as a NoSQL database program. Written in C++, MongoDB is a cross-platform and document-oriented program. Some of its most useful features include scalability and high performance. Thus, it is great for large-scale web apps. Also, it stores data in compliant, JSON-like records. Moreover, it is flexible and ideal for indexing data.
Orange is another open-source tool that is great for mining big data. With this tool, data scientists can process large amounts of data quickly. Also, it lets users examine data as well as visualize it without needing to code. Since its visualizations are interactive, they are very easy to use. Orange uses many different methods to present complex data. It also investigates scatter plots, statistical data, and box designs.
Paxata is one of the many data science tools that you can use to purify and develop data. It is very easy to use, as it also works with MS Excel. With this tool, data scientists can easily gather data, discover data, and even repair stained data. A self-service data preparation solution, Paxata is great for business analysis. Analysts can use it to explore, transform, and combine a lot of data. With great visualization, Paxata offers easy methods of converting raw data into useful information.