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Data Science Tools

Data Science is a new technology, which is basically used for apply critical analysis. It utilizes the potential and scope of Hadoop. It also helps fully in R programming and machine learning implementation. It is a blend of multiple technologies like data interface, algorithm. It helps to solve an analytical problem. Data Science provides a clear understanding of work in big data, analytical tool R. Also, it provide the analyses of big data. It gives a clear idea of understanding of data, transforming the data. Also, it helps in visualizing the data, exploratory analysis, understanding of the null value. It used to impute the value with the help of different rules and logic.

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Data Science Tools

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  1.  Data Scientists carry out data operations usingdata sciencetools.  Here is the list of bestdata sciencetools used most widely. These tools are divided into two categories 1. tools that can be used without any programming knowledge 2. tools used by programmers

  2. ➢Data Robot ➢Rapid Miner ➢Amazon Lex ➢Trifacta ➢Datawrapper ➢Fusioo

  3. ➢Hadoop ➢Tableau ➢Python ➢ NoSQL ➢ R ➢TensorFlow

  4.  Data Robot is an automated machine learning platform used by executives, professionals and data scientists. Features: 1. parallel processing is allowed 2. model optimized 3. easy deployment process

  5. It is a prediction modeling tool that can analyze the complete life cycle of the prediction model.  Rapid Miner contains functionalities required for data preparation, validation and deployment it also provides GUIs for predefinedblocks.  Itcomes with a free trial of 30 days.  Features: For implementing big data analytics Rapid Miner Radoopis used 1. Rapid Miner Cloud is used for the cloud-based repository. 2. For data preparation, statistical modeling and visualization Rapid Miner Studio is used 3.

  6.  Deep learning functions like Automatic Speech Recognition(ASR) and Natural Language Understanding (NLU) has been provided by Amazon Lex.  with the help of this Amazon Lex new category of products can be defined easily through conventional interfaces Features: effective in cost built-in integration with AWS Lambda, AWS Cloud Watch, and AWS Mobile Hub with the help of Amazon Lex, you can build your own chat bots easily in minutes 1. 2. 3.

  7. For Data Preparation and Data Wrangling Trifacta provides three products  ✓ TrifactaWrangler TrifactaWrangler Enterprise ✓ ✓ TrifactaWrangler Pro These three products can be used by organizations and also by individuals.  Features: For exploring files and for cleaning and transforming desktop files we use TrifactaWrangler 1. Self-service platform for data preparation is provided by Trifacta wrangler pro 1. TrifactaWrangler Enterprise serves as an empowering platform for analysts. 1.

  8.  Mainly Data wrapper is used for visualization from any type of data.  Representing data in the form of line or bar charts is also possible with this data wrapper help. Features: 1.customize styling of charts accordingly. 2.installation or up dation is not required 3.code free environment

  9.  The online database can be built and managed using this cloud-based tool. Features: 1.Real-time notifications on discussion boards, task assignments, etc. 2.No coding is required for the creation of multiple apps. 3. Permissions for every App created can be managed at any point of time.

  10.  By using Hadoop distribution of large data across various computer clusters for processing can be done using simple programs.  This is an open-source framework system. Features: 1. In Application, Layer failures are detected and are handled 2.easily scalable 3.Fast at data processing

  11.  with the help of tableau, one can get the whole view of their data.  This data visualization tool helps in converting raw data into an understandable format. Features: 1. Different data sources can be easily connected 2. Drag and Drop feature is very helpful which makes it easy to use. 3. can be used with any database.

  12.  Python is a high —level programming language used mainly utilizing in small or large-scale applications.  It has features to facilitate data analysis and visualization. Features: 1. Extensible programming language 2. It provides many free packages to download 3. consists of many free data analysis libraries

  13.  “No SQL” means “not only SQL” this database is used for retrieval and storage of data. Features: 1. No need for object-relational mapping or data normalization 2. works on self-contained aggregates 3. rich data structures

  14.  R is the most popular open-source programming language used widely in developing data analysis and statistical software.  Both structured and unstructured data can be analyzed using R. Features : 1. supports cross-platform 2. Cost-Effective and easily adaptable to the user’s requirement 3. With its distributed computing it not only reduces processing time but also increases efficiency.

  15.  Tensor flow is a free open source library mostly used for dataflow and different programming.  With its help, it is easy to adapt new algorithms and experiments with the same API’S and server architectures. Features: Visualization of every part of the graph is possible with its responsive construction. 1. 2. distributed computing is possible. 3. Flexible in operating.

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