Data Science Tools For Non-Programmers

Posted by excelr on July 12th, 2019

In data science, programming is considered to be a key part when the development of any tools is considered. It is obvious that a person who knows programming skills, loops, logic, and functions will be given more recognition than a person who hasn’t studied programming at all. The former also has a higher probability of becoming a famous data scientist. But what about the latter who has little or no knowledge on programming paradigms? No need to worry because there is good news for these people that they can move into the data science industry by gaining knowledge on certain tools. There exist certain tools that eliminate programming features by providing GUI (Graphical User Interface) which is more user-friendly. These tools do wonders for those people who have little or no knowledge about programming and helps them to develop machine learning models as well. Let us see some of the versatile tools that are both trendy and useful at the same time.

RapidMiner (RM): Developed in 2006, it is an open-source tool that covers everything from data preparation to create models and then validation and deployment. Initially, it was known by the name Rapid-I but after a few years, it was renamed to RapidMiner. This tool is based on the block-diagram method and somewhat similar to Matlab Simulink when the interface is compared. The tool also has predefined blocks that are plug and play enabled. Just connect in the correct order and multiple algorithms can be executed without writing a single line of code. Python and R scripts are like a bonus feature for the tool. Some of the offerings are RapidMiner Server, RapidMiner Studio, RapidMiner Cloud, and RapidMiner Radoop. 

BigML: BigML contains an intuitive and very user-friendly GUI that allows you to execute the following processes:

  • Source: different sources are utilized to gain information.

  • Dataset: defined sources are used for creating data sets.

  • Model: building predictive models.

  • Prediction: to provide some predictions on the basis of models.

  • Ensemble: Ensembles of models are created.

  • Evaluation: evaluates and validates sets.

Paxata: This is an organization that mainly targets on the cleaning of data and preparation and, unlike other tools, it doesn’t focus on the statistical modeling part or machine learning. The visual guidance feature enables it to group the data, find the missing data and fix it, and can also share the data among teams. Like other tools, it also removes the scope of any programming aspects, and technical obstructions are overcome during data handling. The processes involved in Paxata Platform are as follows:

  • Data Adding: a broad variety of sources are used for acquiring data.

  • Explore: identifying gaps in the data by performing an exploration of data using strong visuals.

  • Clean and Change: cleaning of data is undergone by using a process like normalization, imputation, and duplicates detecting.

  • Shaping: pivoting the data (group formation and aggregation).

  • Share and Govern: enabling sharing of the data across teams and authorization is done.

  • Combining: Combining the data frames with just a click by detecting the best possible combination. A technology named SmartFusion combines multiple data sets into one Answer set. 

  • BI Tools: used for viewing or visualizing the final Answer set in an easy manner and permitting iterations between visualization and data preprocessing. 

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As programmers are getting jobs easily as compared to non-programmers, data science covers it all by providing jobs to non-programmers too. One can apply for a data science course, Malaysia to know more about data science and its related tools

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excelr

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excelr
Joined: July 12th, 2019
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