Depending on the goal and situation, there are many tools for data scientist. Tools makes job easy . Below is the short description of the most commonly used tools by data scientist
Tools needed for data scientist are mentioned here:
R: R is an open source programming language and software environment where statistical computing and graphics can be done. R is widely used for data analysis and developing statistical software .It is compatible with UNIX platforms, Windows and Mac OS. R is mostly used for data analysis and developing statistical software.
Rapid Miner: Rapid Miner is also called as All in one data science platform as it provides an integrated environment for machine learning, deep learning, predicative analysis and text mining. It is written in Java programming language. Rapid Miner provides us with a GUI to design and execute analytical workflows. By using additional plugins, Rapid Miner functionality can be extended.
Logical Blue: Logical Blue is a user-friendly software platform, which lets business use data to automate decision-making and improve revenue. Logical Blue uses latest statistics and computational intelligence to improve the business of a data scientist. Here, the data scientists can generate a report by separating less useful data from more useful data.
Weka: Weka is a machine learning software. It is compatible with all modern computing platforms as it written in Java Programming Language. It supports various data mining tasks, specifically data pre-processing, clustering, regression, classification, visualization and feature selection. It is easy to us because of GUI (Graphical User Interfaces).
Informatica: Informactica’s product is a portfolio that is focused on data integration such as extract, load, transform, cloud computing integration, data masking, data quality, and data replication. All these components together form a tool set for establishing and maintaining data warehouses.
Matplotlib: Matplotlib is a plotting library for python programming language with which statistical data representation is easy to intercept. It generates quality representation of data in the form of graphs, bar charts, power spectra, histograms etc. It can be used in python scripts, python, i python shell and web application servers.
Tableau: Tableau supports data visualization that helps us in analysing virtually any type of structured data and produce highly interactive, dashboards, beautiful graphs and reports in just minutes. After installation, you can connect virtually to any data source from spread sheets to data warehouses and display information in multiple graphic perspectives.
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