• SAS

SAS stands for Statistical Analytics System. As the name suggests it is used for analytics or that what is was designed for other many people who don’t even use it for analytics they do various other thing using SAS but that is one of the prominent features. With SAS as it is one of the best tools for analytics. So yes statistical analytical tools it has its database management capabilities or data management capabilities. It being a great ETL tool. It has various expression transformation capabilities as well and its data management capabilities also make it very easy to use none. It is also great programming language.

  • Hadoop Apache Spark

Hadoop Apache Spark is an open source big data processing framework built around speed, ease of use, and sophisticated analytics. It wasoriginally developed in 2009 in UC Berkeley’s AMPLab, and open sourced in 2010 as an Apache project.

  •  Excel

Microsoft Excel has extensive analytics capabilities to perform statistical analysis with the added advantage of visibility a flat learning curve and good reporting functionalities. Excel has become the tool of choice to start your data analytics journey. Excel offers intuitive analytics features such as find, connect, and shape to model and analyze a company’s data.

mobile friendly dashboard with perfect ability to integrate infinite amount of datapoints in analysis. Ability to connect to numerous sources and servers, compatible with Azure, AWS. In-built machine learning algorithm, recent addition of speech analytical tool.

  • Python

Python is widely use in today world as scripting, oops, data science and in machine learning also. Is just because of easier and interpreted language. Python higher level language which is object oriented programming language. It is used in machine learning also. We use jupyter notebook for predictive modeling in machine learning. Let us move forward to discuss python uses in machine learning.

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