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[qualitythought]

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[qwakho]

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[infintedatascience-blog]

R Vs Python ⏩Swipe Left⏩ R and Python are both open-source programming languages with a large community. New libraries or tools are added continuously to their respective catalog. R is mainly used for statistical analysis while Python provides a more general approach to data science. Choice between R or Python depends on: 1. Statistical analysis or deployment 2. The amount of time you can invest 3. Your company/industry most-used tool #data#science#datascience#datascientist#engineer#datascienceengineer#python#dataanalyst#computerscience#business#hotdatascience#R#Rvspython https://www.instagram.com/p/Bt3uyfQAhUX/?utm_source=ig_tumblr_share&igshid=uec7isl87155

[shivanipandy]

Datatrained provide Scala Programming for Data Science course collaboration with IBM. Scala for Data Science Engineering a Data Science Engineer should have a strong programming knowledge as well as knowledge in Database.

https://www.datatrained.com/scala-programming-for-data-science-in-collaboration-with-ibm

[slajobs]

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[iibmindia]

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[qualitythought]

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[qualitythought]

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[infintedatascience-blog]

Why Python for Data Science??

There are many reasons for using python for data science, some of them are :1. Very simple language to learn. 2. Best packages and libraries like matplotlib, numpy, scipy, scikit-learn, tensorflow, pandas,       seaborn, keras, pytorch and many more. 3. iPython notebooks for interactive data analysis and modelling. 4. Extensively used in industry.

Python is a very powerful tool, which is also open sourced and flexible, adding more to its popularity. It is known to have massive libraries for manipulation of data and is extremely easy to learn and use for all data analysts. Anyone who is familiar with programming languages such as, Java, Visual Basic, C++ or C, will find this tool to be very accessible and easy to work with. Apart from being an independent platform, this tool has the ability to easily integrate with the existing Infrastructure and can also solve the most difficult of problems. It is said, that this tool is powerful, friendly, easy and plays well with others, apart from running everywhere. A lot of banks use this tool for the purpose of crunching data, some institutions use it for analyzing and visualization. This tool offers the great benefit of using one programming language, across multiple application platforms.

[iibmindia]

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[iibmindia]

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[iibmindia]

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[iibmindia]

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[iibmindia]

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