While you are searching for high performance Data science tools, the top 2 options are R and Python

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While there are many commercial statistical packages, the R language (open source) and its comprehensive package library, CRAN -is an essential tool.
Although R is a quirky language, it comes close to providing “one-stop shop-Solution” for most statistical work. R has tremendous graphics facilities; CRAN includes parsers for many kinds of data, and newer extensions extend R into distributed computing. If there’s a single tool that provides an end-to-end solution for statistics work, it is R.
The other powerful tool for <a href="https://www.excelr.com/data-science-cou ... -in-mumbai /">Data science course in Mumbai</a>
in Python. Python is a general programming language developed by software engineers that has solid libraries for math, statistics and machine learning. Python has best-in-class tools for pure machine learning and deep learning as well, but what it lacks is much of the infrastructure for subjects like econometrics and communication tools such as reporting.
You may think that Python is well-suited for computer scientists and software engineers. But in current trend Python is emerging as the popular language used more in data science applications. Python has advantages that accelerate it to the top position in data science tools. It would integrate well with the most cloud platforms as well as platform-as-a-service providers. Python supports multiprocessing for parallel computing, it brings a varied advantage of ensuring large-scale performance in data science and machine learning. Python can also be extended with modules are written in programming languages C/C++.
Python is perfect when it involves the integration of <a href="https://www.excelr.com/data-science-cou ... -in-mumbai /">Data science course in Mumbai</a>
tasks with web apps or when there is a requirement of incorporating statistical code into the production database. The full-fledged programming environment of Python makes it a perfect fit for implementing algorithms. Python Packages like NumPy, SciPy, and pandas are great for data analysis jobs. Packages for graphics are, Python’s matplotlib emerges as a good package, and for machine learning tasks, scikit-learn becomes the ideal alternate.