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R vs Python | Which Is Better For Data Analytics | R Vs Python For Data Science | 2022 | Simplilearn

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This video covers R vs Python. It guides you on which Is Better For Data Analytics. And additionally, the video has detailed information about R vs Python For Data Science.
0:00 - Introduction to Python and R
2:40 - Parameters
6:50 - Best Tools and Libraries
9:18 - Conclusion
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What is Python?
Python is a high-level object-oriented programming language developed by Guido van Rossum in 1989 and was first released in 1991. Python is often called a batteries included language due to its comprehensive standard library. A fun fact about Python is that The name Python was actually taken from the popular BBC comedy show of that time, Monty Python's Flying Circus. Python is widely used these days from data analytics, machine learning, and web development. Python allows you to write programs in fewer lines of code than most of the programming languages. Python as a programming language is growing rapidly. It's the right time to get trained in Python.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A Data Scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a Data Scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
➡️ About Caltech Post Graduate Program In Data Science
This Post Graduation in Data Science leverages the superiority of Caltech's academic eminence. The Data Science program covers critical Data Science topics like Python programming, R programming, Machine Learning, Deep Learning, and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.
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✅ Skills Covered
- Exploratory Data Analysis
- Descriptive Statistics
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- Supervised and Unsupervised Learning
- Ensemble Learning
- Deep Learning
- Data Visualization
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