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"Which programming language should I focus on first for Data science online Learning : Python or R"?
"Which programming language should I focus on first for data science: Python or R?"
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5 answers
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Elsy’s Answer
Both Python and R are excellent choices for data science. Python is a versatile language with a large community and easy-to-use syntax, making it a great starting point. R is specifically designed for statistical analysis and data visualization, with a strong focus on statistics and an extensive collection of packages. You can start with either language, and it's not necessary to choose just one - many data scientists use both Python and R. Ultimately, choose the language that resonates with you more and have fun exploring data science!
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Athira’s Answer
I prefer Python first, because Python is more useful in now a days.
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Marianne’s Answer
Some of the most commonly used programming languages for data science include: Python, R, Java, Scala, MATLAB, C++, JavaScript, SAS, Swift, Julia, and to a lesser extent, Ruby, Perl, and Kotlin; with Python often considered the best entry point due to its ease of use and extensive libraries for data analysis and machine learning.
Python is widely considered the most accessible data science language, with a large community and powerful libraries like NumPy, Pandas, and Scikit-learn for data manipulation and machine learning.
R is excellent for statistical analysis, data visualization, and graphical modeling, particularly popular in research fields
Python is widely considered the most accessible data science language, with a large community and powerful libraries like NumPy, Pandas, and Scikit-learn for data manipulation and machine learning.
R is excellent for statistical analysis, data visualization, and graphical modeling, particularly popular in research fields
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Sahida’s Answer
When deciding whether to start your data science journey with Python or R, the best choice is contingent on your specific objectives. However, Python is often the suggested starting point for beginners, and here's why:
Python
Adaptability: Python is an all-purpose language with broad applications in data science, web development, and machine learning.
Simplicity: Python's syntax is designed to be user-friendly and intuitive, which makes it an accessible entry point for those new to programming.
Comprehensive Libraries: Python boasts a wide range of libraries, such as Pandas, NumPy, Matplotlib, and Scikit-learn. These tools make Python a powerful resource for data manipulation, analysis, and machine learning.
Job Market Demand: Python skills are highly sought-after in the data science job market.
Compatibility: Python integrates seamlessly with other technologies and can be effectively used in production systems.
R
Statistical Power: R was specifically designed for statistics and data visualization, making it perfect for research and academic purposes.
Visualization: R shines when it comes to creating top-notch data visualizations, thanks to libraries like ggplot2 and Shiny.
Advanced Statistics: R is better equipped to handle complex statistical analyses.
Specialized Use: R is primarily used in academia, biology, and healthcare industries.
Recommendation
Begin with Python: Its versatility, user-friendliness, and alignment with industry needs make it an ideal starting point. Once you've gained confidence in Python, you can consider learning R to further enhance your statistical analysis skills.
For a practical approach, start with small projects such as analyzing datasets with Python. This will help you build confidence and gain hands-on experience.
Python
Adaptability: Python is an all-purpose language with broad applications in data science, web development, and machine learning.
Simplicity: Python's syntax is designed to be user-friendly and intuitive, which makes it an accessible entry point for those new to programming.
Comprehensive Libraries: Python boasts a wide range of libraries, such as Pandas, NumPy, Matplotlib, and Scikit-learn. These tools make Python a powerful resource for data manipulation, analysis, and machine learning.
Job Market Demand: Python skills are highly sought-after in the data science job market.
Compatibility: Python integrates seamlessly with other technologies and can be effectively used in production systems.
R
Statistical Power: R was specifically designed for statistics and data visualization, making it perfect for research and academic purposes.
Visualization: R shines when it comes to creating top-notch data visualizations, thanks to libraries like ggplot2 and Shiny.
Advanced Statistics: R is better equipped to handle complex statistical analyses.
Specialized Use: R is primarily used in academia, biology, and healthcare industries.
Recommendation
Begin with Python: Its versatility, user-friendliness, and alignment with industry needs make it an ideal starting point. Once you've gained confidence in Python, you can consider learning R to further enhance your statistical analysis skills.
For a practical approach, start with small projects such as analyzing datasets with Python. This will help you build confidence and gain hands-on experience.
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David’s Answer
Both are powerful programming languages. Python is easier to learn due to simple and readable syntax. It is more beginner friendly. You use Python for machine learning and AI, data manipulation, and web development. R has a steeper learning curve. It is used more for statistical analysis and modeling, data visualization, and bioinformatics research.