Learn how to use Python and/or R programming languages for data analysis (via Zoom) (2024)

FAQs

Should I learn R or Python for Data Analyst? ›

This means that Python is more versatile and can be used for a wider range of tasks, such as web development, data manipulation, and machine learning. R, on the other hand, is primarily used for statistical analysis and data visualization.

In this article, we will provide you with an easy-to-follow three-step approach on the best way to learn Python for data science as a beginner: Studying through online courses and tutorials. Applying your knowledge through participating in coding challenges. Taking on projects that will enrich your data science ...

While knowledge of both Python and R is beneficial, deep expertise in Python will likely give you an edge in technical interviews and help you confidently establish yourself as an expert. However, if you're eyeing a specific data science role that requires R, definitely go for it!

While there are many languages and disciplines to choose from, some of the most popular are R and Python. It's totally fine to learn both at the same time! Generally speaking, Python is more versatile: it was developed as a general-purpose programming language and has evolved to be great for data science.

Python is generally easier to learn for beginners and offers broader use. If your focus is heavily on statistics and data visualization, R's specialized strengths might be a better fit.

Having Python and SQL skills can get you a job in the data field, wether it be Data Science, Data Analytics, Data Engineering or Machine learning. Of course depending on which path you pick there will be new libraries/frameworks you need to understand and master; where you will use these languages as a tool.

To learn the very basics of Python, 2 hours per day for two weeks can be enough. Considering it takes 500+ hours to reach a somewhat advanced level, though, you'll have to study Python for 4 hours per day for 5 months to get there.

If you're passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you. If, on the other hand, you're interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit.

What can Python do that R can't? ›

R also supports a lot of statistical modeling tools such as modelr, Hmisc, and others. R can't be used in production code because of its focus on research, while Python, a general-purpose language, can be used both for prototyping and as a product itself.

Perform statistical analysis in R with functions and packages. Performing statistical analysis in R is a valuable skill for aspiring data analysts to learn in 2024. R provides a wide range of functions and packages that make it easier to prepare data and perform complex analyses.

Disadvantages of Python

Python performs poorly in statistical analysis compared to R due to a lack of statistical packages. Sometimes developers may face runtime errors due to the dynamically typed nature. The flexible data type in Python consumes a lot of memory, causing tasks requiring heavy memory to suffer.

RStudio has recently added support for Python, and you can use it to write and execute Python and R code in the same project, and access Python tools and libraries from R. You can use RStudio to edit, debug, and run Python and R scripts, and create notebooks and reports that combine both languages.

Statistical modeling research tends to be done using R.

R is also widely used in large data analysis teams to conduct statistical models and specialized exploratory work. That being said, R is still a statistical programming language, so you will still need to take time to learn and become familiar with how it works.

R and SQL are both languages that are commonly used for data analysis. The main difference between the two is that R is a programming language that is specifically designed for statistical computing and data analysis, while SQL is a language that is used for managing and querying data stored in relational databases.

For most tasks, SQL is more efficient than Python or R. R is a language for statistical computing. It's different from Python in that is has a different syntax and different data types. Python is better for general-purpose programming.

Data analysts who need to process large data sets and visualize them with attractive graphics would prefer R over Python. Junior developers who require basic web scraping, data processing, and scalability prefer Python.