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This article mainly explains "why Python is better than R". The explanation in this article is simple and clear, easy to learn and understand. Please follow the ideas of Xiaobian and go deep into it slowly to study and learn "why Python is better than R" together.

that's why

scalability of

Scalability is a huge benefit to adopt in data science. Since most data scientists will typically work alongside other employees in the engineering department, the modeling and overall flow of models can be easier to deploy. For example, a typical data scientist may focus only on performing modeling, perhaps even one-time outputs. However, there is one step that is likely to need to be done before training the machine learning model before modeling. This step is part of the data engineering. In this part of the procedure, you can automatically read new data from the SQL database so that the model is always up-to-date when training. Another aspect of the process is deployment. Deploying a model for the first time can be daunting, especially since there is not as much modeling taught in school as there is in the modeling process.

Thanks to Python, software engineers and machine learning engineers can work alongside you.

You can create an airflow oriented acyclic graph (DAG) that automatically trains the model when there is new data on a particular timeline or certain parameters are met (for example, train the model only if we get 100 new incoming data records). After training the model, it can evaluate new data, which can then be exported to SQL tables by using Python.

Jupyter notebooks

Or another similar data science visualization tool that can interpret Python. You can run code units, comment, create titles, and add widgets that improve notebook functionality. The code you write and share here is Python. For your data scientist, being able to code in this programming language in Jupyter Notebook is a huge win.

third-party libraries

There are several powerful and commonly used software packages that can be accessed using Python. Some that come to mind are sklearn(also known as sci-kit learning) and TensorFlow.

Sklearn[2]

This powerful data science library features packaged classification models and regression models ready for use with your datasets.

- Classification

Sklearn defines classification as: identifies the category to which an object belongs. Some popular algorithms include Support Vector Machines (SVM), Nearest Neighbors, and Random Forests. Sklearn also outlined spam detection and image regression as its most popular app use cases.

- Return.

Sklearn defines regression as predicting continuous-valued attributes associated with an object. Popular regression algorithms include Support Vector Regression (SVR) and Nearest Neighbors, whose applications include drug response and stock prices.

TensorFlow [3]

For deep learning, this library is my must-have tool for modeling more complex situations. Some of the main projects that this popular and powerful library can handle are: neural networks, general adversarial networks, and neural machine translation.

integration mode

Because I use Python on most data science projects, I successfully integrated the model.py file into an object-oriented programming format. These documents have been systematically developed in a modular manner. Calling APIs in Python is a bit easy because there are so many documents on websites that can help get website/company data.

cross-functional

The reason is partly a combination of scalability and integration. If you want to perform data science processes locally and hand the output to stakeholders, that's fine, but with Python, you can do more with other experts from engineering.

When I first started coding, it was in R, and when I presented my processes and code to data engineers and software engineers for deployment, it took some time to accurately describe the data science behind the code.

I'll also find that most of the engineers I work with will help me deploy the model while they're already using Python, so they can easily convert my data science code even if they don't fully understand how the model works.。

Thank you for reading, the above is "why Python is better than R" content, after the study of this article, I believe that everyone has a deeper understanding of why Python is better than R, the specific use of the situation also needs to be verified by practice. Here is, Xiaobian will push more articles related to knowledge points for everyone, welcome to pay attention!

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