Five newer data science tools you should be using with Python: 

Here are five newer data science tools that you should consider using with Python: 

 

1. PyCaret: PyCaret is an open-source, low-code machine learning library that simplifies the end-to-end machine learning process. It provides a range of pre-processing, feature engineering, model training, and evaluation functionalities, making it easier to develop machine learning models with minimal code.

2. Dask: Dask is a flexible parallel computing library that enables scalable and distributed data processing. It allows you to work with large datasets that don’t fit into memory by providing parallelized versions of familiar Python data structures like arrays, dataframes, and machine learning algorithms. Dask seamlessly integrates with popular libraries like NumPy, Pandas, and scikit-learn.

3. Optuna: Optuna is a hyperparameter optimization framework that automates the search for the best hyperparameters of machine learning models. It uses Bayesian optimization and other advanced algorithms to efficiently explore the hyperparameter space and find optimal configurations. Optuna integrates well with popular machine learning libraries like TensorFlow, PyTorch, and scikit-learn.

4. Streamlit: Streamlit is an open-source library for creating interactive web applications for data visualization and exploration. It allows you to build custom web interfaces with just a few lines of code, making it easier to share and present your data science projects. Streamlit supports live updates, interactive widgets, and intuitive layouts, providing an efficient way to build and deploy data-driven apps.

5. Vaex: Vaex is a high-performance library for lazy, out-of-core data processing and visualization. It can handle large datasets efficiently by processing data in chunks, making it suitable for working with datasets that are too big to fit into memory. Vaex integrates seamlessly with Pandas, allowing you to leverage familiar syntax and operations.

These tools provide efficient and streamlined solutions for various stages of the data science workflow, from data preprocessing and exploration to model development and deployment. Incorporating them into your Python-based data science toolkit can enhance your productivity and help you tackle more complex and larger-scale projects.