Powerful Trendy Tools You Can Apply with Python Data Science

Anyone who works with Data Science tools would recommend the usual programming languages currently in vogue. The list of data science includes the obvious names, Python, R, Maths and SQL. However, we have access to a few more data science learning tools that could add more power to the obvious names.

In this article, we suggest some important tools that go hand-in-hand with Python Data Science projects.

These tools combining with Python Data Science projects could be further simplified to enhance your role as an advanced level Data Scientist, helping labs to create a production-ready industry app.

TensorFlow: The Favorite Accomplice in Any Machine Learning-Based Python Data Science Project

All top-ten revenue generating companies in the world no rely on Artificial Intelligence and Machine learning to drive their technologies. Google, Airbnb, NVIDIA, DeepMind, Snap, SAP, Intel, and more, these companies work with TensorFlow and Python data science algorithms. Like Python, TensorFlow is also an open source data science software library for high-level computing performance.

TensorFlow, itself developed by Google Brain team at Google AI, has a wide-ranging learning resource to help developers create powerful machine learning algorithms in no time.

Docker

Going a step further with TensorFlow, data scientists would recommend using another powerful tool, Docker. A sound knowledge about TensorFlow Serving 1.8 and Docker Enterprise software package could further simplify the developer experience.

Companies that are relying on Docker Enterprise with TensorFlow and Python data science project need a powerful container strategy. This provides developers with quicker access to Application Lifecycle, and a broader Containerization workflow with Windows Server and Linux.

Python Elasticsearch (Python ES Project)

Built with core Apache Lucene, Elasticsearch is a highly recommended open source search engine platform to develop Python Data Science projects.

Python data science professionals use Elasticsearch tool to provide a dynamic content on any website, allowing developers to build a native app-based feature. Best to understand trends and statistics, Python data science models with Elasticsearch enhances web scraping skills – accessing online analytics and searching.

Also called ES, this works well with both structured as well as unstructured data in JSON format and NoSQL formats.

ReactiveSearch

If you love coding but have no time for doing it, it’s time to dive into ReactiveSearch, a React UI library from ElasticSeach. Python data science projects help in proper handling of UI rendition, query searching, and managing analytics at a generic middleware platform.

Why you should learn ReactiveSearch with Python Data science?

The reason most data scientists would give in dealing with ReactiveSearch would be very compelling. ReactiveSearch provides suitable Python examples to build a data science project from scratch. On top of it, this is fault-free and helps better indexing of documents and a faster query execution.

Rest API with Flask and Connexion

In a web application built on Python, developers can also include Python3 with Flask and Connexion. Rest APIs in Python data science projects include I/O validation (input output validation), in addition to single-page web applications creating using the customary JAVAScript and DOM embellished on it.

If you are looking to build a very simple Python Data Science algorithm, Rest API WITH Connexion and Flask put interactive system at one place which has a beautiful interface with easy comprehension.

Kubernetes (K8s)

Data scientists have a huge fetish about working with Kubernetes in Python data science algorithms. With IBM Cloud acquiring RedHat, the demand for Kubernetes has only magnified, and the trend is going to last for the next five to seven years.

With huge influx of data generated from sensors and Internet of Things (IoT), Python data science dealing with Kubernetes could fill the gap between cloud computing infrastructure and general consensus in hiring more IT professionals to handle data projects.

As we see rampant growth in machine learning, we expect K8s to make a deeper impact on Containerization strategies placed with Python at its core.

There are many more tools that go well with Python, including Apache Airflow, Ansible, TeamCity, GitLab, JIRA, Nginx, Nomad, and Terraform.  With a fast-moving machine learning industry, we expect major data science projects largely relying on Python, Linux, and K8s.