A wide range of data science tools and technologies have been developed due to the rapid rise in the popularity of data science for the general gain and benefit of data science enthusiasts.
A new and widely used word around the globe is data. And most digital behemoths, like Google, Facebook, Microsoft, IBM, and a great number of other significant and minor businesses, are spending their precious time and resources heavily on data and the field of data science.
In this essay, we'll examine eleven amazing tools and technologies that you really must be familiar with. They can be used for building models, starting projects, evaluating data, planning deployments, and so much more! They will greatly assist in developing some original and interesting Python and Data Science projects.
The top 10 technologies and tools that every data scientist should investigate for greater exposure and increased productivity:
GitHub (And Git)
One of the basic qualifications for a Data Scientist is familiarity with GitHub. The best location to display code and talk about projects with a great community is GitHub. Your work can be shared in repositories or as code snippets known as Gists, which are accessible to a wide range of users that visit your profile.
Microsoft's subsidiary GitHub, Inc. offers to host Git version control and software development. It provides its own features in addition to Git's distributed version control and source code management (SCM) capabilities. Every project offers access control in addition to several collaborative tools like wikis, task management, continuous integration, issue tracking, and feature requests.
IDEs
The Integrated Development Environment (IDE) software offers complete tools for the compilation and interpretation of programs. With source code editors, automation tools, and a debugger, it offers a platform for programmers, hobbyists, and developers to experiment with and analyze code and applications.
Python is a well-liked modern language. An IDE can support a single programming language, such as Pycharm, which is only compatible with Python, or it can support a wide range of programming languages, like Visual Studio Code. Therefore, various programming tools, including Pycharm, Visual Studio Code, Jupyter notebooks, etc., are readily available.
Because you may utilize each code block separately and you have the choice to use markdowns, I would also strongly advise using the Jupyter Notebook. It is commonly employed in many successful businesses. Anyone can use these Notebooks to collaborate on code more effectively and efficiently. You may find a comprehensive overview of Jupyter Notebooks in the section below.
GPUs
An electronic circuit known as a graphics processing unit is specialized and built to quickly manipulate and change memory in order to speed up the production of images in a frame buffer that is meant to be output to a display device.
Modern computing relies heavily on GPUs. Computational science and AI are changing due to GPU computing and high-performance networking. The development of GPUs is a major component in the current progress of deep learning.
Since they can handle numerous computations at once, GPUs are ideal for developing deep learning and artificial intelligence models. They have many cores, making it possible for many concurrent processes to compute more effectively.
IBM Watson Studio
IBM's software platform for data science is called Watson Studio, formerly known as Data Science Experience or DSX. The platform comprises a workspace with numerous open-source collaborative tools for data research.
A data scientist can work on a project in Watson Studio with a team of collaborators who all have access to different analytics models and speak different languages (R/Python/Scala). Along with other capabilities like a managed Spark service and data sharing features, Watson Studio integrates common open source tools like RStudio, Spark, and Python in a controlled and secure environment.
Google Cloud Platform
The Google Cloud Platform (GCP) is a set of cloud computing services that Google offers. It utilizes the same internal infrastructure as Google does for its end-user products, including Google Search, Gmail, file storage, and YouTube.
Google Cloud Platform offers serverless computing environments, platform as a service, and infrastructure as a service. In addition to a set of management tools, it provides a variety of modular cloud services, such as computing, data storage, data analytics, and machine learning. An account number or credit card is needed for registration.
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