To Increase Exposure and Productivity, Data Scientists should explore the following 5 Technologies and Tools:

The rapid expansion of Data Science has resulted in a wide array of tools and technologies for the overall benefit of Data Science Enthusiasts.

Data has become a popular term in the world. Most tech giants such as Google, Facebook, Microsoft, IBM, and so many other major and minor companies are heavily investing their precious time and precious resources into data and the subject of data science.

As data are measured, collected, and analyzed, graphs, images, or other analysis tools can be used to visualize them. As a general concept, data refers to the fact that some existing information or knowledge has been presented or coded in a way that enables it to be better utilized or processed.

Now, without further ado, let us look at each of these tools and technologies you can use to increase your efficiency and effectiveness when creating new projects.

GitHub (And Git):

Knowledge of GitHub is one of the basic requirements for a Data Scientist. A GitHub account is a great way to show and discuss code with a great community. Your work can be shared in repositories or code blocks in Gists that a wide range of audiences can access.

GitHub provides its basic services at no cost. Its more sophisticated professional and enterprise offerings are commercial. Its free GitHub accounts are employed to manage open-source software. The company is headquartered in California and has been a division of Microsoft since the year 2018.

We highly recommend creating an account on GitHub account If you don’t have one already. It’s completely cost-free and will provide one of the best platforms to present your work and collaborate with other fantastic Data Science members from the community.

IDEs:

An integrated Development Environment (IDE) is an application that offers comprehensive capabilities for creating and interpreting software. It offers a platform for enthusiasts, coders, and developers to play around with and understand code and programs using the editor of source code, automation tools, and the ability to debug.

An IDE can use a specific programming language such as Pycharm, a Python-only language, or offer a wide range of programming languages, as with Visual Studio Code. Since Python is a favored language in the current era, it supports a wide range of development software available, including Pycharm, Visual Studio Code, Jupyter notebooks, etc.

For instance, Visual Studio Code is a free source code editor developed by Microsoft for Windows, Linux, and macOS. Its features include support for syntax highlighting, debugging, clever code completion, code snippets of code, Refactoring code, and embedded Git. It is compatible with a variety of programming languages, including Python. 

There may be additional installation, to begin with, Python; however, it’s quite simple to install. It’s updated regularly and is among the most reliable platforms to use for Python and different programming languages.

GPUs:

Graphics Processing Unit Graphics Processing Unit is a special electronic circuit designed to quickly alter and manipulate memory to create images inside frames buffers intended to be output to the display device.

GPUs are an integral part of the modern world of computing. GPU computation and high-performance networks are changing the field of computational science as well as AI. The technological advances made in GPUs are a major contributor to the rise of deep learning.

GPUs are designed to train artificial intelligence models and deep learning because they can process multiple calculations simultaneously. They are equipped with a lot of cores that allow for the faster computation of many simultaneous processes.

NVIDIA provides a Compute Unified Device Architecture (CUDA) service, vital for supporting numerous deep learning applications. CUDA is an open-source parallel computing platform and an application programming interface design model that Nvidia developed.

IBM Watson Studio:

Watson Studio, formerly Data Science Experience or DSX IBM’s platform for software explicitly designed for data science. It is an open workspace that allows for numerous collaboration tools and open-source tools used for data-related.

Data Science initiatives. Using IBM Watson Studio, users can manage the AI lifecycle by using Auto AI to prepare, refine, explore, and test models. It can also create open-source notebooks that can be shared to run and train models in the cloud, and finally, launch data science projects.

With Watson Studio, a data scientist can design projects with collaborators, each having access to different analytics models and working with a different language (R/Python/Scala).

Watson Studio brings together staple open-source tools like R Studio, Spark, and Python in a unified environment and other tools, such as a controlled Spark tool and data sharing capabilities that are secure and controlled environments.

Amazon Web Services:

Amazon Web Services (AWS) is an affiliate of Amazon that provides cloud computing platforms on-demand and APIs for individual businesses, government agencies, and individuals on a pay-as-you-go metered basis. 

Cloud computing web services offer an array of fundamental abstract technical infrastructures and distributed computing building blocks and tools.

One of the services offered is Amazon Elastic Compute Cloud (EC2) which lets users have access to the possibility of a virtual cluster of computers accessible 24/7 via the Internet. 

The virtual computer version that AWS offers mimics the characteristics of a traditional computer, which includes physical central processing units (CPUs) along with graphic processing units (GPUs) for processing memory local/RAM, hard-disk/SSD storage, and a variety of operating systems, network connectivity; along with pre-loaded software like databases, web servers, as well as customer relationship management (CRM).

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