Data is a collection of facts and information like numbers, words, measurements, observations, etc that computers can process and provide results. The collection of data allows us to store, manipulate, and analyze important information about our existing and potential customers and find out meaningful insights. Today, gathering data can assist us for better understanding of our customers and business is became comparatively easy.
And most of the tech giants like Google, Facebook, Microsoft, IBM, Amazon Web Services, etc. and so many other major and minor companies are heavily investing their valuable time and precious resources into data and therefore the subject of data science. The rapid climb of the recognition of data Science has resulted in the creation of an array of various tools and technologies for the profit and benefit of Data scientists.
Data science is an emerging field that uses various methods, processes, algorithms, and techniques to bring out meaningful knowledge and insights from an enormous amount of structured and unstructured data. Data science also includes data mining, machine learning, and big data. It combines the study of domain expertise and programming skills using techniques and theories drawn from many fields within the context of mathematics, statistics, computing, domain knowledge, and information science.
In this blog, we will discuss and understand deep into the fantastic tools that are extremely helpful in developing and growing Data Science skills and for creating unique and practical projects also. These tools can be utilized for model building, processing, analyzing results, deployment purposes, and so much more.
Let’s get started:
GitHub is a platform where developers can host their code for version control and collaboration. The primary benefit of GitHub is its version control system, which allows developers to uninterrupted collaborate with other developers without compromising the integrity of the original project. The projects hosted over GitHub are open-source software. GitHub is a platform where more than 65 million developers shape the future of software, together. GitHub is the best place for developers to manifest their codes and discuss projects with an exquisite community.
Now, Knowledge of GitHub has become one of the basic requirements for a Data Scientist. Data scientists got to use Github for an equivalent reason that software engineers do for collaboration, making changes to projects, and having the ability to trace and roll back changes over time. Traditionally Data Scientists didn’t have to use GitHub, as often the method of putting models into production was handled by software or data engineering teams. It is free and will open up one of the best places for developers to showcase their projects and collaborate with other amazing Data Scientists from the community.
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An integrated development environment (IDE) is a software platform that provides developers with comprehensive facilities to code and develops. It’s a coding tool that allows writing, testing, and debugging code more efficiently, as these IDEs typically offer code completion or code insights by highlighting them. IDEs help develop integrating the different aspects of a computer program. IDE plays an essential role in the development of Data Science (DS) and Machine Learning (ML) due to its vast libraries. Choosing the right IDE that suits our needs is often a most significant task. Here is the list of some IDEs suited for Data Science and Machine learning:
- Google Colab
- Jupyter Notebook
- Visual Studio Code
- Sublime Text
A good IDE like an assistant to Data Scientists to compile, debug, test code, and make it error-free.
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3. Amazon Web Services (AWS)
Amazon Web Services is a subsidiary of Amazon Company offering on-demand services of cloud computing platforms (IaaS, PaaS, SaaS) and APIs to many individuals, companies, and governments, based on a meter pay-as-you-go. These cloud computing web services provide a variety of basic building blocks and tools for distributed computing along with abstract technical infrastructure. Data scientists bestride on both businesses as well as the technical world with Data Analysis to achieve desired outcomes. In the field of Machine Learning (ML), Data Scientists design, develop, and build models from data by processing it, create and work on various algorithms, and train the models to predict and achieve their business goals.
Today in 2021, AWS comprises over 200 products and services including Cloud computing, Cloud Storage, Networking, Database Management, Data Analytics, Application Deployment, Machine Learning, Mobile development, Developer Tools, the Internet of things, and various other tools and services.
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Kaggle is a subsidiary created by Google LLC. It is an online platform for Data Scientists and Machine Learning enthusiasts. Kaggle is an open community that allows users to find and publish various datasets for data science and machine learning, explore and build models in a web-based data-science environment, work with various other data scientists and machine learning engineers in the community, and can also participate in competitions to solve data science challenges. Kaggle was introduced in 2010 by providing Machine Learning competitions and now also offers a public platform for data, a broad desk for data scientists over the cloud, and also Artificial Intelligence education. Kaggle has run hundreds of machine learning competitions and these competitions have evolved many successful projects including HIV research, chess ratings, and traffic forecasting.
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5. Stack Overflow
Stack Overflow is a collaboration & knowledge-sharing SaaS platform for companies and as well as for programmers. Stack Overflow features questions and answers on a good range of topics in programming for IT enthusiasts and professionals. It is developed in 2008 by Jeff Atwood and Joel Spolsky and flagship site of Stack Exchange Network. It is an open-source community for developers to work together and help each other.
Till March 2021, Stack Overflow has recorded 14 million registered users and received over 21 million questions and 31 million answers. Most of the questions discussed are based on Java, Python, R, Android, and many more.
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In this blog, we have discussed the most basic and essential data science tools that every data science aspirant should know. These tools help to grow the skills as well as get updates about the trending data science technologies.
Thanks for reading. Do let me know if there is any comment or feedback.
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