This article was published as part of the Data Science Blogathon
etc that computers can process and provide results. Data collection allows us to store, manipulate and analyze important information about our current and potential customers and discover valuable information. Today, data collection can help us understand our customers better and business has become relatively easy.
And most of the tech giants like Google, Facebook, Microsoft, IBM, Amazon Web Services, etc. and so many other large and small businesses are investing their valuable time and valuable resources heavily in data and, Thus, on the subject of data science. The rapid rise in recognition of data science has resulted in the creation of a variety of diverse tools and technologies for the benefit and benefit of data scientists..
Data science is an emerging field that uses various methods, processes, algorithms and techniques to extract meaningful insights and insights from a huge amount of structured and unstructured data. Data science also includes data mining, machine learning and big data. Combines the study of domain experience 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 in depth the fantastic tools that are extremely useful for developing and increasing data science skills and also for creating unique and practical projects. These tools can be used for model creation, the process, the analysis of results, implementation and much more.
Let us begin:
GitHub is a platform where developers can host their code for version control and collaboration. The main benefit of GitHub is its version control system, allowing developers to collaborate seamlessly with other developers without compromising the integrity of the original project. The projects hosted on GitHub are open source software. GitHub is a platform where more than 65 millions of developers shape the future of software, together. GitHub is the best place for developers to manifest their code and discuss projects with an exquisite community.
Now, knowledge of GitHub has become one of the basic requirements for a data scientist. Data scientists were able to use Github for a reason equivalent to what software engineers do to collaborate., make changes to projects and have the ability to track and reverse changes over time. Traditionally, data scientists didn't have to use GitHub, as the method of putting models into production was often handled by data engineering teams or software. It's free and will open one of the best places for developers to showcase their projects and collaborate with other amazing data scientists in the community..
Image source: developer community dev.to
An integrated development environment (HERE) is a software platform that provides developers with complete facilities to code and develop. It is a coding tool that allows you to write, test and debug code more efficiently, as these IDEs usually offer code completion or information about the code by highlighting them. IDEs help develop the integration of the different aspects of a computer program. IDE plays an essential role in the development of Data Science (DS) y Machine Learning (ML) due to its vast libraries. Choosing the right IDE that suits our needs is usually a very important task. Here is the list of some IDEs suitable for data science and machine learning:
- Google Colab
- Jupyter Notebook
- Visual Studio code
- Sublime text
A good IDE as a data scientist assistant to compile, debug, test code and make it bug free.
Image source: analyticsvidhya.com
3. Amazon web services (AWS)
Amazon Web Services is a subsidiary of Amazon Company that provides on-demand services from cloud computing platforms (IaaS, PaaS, SaaS) and API to many people, companies and governments, based on a pay-as-you-go meter. These cloud computing web services provide a variety of building blocks and tools for distributed computing along with an abstract technical infrastructure.. Data scientists rely on both business and the technical world with data analysis to achieve desired results. 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 your business goals.
Today, in 2021, AWS comprises more than 200 products and services including cloud computing, cloud storage, networks, database administration, analysis of data, application deployment, machine learning, mobile development, developer tools, Internet of things and various other tools and services.
Image source: analyticsvidhya.com
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 data sets for data science and machine learning., explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers in the community, y You can also enter contests to solve data science challenges. Kaggle was introduced to 2010 by offering machine learning competencies and now also offering a public platform for data, a wide desktop for cloud data scientists and also artificial intelligence education. Kaggle has organized hundreds of machine learning contests and these contests have developed many successful projects, including HIV research, chess ratings and traffic forecast.
Image source: analyticsvidhya.com
5. Stack overflow
Stack Overflow is a SaaS platform for collaboration and knowledge sharing for companies and also for programmers. Stack Overflow features questions and answers on a good variety of programming topics for IT professionals and enthusiasts.. It was developed in 2008 by Jeff Atwood and Joel Spolsky and the Stack Exchange Network flagship site. It is an open source community for developers to work together and help each other.
Until March 2021, Stack Overflow registró 14 million registered users and received more than 21 million questions and 31 million responses. Most of the discussed questions are based on Java, Python, R, Android and many more.
Image source: medium.com
In this blog, We have discussed the most basic and essential data science tools that every aspiring data science should know. These tools help build skills and get updates on hot data science technologies..
Thank you for reading. Please let me know if there are any comments or feedback.
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