What Are Some Essential Tools And Technologies For Data Science?

The rapidly growing popularity of machine learning and data science has led to the development of a wide range of tools and technologies. Data science is an evolving concept that deals with combining statistics, data analysis and other operations. There are many data science software tools in the market that can be used for data analysis in larger companies. This article further takes you through the top 10 data science tools which are very essential in the field of data science. You can learn about these tools and technologies in a full-time data science & machine learning course offered by various institutions. Let’s take a quick look at some of the most crucial tools and technologies used in data science. 

Some Important Tools and Technologies for Data Science: 

Here is a list of essential tools and technologies which are popularly used in the field of data science by programmers. All these tools are very efficient in working and can effectively perform all functions related to data, including data measurement and analysis. 

1. BigML

BigML is a tool that is widely used in data science. It offers an interactive cloud-based environment of GUI that can be used to run machine learning algorithms. BigML also serves as benchmark software with the help of cloud computing to fulfil industry needs. By using it, companies can utilise these machine learning algorithms in different sectors of the company. The special ability of BigML is predictive modelling. It utilises a wide range of machine learning algorithms such as time-series forecasting, classification and clustering. 

2. Excel

Excel is one of the most popular data science tools in demand. It was developed by Microsoft mainly for spreadsheet estimations, and now, it is used to process data, complex calculations and visualisation. There are different filters, formulae and slicers provided by excel. However, excel can’t be effectively used to calcite large amounts of data. But, it is a good option to create robust spreadsheets and data visualisation. You can link SQL with excel and use it for data manipulation and analysis. Many data scientists utilise excel for data cleaning as it delivers an interactive GUI platform to process information seamlessly. 

3. TensorFlow 

TensorFlow is a standardised tool and one of the reputed technologies used in data science and machine learning. It is used for strong machine learning algorithms such as Deep Learning. TensorFlow got its name from its developers from Tensors which are multifaceted arrays. It is an open-ended and rapidly developing toolkit that has a reputation because of its performance. This tool can be used on both GPUs and CPUs and has currently evolved on more robust TPU platforms. Because of its high processing capacity, this tool has a wide range of applications like image classification, speech recognition, drug discovery, and language and image generation. 

4. Jupyter 

Jupyter is one of the best data science tools for beginners. It is based on Ipython and helps developers to make open-ended software. This tool supports several languages like R, Python and Julia. It is a web-app tool that is used to write live code, presentations and visualisation. This tool is very popular and has been created to meet the needs of data science. It is an interactive environment via which data scientists can accomplish their tasks. It is also used for storytelling due to the presentation features it possesses. 

5. SAS 

SAS is one of the few data science tools which are designed particularly for statistical functions. SAS is close-ended software that is used by big companies for data analysis. It utilises basic SAS commanding language to perform statistical modelling. It is used widely by companies and professionals working on commercial software. This tool offers several statistical libraries and tools that can be used by data scientists for data organising. SAS is a very reliable tool and is strongly supported by the company. It is quite expensive and is used only by larger companies. Also, there are many packages and libraries in SAS that can be availed in the basic pack and need high-cost upgrades. 

6. GitHub

Being familiar with the concept of GitHub is one of the most crucial requirements for a data scientist. GitHub is the best platform to display codes and talk about projects with a community. You can also share your work in code blocks in the Gist form, which can be operated by a vast group of audience who visit your profile. All the primary services of this tool are free to use. It offers access control and many collaborative features like task management, feature requests, bug tracking and constant integration for each project. 

7. IBM Watson Studio

IBM Watson Studio is IBM’s own software used for machine learning and data science. The platform comprises a workspace that has many open-ended tools used in data science. This tool enables the users to organise the AI lifecycle with functions like prepare, Auto AI, refine, prepare and develop open-ended notebooks that can be shared easily. This tool gives you access to various data sets that are available on this platform, including both on the cloud and on-premises. It is also considered to be one of the best tools for data science in python. 

8. Microsoft Azure 

Microsoft Azure, also known as Azure, is a cloud computing platform developed by Microsoft. It is used to build, test, deploy and manage applications through the data centres managed by Microsoft. It provides a SaaS (Software as a Service), IaaS (Infrastructure as a Service) and PaaS (Platform as a Service). It also supports a lot of different programming frameworks, tools and languages. Both Microsoft-specific tools and third-party applications are supported on this platform. 

Conclusion 

So, mentioned above are some of the latest technologies in data science. You must know about these tools and technologies if you want to have a career in data science and machine learning. These tools are widely used by programmers in data analysis and many other uses. 

Also Read: Using A Web Crawler In Data Science