Category: 1. Most Used Data Science Tools

Javascript is mainly used as a client-side scripting language. D3.js, a Javascript library allows you to make interactive visualizations on your web-browser. With several APIs of D3.js, you can use several functions to create dynamic visualization and analysis of data in your browser.

Another powerful feature of D3.js is the usage of animated transitions. D3.js makes documents dynamic by allowing updates on the client side and actively using the change in data to reflect visualizations on the browser.

You can combine this with CSS to create illustrious and transitory visualizations that will help you to implement customized graphs on web-pages.

Overall, it can be a very useful tool for Data Scientists who are working on IOT based devices that require client-side interaction for visualization and data processing.

BigML, it is another widely used Data Science Tool. It provides a fully interactable, cloud-based GUI environment that you can use for processing Machine Learning Algorithms. BigML provides standardized software using cloud computing for industry requirements.

Through it, companies can use Machine Learning algorithms across various parts of their company. For example, it can use this one software across for sales forecasting, risk analytics, and product innovation.

BigML specializes in predictive modeling. It uses a wide variety of Machine Learning algorithms like clustering, classification, time-series forecasting, etc.

BigML provides an easy to use web-interface using Rest APIs and you can create a free account or a premium account based on your data needs. It allows interactive visualizations of data and provides you with the ability to export visual charts on your mobile or IOT devices.

Furthermore, BigML comes with various automation methods that can help you to automate the tuning of hyperparameter models and even automate the workflow of reusable scripts.

Apache Spark or simply Spark is an all-powerful analytics engine and it is the most used Data Science tool. Spark is specifically designed to handle batch processing and Stream Processing.

It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can perform 100 times faster than MapReduce.

Spark has many Machine Learning APIs that can help Data Scientists to make powerful predictions with the given data.

Spark does better than other Big Data Platforms in its ability to handle streaming data. This means that Spark can process real-time data as compared to other analytical tools that process only historical data in batches.

Spark offers various APIs that are programmable in Python, Java, and R. But the most powerful conjunction of Spark is with Scala programming language which is based on Java Virtual Machine and is cross-platform in nature.

Spark is highly efficient in cluster management which makes it much better than Hadoop as the latter is only used for storage. It is this cluster management system that allows Spark to process applications at a high speed.

It is one of those data science tools which are specifically designed for statistical operations. SAS is a closed source proprietary software that is used by large organizations to analyze data. SAS uses base SAS programming language which for performing statistical modeling.

It is widely used by professionals and companies working on reliable commercial software. SAS offers numerous statistical libraries and tools that you as a Data Scientist can use for modeling and organizing their data.

While SAS is highly reliable and has strong support from the company, it is highly expensive and is only used by larger industries. Also, SAS pales in comparison with some of the more modern tools which are open-source.

Furthermore, there are several libraries and packages in SAS that are not available in the base pack and can require an expensive upgradation.