Data Science Research Tools in 2022

By: Mamta

Data Science Tools aim to extract relevant insights from enormous databases that can then be turned into actionable decisions. A data scientist’s task is to gather, cleanse, extract, modify, and forecast data for different domains like AI, machine learning, blockchain, etc. To do so, there is a need for the best data science tools. This article summarizes the most popular data science tools and technologies [1].

Data Cleaning and Acquisition Techniques

It is a huge problem for data-driven companies dealing with large volumes of data to transform raw data into meaningful and useful information for organizations. Data Acquisition and cleaning tools help the data scientists collect data from various sources and convert them into suitable formats for further processing.

• Scraper: Scraper [2] is a very simple and free tool for facilitating online research, it extracts data from web pages and put it into spreadsheets.
• Import.io: Import.io [3] is a paid data extraction tool that allows you to extract data from websites. Simply highlight what you require, and Import.io will guide you through the process and “realise” what you are looking for. Import.io will then extract, process, and provide information for you to analyse.
• Xplenty [4]: It is a paid cloud-based ETL (extract, transform and load) tool. With the help of the sophisticated on-platform transformation tools customers can clean, normalise, and convert their data while complying to best standards.
• Xtract.io: Xtract.io is an effective data extraction system that assists companies in converting unstructured data into structured data. It facilitates the extraction of PDFs, documents, emails, and web pages. They use sophisticated machine learning methods with human in the loop to extract the meaningful insights about the data.
• IBM Data Camp: It collects documents, extracts usable data, and feeds the documents into downstream processes. It identifies, extracts, and classifies content from unstructured data using NLP, sentiment analytics, and machine learning technologies.
• SAS – Data Integration Studio: It is a graphical user interface that allows you to create and manage data integration workflows. Data source can be any platform or an application.
• Octoparse: It is a client-side web scraping program for Windows. Data from websites may be transformed into an organized database without the need for any programming.

Data Analysis Tools

Analysts employ these data tools and technologies to help organizations make better, more informed choices while reducing costs and increasing revenues.

• SAS: It is a statistical modelling tool. It has a heavy cost and is generally used by big organizations. Furthermore, a number of SAS libraries and packages are not included in the base package and must be purchased separately.
• Spark: It is a well-known and widely used analytics engine. Spark provides a number of Machine Learning APIs that assist data scientists in predicting data.
• Weka: It is an open-source graphical user interface that enables implementing machine learning algorithms easier and more user-friendly, and perfect for beginners. Regression, grouping, visualisation, and information creation are only a few of the learning machines included in Weka.
• BigML: It is a leading Machine Learning organization. BigML, with its scalable software platform, has ushered in the Machine Learning as a Service (MLAAS) wave of innovation. It streamlines the building and deployment of smart applications driven by the predictive models.
• RapidMiner: Rapid Miner is a data science tool designed for non-programmers who need to analyse data quickly. It can integrate ML models into web apps like flask or nodeJS, as well as android, iOS, and other platforms. Data is loaded from a variety of frameworks, including Hadoop, Cloud, RDBMS, NoSQL, pdf, and many more. The data is then pre-processed and prepared using typical procedures, such as interpolating missing data, trimming outliers. It also trains AI models, which are then deployed in the production environment or on the cloud.
• DataRobot: It is a machine learning platform that allows data scientists of all skill levels to quickly construct and deploy reliable prediction models. To train and assess 1000s of models in R, Python, Spark MLlib, H2O, and other open source libraries, the DataRobot platform employs massively parallel computing. It looks for the optimal models for your dataset and prediction objective by combing through millions of potential combinations of algorithms, pre-processing stages, features, transformations, and tuning parameters.

Data Visualization Tools

These tools assist data scientists in breaking down raw data and demonstrating everything through charts, graphs, and videos because humans can better discover patterns visually than by looking at statistics.

• D3.js: It is a Javascript package that allows you to create and design interactive online visualisations. It’s a fantastic tool for developers working on applications or services that require client-side data visualisation and processing. The APIs for D3.js allow you to utilise its numerous functions to analyse data and produce dynamic visuals in a web browser.
• Tableau: Tableau is an end-to-end analytics and data visualisation platform that is powerful, secure, and versatile. Tableau is an interactive visualisation tool with impressive graphics. Tableau’s most important feature is its ability to connect to databases, tablets, and OLAP cubes. Tableau can also show geographic data and use these properties to construct the lengths and latitudes of maps. One can also utilise its analytics tool to analyse the data and create visuals.
• Google Fusion Tables: It is a Google online service that allows you to manage your data. Data tables are gathered, visualised, and shared using this service. Data may be visualised using bar charts, line plots, pie charts, timelines, scatterplots, and geographical maps and can exported in a CSV file format.
• Qlik: It is a visual analytics platform that can be used both by teams and individuals to visualise and uncover data. Using it data from a variety of databases, including IBM DB2, Cloudera Impala, Oracle, Microsoft SQL Server, Sybase, and Teradata, may be merged. It also provides a central location where users may exchange and obtain relevant data analysis.
• Infogram: It is a web-based data visualization platform. Infogram has more than 35 intelligent charts and more than 500 maps to help you see your data in a pleasant way. People may create and share digital charts, infographics, and maps with it. Infogram is a user-friendly tool, which converts data into infographics that can be published, embedded, or shared.

References

1. Baviskar, M. R., Nagargoje, P. N., Deshmukh, P. A., & Baviskar, R. R. (2021). A Survey of Data Science Techniques and Available Tools. International Research Journal of Engineering and Technology (IRJET) e-ISSN, 2395-0056.
2. Mahto, D. K., & Singh, L. (2016, March). A dive into Web Scraper world. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 689-693). IEEE.
3. https://www.import.io/
4. https://www.xplenty.com/

Cite this article:

Mamta (2021), Data Science Research Tools, Insights2Techinfo, pp.1

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