7 Most Widely Used Tools in Data Science and ML in 2022

Data science and Machine learning are together two of the most buzzing career spaces in the technology arena today. All thanks to the data explosion in the 21st century that has created the need for data scientists who can make sense of this huge data.

The data science and machine learning domains attract not just lucrative compensation, but also assure professionals working in the field of a high degree of professional satisfaction.

A variety of tools are used in both the Data science and machine learning fields by data science professionals to analyze and process data, and create algorithms and predictive models. In this article, we are going to take a look at the seven most widely used data science and machine learning tools.

Seven most widely used data science and machine learning tools

1.Apache Spark

Massive amounts of data are needed to effectively train Machine Learning models.

While earlier, the creation of precise machine learning models was not possible due to a lack of large data and computing power, the case is different today. Big data processing frameworks such as Apache Spark make this possible. Apache Spark or Spark is a potent analytics engine. As one of the most widely used data science tools, Spark is designed to carry out batch processing and Stream Processing.

Spark has many APIs that enable Data Science professionals to have unhindered access to data for Machine Learning, SQL storage, etc. It is more efficient than other data processing frameworks such as Hadoop, and MapReduce.

2.BigML

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.

3.D3.js

D3.is a front-end Javascript library used by data science professionals to create dynamic visualizations. Using the numerous APIs of D3.js available, data scientists can create powerful visualization, helping in the analysis of data.

D3.js also enables the usage of animated transitions. Documents made by D3.js have a dynamic feature, thanks to its ability to allow updates on the front-end, and a unique feature that enables data changes to be shown as browser visualizations.

4.Tableau

Tableau is a type of interactive data visualization tool that is commonly used by data scientists. It is one of the most widely used data visualization software in the industry. With its drag and drop interface, the simplicity and convenience it offers in performing tasks briskly make it a favourite among data professionals.

Key features of Tableau:

• Simplest and most hassle-free business intelligence software for data visualization
• No need for Data scientists to write custom code when using Tableau.
• Offers real-time collaboration and data mixing facility

5.Matplotlib

Matplotlib is, again, a commonly used data science tool. As a plotting and visualization library, it was developed for Python. Matplotlib is used to create graphs to represent the analyzed data visually. Data scientists and analysts use Matplotlib for plotting complex graphs using simple lines of code. Matplotlib can be used to produce other forms of visualization such as bar plots, histograms, scatterplots etc.

6.SAS

SAS is an ideal data science tool for statistical data analysis. It is a licensed software that is used for data analysis.SAS makes use of the base SAS programming language in order to handle tasks of statistical modeling. SAS helps in the retrieval, reporting, and analysis of statistical data. Besides, SAS helps in visualization through graphs. Some versions of SAS even provide the facility of reporting of machine learning, data mining, time series, etc.

7.Scikit-learn

If there is one machine learning tool that deserves a mention, it has to be Scikit-learn. Being a Python-based library, scikit-learn is used for implementing Machine Learning Algorithms. Scikit-learn provides support for a number of machine learning features and algorithms. These include data preprocessing, classification, regression, clustering, dimensionality reduction, and more.

Conclusion

Data science and machine learning are the future of technology space. All industries, businesses, and organizations are steadily adopting data science and machine learning in their business processes and workflows. Data science has great benefits in terms of predictive planning, efficiency improvements, and cost-optimization for businesses. This has opened many career avenues for skilled data science and machine learning professionals. These data science and ML tools viz. Apache Spark, Big ML, D3.js, Tableau, Matplotlib, SAS, and Scikit-learn simplify the life of data science and ML professionals.

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