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Tools For Data Science

Tools For Data Science

Data Scientist is a person who is actually accountable for extracting, manipulating, pre-processing as well as generating predictions out of data. In order to really do so, he requires various statistical tools and programming languages. In this article, we’ll be sharing a number of the Tools for Data Science that are utilized by Data Scientists to carry out their data operations. We will understand the key features of the tools, the benefits they actually provide, and the comparison of various different data science tools. Here is the list of the best data science tools that most data scientists used.

1. SAS

It is one among those data science tools which are specifically designed for statistical operations. SAS is actually a closed source proprietary software that’s utilized by large organizations to analyze data. Moreover, SAS uses a base SAS programming language for performing statistical modeling. It is one of the most widely utilized tools by professionals as well as companies working on reliable commercial software. SAS offers a variety of statistical libraries as well as tools that you as an amazing Data Scientist can use for modeling and organizing their data. While SAS is really reliable and has very strong support from the company, it’s highly expensive and is merely used by larger industries. Also, SAS pales as compared with some of the more modern tools which are open-source. Furthermore, there are several libraries and packages in SAS that aren’t available in the base pack and may require an expensive upgradation.

2.  Apache Spark

Apache Spark or just Spark is an all-powerful analytics engine and it’s the most used Data Science tool. Spark is specifically designed in order to handle batch as well as stream processing. It comes with many APIs that facilitate Data Scientists in order to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can actually perform 100 times faster than MapReduce. Spark has many Machine Learning APIs which will help Data Scientists actually make powerful predictions with the given data.

Apache Spark does better than several other Big Data Platforms because of its ability to handle streaming data. This implies that Spark can actually process real-time data as compared to other analytical tools that simply only process historical data in batches. Spark offers various different APIs that are programmable in Java, Python and R as well. But the most powerful conjunction of Spark is with Scala programming language which is simply based on Java Virtual Machine and is cross-platform in nature.

Spark is extremely efficient in cluster management which makes it far better than Hadoop because the latter is merely used for storage. It is this cluster management system that enables Spark to process application at a high speed.

3.  Excel

Probably the most widely used Data Analysis tool. Microsoft developed Excel mostly for spreadsheet calculations and today, it’s widely used for data processing, visualization, and complicated calculations. Excel is a rather powerful analytical tool for Data Science. While it’s been the standard tool for data analysis, Excel still packs a punch.

Excel is a Data Analysis tool that comes with various different filters, formulae, tables, slicers and many more. In addition, you can also create your very own custom functions and formulae with the help of Excel. While Excel isn’t for calculating a large amount of data, it’s still a perfect choice for creating powerful data visualizations and spreadsheets. You can also connect SQL with Excel and can further use it to manipulate and analyze data. Multiple Data Scientists use Excel for data cleaning because it actually provides an interactable GUI environment to pre-process information easily.

4.  Python

Nowadays, Python is one of the most dominant languages for data science within the industry due to its ease, open-source nature, and flexibility. Phyton one tool that has gained rapid popularity as well as acceptance within the ML community

 

5.  SQL

SQL is one of the most famous data management systems which has been around since the 1970s. It was one of the primary database solutions for some decades. SQL still remains famous, however, there’s a drawback, which is that it becomes difficult to scale because the database continues to grow.

 

6.  Hadoop

Hadoop is an open-source distributed framework that manages data processing as well as storage for big data. You are most likely to come across this tool whenever you build a machine learning project right from scratch.

7.  Hive

It is actually a data warehouse built on top of Hadoop. A hive is a tool that provides a SQL-like interface to query the data stored in various different types of databases as well as file systems that integrate with Hadoop.

8. Tableau

Tableau is one of the most famous data visualization tools within the market today. It is really capable of handling large amounts of data as well as offers Excel-like calculation functions and parameters too. Tableau is quite popular because of its neat dashboard and story interface.

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