Top 10 data analysis tools

The growing demand and importance of data analytics in the market has generated many openings around the world. It becomes a bit difficult to shortlist the top data analytics tools, as the open source tools are more popular, easier to use, and more performance-oriented than the paid version. There are many open source tools that do not require much / no coding and manage to deliver better results than paid versions, for example R programming in data mining and Tableau public, Python in data visualization. Below is the list of the top 10 data analytics tools, both open source and paid, based on popularity, learning, and performance.

1. R programming

R is the industry leading analysis tool and is widely used for statistics and data modeling. You can easily manipulate your data and present it in different ways. It has outperformed SAS in many ways, such as data capacity, performance, and results. R compiles and runs on a wide variety of platforms, namely UNIX, Windows, and MacOS. It has 11,556 packages and you can browse the packages by categories. R also provides tools to automatically install all packages based on user requirements, which can also be assembled well with Big Data.

2. Public table:

Tableau Public is free software that connects any data source, be it a corporate data warehouse, Microsoft Excel, or web-based data, and creates data visualizations, maps, dashboards, and more. with real-time updates that are presented on the web. They can also be shared through social networks or with the client. It allows access to download the file in different formats. If you want to see the power of the frame, we must have a very good data source. Tableau’s Big Data capabilities make them important and one can analyze and visualize data better than any other data visualization software on the market.

3. Python

Python is an object-oriented scripting language that is easy to read, write, maintain, and is a free, open source tool. It was developed by Guido van Rossum in the late 1980s, which supports both functional and structured programming methods.

Python is easy to learn as it is very similar to JavaScript, Ruby, and PHP. Also, Python has very good machine learning libraries viz. Scikitlearn, Theano, Tensorflow, and Keras. Another important feature of Python is that it can be assembled on any platform such as a SQL server, a MongoDB database, or JSON. Python can also handle text data very well.

4. SAS

Sas is a leading analytics language and programming environment for data manipulation, developed by the SAS Institute in 1966 and developed in the 1980s and 1990s. SAS is easily accessible, manageable, and can analyze data from any source. SAS introduced a large suite of products in 2011 for customer intelligence and numerous SAS modules for marketing, social media and web analytics that are widely used for customer and prospect profiling. You can also predict their behaviors, manage and optimize communications.

5. Apache Spark

Apache was developed by the University of California, Berkeley’s AMP Lab, in 2009. Apache Spark is a fast, large-scale data processing engine, running applications on Hadoop clusters 100 times faster in memory and 10 times faster on disk. Spark is built on data science and its concept makes data science easy. Spark is also popular for developing data pipelines and machine learning models.

Spark also includes a library, MLlib, which provides a progressive set of machine algorithms for repetitive data science techniques such as classification, regression, collaborative filtering, clustering, and more.

6. Excel

Excel is a basic, popular, and widely used analytical tool in almost every industry. Whether you’re a Sas, R, or Tableau expert, you’ll still need to use Excel. Excel becomes important when there is a requirement for analysis of internal customer data. Analyze the complex task that summarizes the data with a PivotTable preview that helps filter the data based on customer requirements. Excel has the advanced business analysis option that aids in modeling capabilities that have predesigned options such as automatic relationship detection, DAX measurement creation, and time grouping.

7. RapidMiner:

RapidMiner is a powerful integrated data science platform developed by the same company that performs predictive analytics and other advanced analytics like data mining, text analytics, machine learning, and visual analytics without any programming. RapidMiner can be incorporated with any type of data source, including Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase, IBM DB2, Ingres, MySQL, IBM SPSS, Dbase, etc. The tool is very powerful and can generate analytics based on real-life data transformation settings, that is, it can control data sets and formats for predictive analytics.

8. KNIME

KNIME Developed in January 2004 by a team of software engineers from the University of Konstanz. KNIME is a leading open source, reporting and embedded analytics tool that enables you to analyze and model data through visual programming, integrates various components for data mining and machine learning through its modular data pipeline concept .

9. QlikView

QlikView has many unique features like proprietary technology and has in-memory data processing, which executes the result very fast for end users and stores the data in the report. The data association in QlikView is automatically maintained and can be compressed to almost 10% of its original size. The relationship of data is displayed using colors: a specific color is given to related data and another color to unrelated data.

10. Splunk:

Splunk is a tool that analyzes and searches the data generated by the machine. Splunk extracts all text-based log data and provides an easy way to search through it, a user can extract all kinds of data and perform all kinds of interesting statistical analysis on it and present it in different formats.