The increasing demand and importance of data analytics in the market have generated many openings all over the world. It becomes a bit difficult to select the top data analysis tools as the open source tools are more popular, user friendly and performance oriented than the paid version. There are many open source tools that do not require much or any coding and manage to deliver better results than the 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 leading analysis tool in the industry 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, including 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 allows you to browse packages by category. R also provides tools to automatically install all packages based on user requirements, which can also be well assembled with Big data.

2. Public Meeting:

Tableau Public is free software that connects any data source, be it 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 Tableau, then you must have a very good data source. Tableau’s Big Data capabilities make it 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 and 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 like SQL server, MongoDB or JSON database. Python can also handle text data very well.

4.SAS

SAS is a leading analytics and data manipulation programming language and environment, developed by the SAS Institute in 1966 and further 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 web, social media, and marketing analytics that are widely used to profile customers and prospects. You can also predict their behaviors, manage and optimize communications.

5 Spark Apache

The AMP Lab at the University of California, Berkeley, developed Apache 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 data pipelines and machine learning model development.

Spark also includes a library, MLlib, which provides a progressive set of machine algorithms for boilerplate data science techniques like Classification, Regression, Collaborative Filtering, Clustering, etc.

6. stand out

Excel is a basic, popular and widely used analytical tool in almost all industries. If you’re an expert in Sas, R, or Tableau, you’ll still need to use Excel. Excel becomes important when there is a requirement for analysis of internal customer data. It analyzes the complex task that summarizes the data with a PivotTables preview that helps to filter the data based on customer requirements. Excel has the option of advanced business analysis that helps in modeling capabilities that have pre-built options like automatic relationship detection, DAX measure creation, and time binning.

7. Fast Miner:

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 programming. RapidMiner can embed 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 configurations, i.e. you can control the data formats and data sets for predictive analytics.

8. KNIFE

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

9.QlikView

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

10. Splunk:

Splunk is a tool that analyzes and searches the data generated by machines. 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.