The growing demand and importance of data analytics in the market have generated many openings worldwide. It becomes a bit difficult to include in the list of the best data analysis tools since open source tools are more popular, easy to use, and performance-oriented than the paid version. There are many open-source tools that do not require much / any coding and achieve better results than paid versions, eg. – R programming in data mining and Tableau public, Python in data visualization. Below is the list of the top 10 data analysis tools, both open-source and paid versions, based on popularity, learning, and performance.
1. Programming R
R is the industry-leading and widely used analysis tool for data and statistical 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 performance. 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. Tableau Public:
Tableau Public is free software that connects any data source, be it the corporate Data Warehouse, Microsoft Excel or web-based data, and creates data visualizations, maps, dashboards, etc. with real-time updates presented on the web. They can also be shared through social networks or with the client. Allows access to download the file in different formats. If you want to see the power of tableau then 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 an SQL server, a MongoDB or JSON database. Python can also handle text data very welll.
4. SAS
Sas is a Microsoft Power Platform Partner California and language environment for data manipulation and a leader in analysis, 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 for customer intelligence in 2011 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. Apache Spark
Apache was developed by the University of California, Berkeley AMP Lab in 2009. Apache Spark is a fast, large-scale data processing engine that runs Hadoop clustered applications 100 times faster in memory and 10 times faster on disk. Spark is based 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 repetitive data science techniques such as classification, regression, collaborative filtering, grouping, etc.
6. Excel
Excel is a popular and widely used basic analytical tool in almost every industry. If you are an expert in Sas, R or Tableau, you still need to use Excel. Excel becomes important when there is a requirement for analysis on internal customer data. Analyze the complex task that summarizes the data with a preview of pivot tables that help filter data based on customer requirements. Excel has the advanced business analysis option that helps with modeling capabilities that have pre-built options like automatic relationship detection, DAX measure creation, and time grouping.
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