Top 10 Data Analysis Tools
The growing demand and importance of data analysis in the market have generated many openings around the world. It becomes a bit difficult to select the top data analytics tools as the open source tools are more popular, easy to use and performance oriented than the paid version. There are many open source tools that don’t require much or 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 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 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 allows you to 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 Meeting:
Tableau Public is free software that connects any data source, whether corporate data warehouse, Microsoft Excel, or web-based data, and creates data visualizations, maps, dashboards, and more. 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 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, a MongoDB or JSON database. Python can also handle text data very well.
4.SAS
SAS is a leading data manipulation and analysis 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 set 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 Apache spark
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 100x faster in memory and 10x 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 repetitive 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. Analyze the complex task summarizing data with a pivot table preview that helps filter data based on customer requirements. Excel has advanced business analytics option that helps in modeling capabilities that have pre-built options like automatic relationship detection, DAX measure creation and time pooling.
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 incorporate 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 setups, i.e. you can control the data formats and 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 reporting and analytics 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 such as proprietary technology and in-memory data processing, which renders the output very fast for end users and stores the data in the report itself. 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.