In this, article we have discussed about Data analysis tools and technologies but before we discussed data analysis tools we should also try to understand what is Data Analysis?
What Is Data Analysis?
Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making.
A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis. There are several methods and techniques to perform analysis depending on the industry and the aim of the analysis.
Data Analysis Tools
The growing demand and importance of data analytics in the market have generated many openings worldwide. It becomes slightly tough to shortlist the top data analytics tools as the open source tools are more popular, user-friendly and performance oriented than the paid version. There are many open source tools which doesn’t require much/any coding and manages to deliver better results than paid versions.
There are the list of top 10 of data analytics tools, both open source and paid version, based on their popularity, learning and performance.
1. R Programming
R is a programming language and free software environment for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. It is widely used among statisticians and data miners for developing statistical software and data analysis.
2. Tableau Public
Tableau Public is a free software that connects any data source be it corporate Data Warehouse, Microsoft Excel or web-based data, and creates data visualizations, maps, dashboards etc. with real-time updates presenting on web.
3.Python
Python is an object-oriented scripting language which is easy to read, write, maintain and is a free open source tool. It was developed by Guido van Rossum in late 1980’s which supports both functional and structured programming methods.
Python is easy to learn as it is very similar to JavaScript, Ruby, and PHP.
4. RapidMiner
RapidMiner is a powerful integrated data science platform developed by the same company that performs predictive analysis and other advanced analytics like data mining, text analytics, machine learning and visual analytics without any programming
5.SAS
SAS is a programming environment and language for data manipulation and a leader in analytics.
6. Apache Spark
The University of California, Berkeley’s AMP Lab, developed Apache in 2009. Apache Spark is a fast large-scale data processing engine and executes applications in 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 effortless. Spark is also popular for data pipelines and machine learning models development.
7.Excel
Excel is a basic, popular and widely used analytical tool almost in all industries.
8. KNIME
KNIME is leading open source, reporting, and integrated analytics tools that allow you to analyze and model the data through visual programming, it integrates various components for data mining and machine learning via its modular data-pipelining concept.
9. QlikView
10. Splunk
