7 Data Analysis Tools You Should Know

Data analysis as a discipline continues to evolve and establish itself in the broader market. More and more companies are allocating funds to attract specialized professionals and are looking for data analysis tools that will allow them to become more competitive players.

Finding ways to stand out from competitors provides an opportunity and advantage to those who have managed to find quality data analysis training and learn to use some of the essential tools used today. In this article, we will look at seven essential data analysis tools you should know. Let's go!

Here Are the Data Analysis Tools You Should Know

So, here is a list of 7 data analysis tools you should know. If you want to work in this field, you need to master some of them:

Zoho Analytics

Zoho Analytics is a platform used, in particular, for business intelligence projects. This tool is used to create reports and analyze collected data. Thus, it helps professionals access key business metrics as well as visualize trends and patterns.

One of the advantages of this tool, making it one of the most important data analysis tools today, is the creation of personalized dashboards. Additionally, it is easy to create alerts and use result forecasting tools.

Thanks to this, data analysts can make predictions and improve decision-making efficiency in the future.

Tableau Public

Like Zoho Analytics, Tableau Public is one of the data analysis tools used within Business Intelligence processes. In this case, the application facilitates the analysis of historical data as a basis for companies to make important strategic decisions.

Tableau Public allows companies to process information and shape it so that it can then be presented using various data visualization tools. The tool enables the creation of simple and intuitive visualizations.

In the final stage, this software allows experts and companies to upload the results and share them with clients and other partners.

Rapid Miner

Rapid Miner is a data analysis tool focused on machine learning. It is entirely oriented towards creating various analytics and tracking the various transformations that data undergoes in real time.

This software can be integrated with other useful data analysis tools. It allows professionals to access data collected and stored in both traditional databases and other applications such as Access or Excel.

As for integration with Machine Learning, this tool is intuitive, so no programming knowledge is required to find and use various machine learning functions.

Hadoop

Apache Hadoop is a framework used for processing and analyzing data in the field of Big Data. It represents a framework with its own ecosystem of applications and related technologies. Moreover, it is one of the original tools in the world of data analysis.

Hadoop libraries provide simple programming models that are mainly used for distributed storage of information models. This means Hadoop operates through a cluster of interconnected computers.

Apache Hadoop distributes workload and data storage to make processing faster and more efficient.

MongoDB

MongoDB is a NoSQL database, which makes it particularly interesting for storing unstructured data. Moreover, MongoDB has a broader scope of application than traditional NoSQL databases.

Thus, it is a useful tool for various projects, especially those aimed at achieving scalability. Thanks to its capabilities, one can easily and seamlessly create a data system for horizontal scaling.

Apache Spark

Apache Spark is an engine used for data processing. It is an open-source tool used in distributed programming, making it an accessible system for data scientists.

It can be used as a complement to Hadoop in data processing, as it is 100 times faster than Hadoop for in-memory processes and 10 times faster on disk platforms.

Python

Python is one of the main programming languages used in data analysis, Data Science, and artificial intelligence. Over the years, this language has become increasingly popular and today represents one of the most effective data analysis tools.

Of course, it is a complex programming language with a steeper learning curve than other Big Data tools, but it is worth it as it is one of the most comprehensive.

One of the significant drawbacks of Python today is its computing power, which is lower than that of other data analysis tools. For this reason, Python is typically used in integration tasks, other tasks that do not require heavy computation, as well as in software testing.