Redacción Tokio | 31/01/2023
Data analysis as a discipline continues to grow and gain a foothold in the wider market. More and more companies are devoting resources to hiring specialized professionals and looking for data analysis tools that turn them into more competitive players.
This search for differentiation from the competition represents an opportunity and an advantage for those able to find quality training in data analysis and learn to employ some of the main tools used today. In this article we are going to review the top seven tools for data analysis that you should know about. Let’s go!
These are the data analysis tools you should know about
Without further ado, here’s the list of the 7 data analysis tools you should know about. If you want to work in this field, it’s necessary for you to master some of these:
Zoho Analytics is a platform employed for Business Intelligence projects in particular. This tool is used to generate reports and to analyze the data collected. Thus, it helps professionals access key business-wide metrics and helps them visualize trends and patterns.
One of this tool’s strengths, which makes it one of the most important data analysis tools today, is the generation of personalized dashboards. It also makes it easy to create alerts and use outcome prediction tools.
With this in mind, data analysts can make predictions and improve decision making in the future.
Just like Zoho Analytics, Tableau Public is one of the tools for data analysis used as part of Business Intelligence processes. In this case, the application facilitates the analysis of historical data as the basis for companies to make important strategic decisions.
Tableau Public allows companies to process information and shape it, so that they’re then able to present it through different data visualization tools. The tool then allows for easy and intuitive visualizations..
As the last step, this software makes it possible for experts and companies to download the results and share them with clients and other partners.
Rapid Miner is a data analysis tool focused on working with Machine Learning. It is completely oriented to generate different analytics and to monitor the different transformations that the data undergoes in real time.
This software can be integrated with other useful tools in data analysis. It allows professionals to access data collected and stored in traditional databases, as well as other applications such as Access or Excel.
Regarding its integration with Machine Learning, this tool is intuitive, so that programming knowledge is not necessary to find and use the different functionalities related to machine learning.
Apache Hadoop is a work framework used for the treatment, processing and data analysis in the field of Big Data. It represents a work environment with its own ecosystem of applications and related technologies. In addition to this, it is one of the original tools in the world of data analysis.
Hadoop libraries present simple programming models that are mainly used for distributed storage of information models. This means that Hadoop works through a cluster of computers connected to each other.
Apache Hadoop spreads the workload and data storage in order to make data processing faster and more efficient.
MongoDB is a NoSQL-type database, which makes it especially interesting for storing unstructured data. However, MongoDB presents a broader scope of application than traditional NoSQL databases.
Thus, this represents a tool that is useful for different projects, especially for those that aim at achieving scalability. Thanks to its possibilities, a data system can be made for horizontal scalations that are easy and trouble-free.
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 the work of Data Scientists.
It can be used as a complement to Hadoop in data processing, as it is up to 100 times faster than Hadoop when it comes to memory processes and 10 times faster on disk platforms.
Python is one of the main programming languages used in data analysis, Data Science and Artificial Intelligence. This code has become more and more popular over the years, and today represents one of the most efficient data analysis tools.
Obviously, it is a complex programming language that involves a more complex learning curve than other Big Data tools, but it is worth it since it is one of the most complete.
One of Python’s big drawbacks right now is its processing power, which is less than what other data analysis tools can provide. For this reason, Python is usually used in integration tasks or other tasks that don’t involve heavy calculations.
Get trained as a data analysis expert!
We have seen some of the data analysis tools that you should know about in order to work in this sector. Some of them are easier to access than others and you can even learn to use them on your own. However, if you want to succeed in the world of data analysis, the best option is to take part in quality specialized training.
At Tokyo School we have a wide catalog of Data Analysis courses, that allow you to become a specialist in different fields within the sector. From Cloud Computing to Big Data as well as other specializations in specific tools such as Apache Hadoop, which we mentioned above in this article. Pick the one that best suits your needs and start your training!
Fill out the form below to find out more about us or about our training and become an expert in data analysis! We can’t wait to meet you!