Data analysis is the process of investigating a data set and drawing conclusions about it. To do this, data analysts rely on advanced software and tools. Learning about the common tools for data analysis may help you choose the best one for a project, improving your productivity at work. In this article, Pritish Kumar Halder explores what data analysis tools are, lists the 11 most common tools and provides tips for choosing the right one for your requirements.

What are data analysis tools?

Data analysis tools probe data sets, perform calculations and return insights. Data analysts rely on these tools to help them analyse complex data sets. These tools can perform thousands of calculations in a short period, making them essential for investigating large data sets. There are different types of analysis tools available, including programming environments, spreadsheet software and automation. The tools in use typically depend on the requirements of each project.

11 Data Analysis Tools

Here are 11 data analysis options you may consider using:

1. MySQL Workbench

MySQL Workbench is a tool that makes it easy to manage MySQL databases. MySQL is a database management service that is free to use. If you are working with an SQL database, MySQL Workbench can help you visualise data, create models and improve your analysis performance.

2. Python

Python is a programming language that you can use as a tool for data analysis. Python offers built-in tools that make it easy for users to detect patterns, gather insights and evaluate performance. In addition, Python may be easier to learn, making it an efficient choice for those interested in getting started with data analysis. Large corporations, small companies and freelance data analysts often use the tools that Python offers.

3. RStudio

RStudio is an open-source tool for performing data analysis with the R programming language. Programmers often use R for statistical computing and graphics, making RStudio an effective tool for data analysts. You can run RStudio as either a desktop application or on a remote server, giving you the option to access the software remotely through a web browser. Since RStudio is open source, you can use it for free, which can be helpful for data analysts who are beginning their careers.

4. Tableau

Tableau is a tool that makes it easy for users to create visualisations based on their data. This tool helps simplify complex datasets and turns them into formats that are easy to understand. In addition, the visualisations you can create through Tableau are aesthetically pleasing, making it an ideal platform if you want to share insights with others. Data analysts can benefit from using Tableau if they intend to promote their work online or plan to present data to an audience that does not have a background in data analysis.

5. Microsoft Excel

Since it is widely available, many organisations use Excel as a tool for data analysis. It is also easy for individual users to download and use. Excel makes organising data easy with its spreadsheet layout. It also provides numerous built-in tools for data analysis that do not require the user to learn any additional programming languages. While users who plan to perform advanced data analysis may prefer to use more complex tools, Excel is typically effective for simple to intermediate analysis.

6. Datapine

Datapine is a business intelligence software that aims to help beginners and more advanced users. It does this by providing a drag-and-drop interface through which users can build database queries. It also has the option to switch to manual mode so that advanced users can perform deeper analysis. This makes it an effective tool if you are new to data analysis but want the ability to implement more advanced methods as you learn.

7. Jenkins

Jenkins is an open-source automation tool that helps developers automate parts of their software development. This enables developers to perform data analysis and complete projects faster. Jenkins also helps data analysts by automatically running software at certain times. For instance, a user analysing stock market information may use this tool to run their software automatically each day after the market opens. This can help data analysts become more productive and improve their datasets.

8. Apache Spark

Apache Spark is an open-source analytics engine. A key feature of Spark is its ability to process data at high speeds. This makes it ideal if you have a workload with a lot of data. It is also helpful for data analysts to learn how to use Spark because it has a very active community. New users can ask questions and learn from other data analysts, helping them grow professionally and learn new methods to use in their projects.

9. Splunk Platform

Splunk Platform is a tool you can use to analyse and visualise machine-generated data. The goal of Splunk is to help organisations extract data from their servers and perform analysis on it. Some very large and well-known organisations use Splunk to help them improve their day-to-day operations. Since employers may be seeking candidates who know how to use this software, it is beneficial for data analysts to become familiar with it.

10. Talend Data Fabric

Talend is a tool that provides many features for data analysis. With this tool, you can easily integrate large datasets, connect to cloud services, prepare your data and extract insights from your data sets. It is a comprehensive tool that is important to learn, as you are likely to encounter it at some point during your career.

11. KNIME Platform

KNIME Platform is an open-source tool that uses a GUI-based workflow. The benefit of a GUI system is that you can visualise your analysis steps, which can help you understand it in more depth. KNIME Platform can also execute some of your analysis steps and inspect the results, which allows you to see how different steps affect the outcome. KNIME Platform is a useful tool if you are a visual learner and have minimal prior programming knowledge.

Tips for choosing a tool for data analysis

Here are some tips for choosing an analysis tool that meets your professional requirements:

Think about which advanced features you require

Each tool comes with its own features, so it is helpful to consider what you plan to do with your data. For example, if you plan to create data visualisations, you may want to use a tool like Tableau. If you want the ability to automate some processes, you might consider a tool like Jenkins. To determine which tool to choose, you can write a list of features you want your software tool to have, then explore the tools that fit your requirements.

Determine how much time you can spend on learning

Some tools may take more time to learn than others. For instance, if you know the R programming language, you may understand a tool like RStudio more quickly. Likewise, if you have some experience in computer programming, you may learn the R programming language quickly. If you are new to coding, you may find a tool like KNIME Platform or Excel easier to use without much additional training.

Consider The Type Of Data You Want To Analyse

Think about the type of data you are working with, as some tools are better with certain types of data sets than others. For instance, if you are working with a very large data set, consider using a tool designed to process large batches of information quickly. You may also think about the format of your data. For example, if you have a spreadsheet, you may find it easier to use a tool like Microsoft Excel, whereas if you have information in a MySQL database, it is likely easier to use MySQL Workbench.