Data Science

10 Data Analytics Tools

The process of cleaning, converting, and modelling data to uncover information that can be useful for business choices is known as data analysis. To extract useful information from data and make decisions based on that knowledge is the aim of data analysis.

The process of cleaning, evaluating, and presenting data in order to gain significant insights and improve business decisions is known as data analysis. Depending on whether you’re looking at quantitative or qualitative data, there are differences in the methods you use to analyse the data.

To help you extract pertinent information from corporate data and streamline the data analysis process, you will need data analysis tools in any instance. The term “data analytics,” which describes the science or discipline that covers the entire data management process, from data collection and storage through data analysis and visualisation, is frequently used in corporate contexts.

Although it is a step in the data management process, data analysis concentrates on the transformation of unprocessed data into informative statistics and explanations.

Data Analytics Tools


After being developed for software and web development, Python underwent enhancements to make it more suitable for data research. One of the computer languages with the highest growth right now is Python.

With an excellent collection of user-friendly libraries covering every facet of scientific computing, it is an advanced data analysis tool. Python is an easy-to-learn programming language that is free and open-source.

One of Python’s initial data science libraries, NumPy, served as the foundation for the development of Pandas, the language’s data analysis module. With pandas, anything is possible! You may perform complex data manipulations and numerical analysis using data frames.

Pandas can handle a wide range of file formats. For time-series analysis, it is possible to input data from Excel spreadsheets into processing sets. (Time-series analysis is a statistical method by definition that examines data collected at regular periods of time.)

Pandas is an incredibly useful tool for a variety of tasks, including data cleaning, indexing and grouping, data masking, data merging, and data visualisation.

Microsoft Excel

The most widely used spreadsheet application worldwide is Excel. Moreover, it offers graphing and computing features that are conducive to data analysis. Regardless of specialisation or other software requirements, Excel is a need in any field.

The built-in features of Excel are essential; these include form development tools and pivot tables, which are useful for organising or summarising data. In addition, it has several other features that facilitate faster data manipulation.

Text, numbers, and dates, for instance, can all be combined into one cell using the CONCATENATE function. SUMIF lets you create value totals based on variable criteria, and Excel’s search function makes it simple to find specific data.

But it is not without limitations. For instance, it tends to approximatively calculate large numbers, which leads to errors, and operates very slowly with large datasets. Despite this, Excel is a useful and strong tool, and its shortcomings are readily surmounted thanks to the abundance of available plug-ins. Start by learning these five Excel formulas that are essential for any data analyst.


SAS is a statistical software programme that is widely utilised in data administration, predictive analysis, and business intelligence (BI). SAS is a proprietary programme that costs money for companies to use. A complimentary university edition of SAS has been made available for students to learn and use.

SAS is easy to use because it has a straightforward graphical user interface (GUI), yet using the tool requires a solid grasp of SAS programming. SAS’s DATA phase, which handles data production, import, modification, merging, and computation, helps with ineffective data handling and manipulation.

Jupyter Notebook

An open-source web application called Jupyter Notebook is used to create interactive documents. These combine narrative writing, images, mathematics, and live programming. Imagine something a lot more dynamic and specifically designed for data analytics—something like a Microsoft Word page!

It’s perfect for displaying work as a tool for data analytics: Python and R programming are supported in Jupyter Notebook, a browser-based programming environment that supports more than 40 languages.

It also offers a range of outputs, including HTML, images, videos, and more, and integrates with big data platforms like Apache Spark. Like any other instrument, it is not without limitations, though.

Version control in Jupyter Notebook documents is weak, and it’s challenging to monitor changes. This indicates that it’s not the best option for work involving development or analytics (you should use a separate IDE for these tasks), and it hinders teamwork.

You have to provide any extra resources (like libraries or runtime systems) to all the people you share the document with because it isn’t self-contained. It is still a helpful data science and analytics tool, nevertheless, for teaching and presenting information.

Power BI

There are three editions of Power BI available: Desktop, Pro, and Premium. The desktop version is available to users without charge, however Pro and Premium editions require payment. You may examine your data, establish connections with many data sources, and share the outcomes within your company. Power BI allows you to create live dashboards and reports that really make your data come to life.

Because Power BI connects with other programmes, such as Microsoft Excel, you may quickly and easily start using your current solutions. Microsoft is a Magic Quadrant Leader in analytics and business intelligence systems, according to Gartner. Power BI is used by Nestle, Tenneco, Ecolab, and other top companies.


One of the best commercial data analytics tools for building interactive dashboards and visualisations without extensive coding experience is Tableau. The suite is incredibly user-friendly and handles large amounts of data better than many other business intelligence tools.

Its graphical drag-and-drop interface is yet another feature that sets it apart from a lot of other data analysis tools. However, Tableau’s functionality is restricted because to the absence of a scripting layer. It performs poorly, for example, when pre-processing data or creating calculations that are more intricate.

It has data manipulation features, although they’re not very great. Usually, you’ll need to do scripting operations in R or Python before importing your data into Tableau.

Its popularity stems from its decent visualisation, which helps to offset its shortcomings. Moreover, it works well on mobile. As a data analyst, mobility might not be essential, but it can be helpful if you want to experiment while you’re on the road!


The last product on our list is an open-source, cloud-based data integration platform called KNIME (Konstanz Information Miner). Software developers at Konstanz University in Germany produced it in 2004.

KNIME was initially created for the pharmaceutical industry, but other industries have come to use it because of its capacity to combine data from multiple sources into a unified system. Examples of this include business intelligence, machine learning, and customer analysis.

Its usability is its main selling feature, besides the fact that it is free. Its graphical user interface (GUI) with drag-and-drop functionality makes it appropriate for visual programming. This suggests that building data pipelines doesn’t require consumers to have a significant degree of technical proficiency.

Although it claims to address every type of data analytics task, data mining is where it really shines. It offers extensive statistical analysis, however people with some Python and R familiarity will find it more useful.

Since KNIME is open-source, it may be easily customised to meet the needs of any kind of organisation without requiring hefty costs. It is therefore well-liked by smaller businesses with tighter budgets.


Like Python, R is a well-known open-source programming language. It is frequently employed in the creation of statistical and data analysis software. R has a steeper learning curve and a more complex syntax than Python.

However, it is frequently used for data visualisation and was primarily created for heavy statistical processing demands. Similar to Python, R boasts more than 10,000 packages available on CRAN (the Comprehensive R Archive Network), a network of publicly accessible code.

It can access code written in languages like C, C++, and FORTRAN and functions well with a wide range of systems and languages, especially massive data applications.

However, R lacks a dedicated support team and has poor memory management despite having a vast user community to turn to for help. Still, there’s always a plus: RStudio is a superb R-specific integrated development environment (IDE).

SQL Consoles

Our list of data analyst tools would be lacking if SQL consoles weren’t present. As a database tool for analysts, SQL is essentially a computer language used to manage and query data contained in relational databases. It is particularly helpful at managing structured data.

It is one of the analyst tools used in a range of business cases and data scenarios, and it is widely used in the data science community.

The explanation is straightforward: SQL is essential to business success since it allows analysts to acquire a competitive edge because most data is stored in relational databases, which means you must access and unlock its value. Database management systems that are based on SQL and are relational include MySQL, PostgreSQL, MS SQL, and Oracle.

ETL Tools

Businesses of all sizes use ETL processes worldwide, and as a firm grows, it’s likely that you’ll need to load, extract, and convert data into a different database so that you can analyse it and create queries.

ETL solutions can be broadly classified into three categories: cloud-based ETL, real-time ETL, and batch ETL. Each category has specific requirements and features that meet different needs of the organisation.

These are the tools used by analysts, Talend being one of the best examples, who take part in more technical data management operations inside a company.

Data analysis is easy to learn and do with some practise. Not every instrument will be equally beneficial. It is advantageous to concentrate on and master one particular tool.

Knowing the data is essential to assessing our current position in data analysis. In the analysis and visualisation of data, programming is not very important. But some tools help you get into programming.




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