Top 15 Best Data Analysis Software & Tools in 2023

A data analyst reviews data to identify key insights into a business’s customers and ways the data can be used to solve problems. They also communicate this information to company leadership and other stakeholders.

As the field of data analytics evolves, the range of available data analysis software grows. In our previous post, we looked at the job descriptions of a data analyst, the required skills, job prospects, salary, and how to become a data analyst.

In this article, we highlight the top 15 best data analysis software and tools every data analyst must master to be efficient in the job.

Top 15 Best Data Analysis Software & Tools in 2023

Data analysis is a core practice of modern businesses. Choosing the right data analytics tool is challenging, as no tool fits every need. To help you determine which data analysis tool best fits your organization, let’s examine the important factors for choosing between them and then look at some of the most popular options on the market today.

There are a few things to take care of before evaluating the available tools. You should first understand the types of data your enterprise wants to analyze, and, by extension, your data integration requirements.

In addition, before you can begin analyzing data, you’ll need to select data sources and the tables and columns within them, and replicate them to a data warehouse to create a single source of truth for analytics.

You’ll want to assess data security and data governance as well. If data is shared between departments, for example, there should be access control and permission systems to protect sensitive information.

How to choose a data analysis tool

Once you have data ready, you can try analyzing it using different tools. How do you find one that’s a good fit for your company? Start by considering your organization’s business needs and learning who will be using your analytics tool.

Will it be used by sophisticated data analysts and data scientists, by nontechnical users who need an intuitive interface, or should it suit both kinds of users?

Some platforms provide an interactive experience for iterating on code development — typically using SQL — while others focus more on point-and-click analysis for less technical users. The tool should also provide support for visualizations relevant to your enterprise.

Consider a tool’s data modeling capabilities. Some support a semantic layer or can perform data modeling themselves. If you want to use one that doesn’t, you’ll have to use SQL or a tool like dbt to model your data prior to analysis.

Finally, consider price and licensing. Some offerings are free, while others charge licensing or subscription fees. The most expensive tools are not necessarily the most feature-complete, and users should not ignore the many robust free solutions available.

How fast can I become a data analyst?

In terms of how long it takes to become an analyst, that very much depends on the individual. Those with a little existing knowledge and experience can master the skills within a few months. For others, it will take several years of study.

Top 15 Best Data Analysis Software and Tools in 2023

1. R Programming

R is the leading data analytics tool and is widely used for statistics and data modeling. It has 11,556 packages and allows you to browse the packages by category. Compiles and runs on a wide variety of platforms viz -UNIX, Windows, and macOS.


KNIME is an open-source, reporting, and integrated analytics tool that allows you to analyze and model data through visual programming. It integrates various components for data mining and machine learning via its modular data pipelining concept. KNIME was developed in 2004 by a team of software engineers at the University of Konstanz.

3. Apache Spark

Apache Spark is a fast large-scale data processing engine that executes applications in Hadoop clusters. Spark is built on data science and its concept makes data science effortless. Spark also includes a library – MLlib, that provides a progressive set of machine algorithms for repetitive data science techniques.

4. SAS

SAS was developed by the SAS Institute in the 1960s and has evolved ever since. Its main use today is for profiling customers, reporting, data mining, and predictive modeling. It offers modules for specific uses such as anti-money laundering and analytics for the Internet of Things.

5. Python

Python is an object-oriented scripting language that was developed by Guido van Rossum in the late 1980s. It is easy to learn as it is very similar to JavaScript, Ruby, and PHP. Also, Python has excellent machine-learning libraries including Scikitlearn, Theano, Tensorflow, and Keras.

6. Microsoft Excel

Excel is the world’s best-known spreadsheet software. Built-in features include pivot tables and form-creation tools. It also has a variety of other functions that streamline data manipulation. With many plug-ins available, you can easily bypass Excel’s shortcomings.

7. Jupyter Notebook

Jupyter Notebook allows you to create interactive documents. These combine live code, equations, visualizations, and narrative text. It runs in the browser and supports over 40 languages, including Python and R. It also integrates with big data analysis tools, like Apache Spark.

8. Microsoft Power BI

Microsoft’s Power BI is a relative newcomer to the market of data analytics tools. It allows users to create interactive visual reports and dashboards, with a minimal learning curve. The main limitation is the low data limit (around 2GB), although it does come with a free version.

9. Tableau

Tableau is one of the best commercial data analysis tools available. The suite handles large amounts of data better than many other BI tools, and it is straightforward to use. It has a visual drag-and-drop interface (another definite advantage over many other data analysts’ tools).

10. LookML

Looker offers a BI and data analytics platform built on LookML, the company’s proprietary modeling language. Embedded analytics in Powered by Looker utilize modern databases and an agile modeling layer. Organizations can use Looker’s full RESTful API or the schedule feature to deliver reports by email or webhook.

11. Oracle Analytics Cloud

Oracle offers a wide range of BI and analytics tools that can be deployed on-prem or in the Oracle Cloud. The company provides traditional BI capabilities inside its Business Intelligence 12c solution. Oracle Data Visualization allows users to automatically visualize data as drag-and-drop attributes, charts, and graphs.

12. Qlik

The company’s flagship offering, Qlik Sense, enables organizations to combine all their data sources into a single view. The Qlik Analytics Platform allows users to develop, extend and embed visual analytics in existing applications. Users can build and embed Qlik as simple mashups or integrate within existing platforms.

13. SAP

SAP is one of the best data analysis software and tools around. It offers a broad range of BI and analytics tools in both enterprise and business-user-driven editions. The company’s flagship BI portfolio is delivered via on-prem and cloud deployments atop the SAP HANA Cloud. SAP also offers a suite of traditional BI capabilities for dashboards and reporting.

14. Amazon QuickSight

Amazon QuickSight is a serverless and embeddable business intelligence service for the cloud with built-in machine learning. The product lets you create and publish interactive BI dashboards that can be queried using natural language. It can automatically scale to thousands of users without any infrastructure.

15. Board

BOARD combines business intelligence, performance management, and predictive analytics into one platform. Any change to data, data models, security profiles, or business rules is immediately propagated to every application. The company also offers BOARD Cloud, a SaaS version of the platform, backed by Microsoft Azure.

Can I become data analyst in 3 months?

You can make use of the playlists to lean skills needed for a data analyst in 3 months. Remember this important point doing practical work is important than theory. Spend 20% time on theory and 80% time on implementing it.

Is data analyst is easy to learn?

Because the skills needed to perform Data Analyst jobs can be highly technically demanding, data analysis can sometimes be more challenging to learn than other fields in technology.

Do data analysts do a lot of coding?

Do Data Analysts Code? Some Data Analysts do have to code as part of their day-to-day work, but coding skills are not typically required for jobs in data analysis.

How long is a data analyst course?

around 3-4 years

How long does it take to become a data analyst? Data analyst positions require a bachelor’s degree, which typically takes around 3-4 years to complete. A master’s degree or MBA can be completed in under two years, and a post-master’s certificate can be completed in under a year.

Can data analyst work from home?

As the data market grows and remote work continues to rise, data analysts will increasingly find opportunities for flexible, location-independent work. While it may prove more difficult for entry-level analysts to find a remote position, it’s certainly possible.

Chinedu Okeke

Chinedu is the founder of Nigerian Tech. He is a tech enthusiast who has the passion for emerging trends in the tech industry. He is also a professional web content developer.

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