Top 10 Data Analytics Tools You Need To Know In 2023

Data analytics tools are essential for organizations and professionals seeking to derive insights and make data-driven decisions. As of my last knowledge update in September 2021, I can provide a list of 10 data analytics tools that were popular and widely used at that time. Please keep in mind that the landscape of data analytics tools may have evolved since then, so it's a good idea to research the latest developments and trends in 2023. Here are 10 data analytics tools that were relevant in 2021:

Tableau: Tableau is a powerful data visualization tool that allows users to create interactive and shareable dashboards. It's known for its user-friendly interface and wide range of data connectors.

Microsoft Power BI: Power BI is a business intelligence tool by Microsoft. It offers data visualization, interactive reports, and the ability to share insights with others. It's particularly well-integrated with Microsoft's ecosystem.

Google Data Studio: Google Data Studio is a free tool for creating interactive reports and dashboards. It integrates seamlessly with other Google products and services, making it popular for businesses using Google Analytics and Google Ads.

Python (with libraries like Pandas and Matplotlib): Python is a versatile programming language commonly used in data analysis. Libraries like Pandas and Matplotlib are essential for data manipulation and visualization.

R: R is a programming language and environment specifically designed for statistical computing and data analysis. It's widely used in academia and by data scientists.

SAS: SAS is a software suite for advanced analytics, business intelligence, and data management. It's known for its robust statistical analysis capabilities.

IBM Watson Analytics: IBM Watson Analytics is an AI-powered analytics tool that offers data exploration, predictive analytics, and natural language querying.

QlikView/Qlik Sense: QlikView and Qlik Sense are data visualization and business intelligence platforms that allow users to explore data and create interactive dashboards.

Apache Spark: Apache Spark is an open-source big data processing framework. It's used for distributed data processing and analytics, making it suitable for handling large datasets.

Alteryx: Alteryx is a data preparation and analytics platform that streamlines data blending, cleaning, and analysis. It's popular for data wrangling tasks.