Four data analytics tools to get you started

Decisions made at an individual level can no longer affect the proceedings of commerce. The business operations of our times depend on data for effective decision-making. Data analytics as a discipline adds the predictability factor in strategies of the trade. And with adept effort, data analytics can propel businesses to new heights. And the process is easier than ever due to the emergence of data analytics tools and services related to the same. This article will shed light on four of such tools, considered ideal for a beginner for learning and implementation.

The growing demand and importance of data analytics in the market have generated many openings worldwide. It becomes slightly tough to shortlist the top data analytics tools as the open source tools are more popular, user-friendly and performance oriented than the paid version.

If you’re looking to analyze business data, you may have lots of different, specific needs. As Sebastian pointed out, you want managers to be able to see and use the data, maybe even build their own reports. If that’s the case, you’re going to want to look at BI (Business Intelligence) and BA (Business Analytics.) Again, what you pick is going to depend on your needs and the solutions you already have in place.


For the manifold benedictions of R in the development of data analytics paradigms, it is considered an essential tool by data analysts. R comes with a graphical interface, which makes it easier to visualize statistics and analytics in a clear manner. Additionally, R is open source and free to use, utilized by developers and analysts all over the world. Hence, community support is extensive.


Python is perhaps the most popular and relatively easy to learn when it comes to other programming languages. Due to a plethora of opportunities python is used by a large number of professionals. The community support in the case of python is phenomenal and the syntax of python is considered the easiest of the lot. Due to multiple factors, python is widely accepted by academia and industry as the weapon of choice when it comes to data analytics.


Statistical analysis system or SAS is a dedicated analytics tool. It finds the native state of applicability in the case of business intelligence. SAS is most commonly used for data analysis and data visualization purposes in commercial and academic fields. Additionally, the predictions obtained by the use of SAS are usually more thoughtful due to the specialized nature. SAS is easy to learn and the applicability in the case of statistical analysis is wide due to the easy-going nature.

Apache spark

Apache Spark is a data processing engine developed by the University of California. Spark has the ability to handle and utilize a large quantity of unstructured data. The big data abilities of spark attract businesses of all stature with requirements of big data handling. Additionally, the spark comes with significant machine learning capabilities, necessary for the conversion of unstructured data into something structured and workable. 

Author’s words

Data analytics has become mainstream over the years. And after the lockdowns, data analytics became the lifeline of many ventures and enterprises due to the versatility and predictability factors. Struggling IT professionals and analysts working in many relevant fields are thus opting for data analytics courses in order to secure their employment. Perhaps the time is ripe for stepping up and making contributions in the field.