Top 10 Data Analytics Tools in 2020

The increasing requires and importances of data analytics in the market have generated lots of openings globally. It becomes faintly hard to choose the top data analytics tools as the open-source tools are further trendy, user-friendly and performance-oriented than the paid version. There are lots of open-source tools that don't need much/any coding and manages to deliver enhanced consequences than paid versions e.g. - R programming within data mining and Tableau public, Python in data visualization. Below is the listing of the top 10 data analytics tools, equally open-source and paid versions, based on their reputation, learning and performance.

Top 10 Data Analytics Tools in 2020
Top 10 Data Analytics Tools in 2020

Top 10 Data Analytics Tools in 2020

  • R Programming

R is the most important analytics tool in the business and extensively used for statistics and data modeling. It can effortlessly control your data and present it in dissimilar ways. It has exceeded SAS in lots of ways like the capability of data, performance, and results. R compiles and runs on a wide diversity of platforms viz -UNIX, Windows, and macOS. It has 11,556 packages and allows you to glance through the packages by category. R also provides tools to robotically install all packages as per user requirement, which can also be glowingly assembled with big data.

  • Tableau Public:

Tableau Public is a without charge software that connects any database be it commercial Data Warehouse, Microsoft Excel or web-based data, and creates data visualizations, maps, dashboards, etc. with real-time updates presenting on the web. They can also be communal through social media or with the client. It allows the right to use to download the file in diverse formats. If you want to see the power of tableau, then we must have an exceptionally good data source. Tableau's Big Data capabilities make them significant and one can analyze and visualize data better than any other data visualization software in the marketplace.

  • Python

Python is an object-oriented scripting language that is trouble-free to examine, write, maintain and is a free open source instrument. It was developed by Guido van Rossum in the late 1980s which supports both functional and structured programming methods.

Python is simple to learn as it is very comparable to JavaScript, Ruby, and PHP. Also, Python has extremely good machine learning libraries viz. Scikitlearn, Theano, Tensorflow, and Keras. Another vital feature of Python is that it can be assembled on any stage like SQL server, a MongoDB database or JSON. Python can also handle text data very fine.

  • SAS

Sas is a programming environment and language for data handling and a leader in analytics, developed by the SAS Institute in 1966 and advance developed in the 1980s and 1990s. SAS is with no trouble accessible, manageable and can analyze data from every source. SAS introduces a vast set of products in 2011 for customer cleverness and lots of SAS modules for web, social media, and marketing analytics that is extensively used for profiling customers and forecast. It can in addition forecast their behaviors, manage, and optimize communications.

  • Apache Spark

The University of California, Berkeley's AMP Lab, developed Apache in 2009. Apache Spark is a rapid large-scale data processing engine and executes applications in Hadoop clusters 100 times faster in memory and 10 times faster on disk. Spark is built on data science and its idea makes data science easy. Spark is also trendy for data pipelines and machine learning models development.

Spark also includes a library - MLlib that provides a progressive set of machine algorithms for repetitive data science techniques similar to Classification, Regression, Collaborative Filtering, Clustering, etc.

  • Excel

Excel is a basic, popular and widely used analytical tool approximately in all industries. Whether you are a specialist in Sas, R or Tableau, you will still require using Excel. Excel becomes significant when there is a requirement of analytics on the client's internal data. It analyzes the multifaceted mission that summarizes the data with a preview of pivot tables that helps in filtering the data as per client requirement. Excel has the development of business analytics anymore which helps in modeling capabilities that have prebuilt options comparable to automatic relationship detection, creation of DAX measures and time grouping.

  • RapidMiner

RapidMiner is an authoritative integrated data science stage developed by the analogous company that performs prognostic analysis and further advanced analytics similar to data mining, text analytics, machine learning and visual analytics without any programming. RapidMiner can include any data basis types, including Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase, IBM DB2, Ingres, MySQL, IBM SPSS, and Dbase, etc. The tool is extremely authoritative that can generate analytics based on real-life data transformation settings, i.e. you can manage the formats and data sets for predictive analysis.


KNIME Developed in January 2004 by a team of software engineers at the University of Konstanz. KNIME is principally important open source, reporting, and integrated analytics tools that permit you to inspect and model the data through visual programming; it integrates a variety of components for data mining and machine knowledge via its modular data-pipelining perception.

  • QlikView

QlikView has lots of elite features like patented technology and has in-memory data processing, which executes the result tremendously rapidly to the end-users and stores the data in the report itself. Data association in QlikView is automatically maintained and can be dense to almost 10% from its original size. Data relationship is visualized using colors - an exact color is given to related data and another color for non-related data.

  • Splunk

Splunk is a tool that analyzes and searches the machine-generated data. Splunk pulls all text-based log data and provides a straightforward way to explore through it, a user can pull in all type of data, and perform all sort of interesting statistical analysis on it, and present it in dissimilar formats.


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