Data Analytics Tools: In today’s modern era, where every person is producing several gigabytes of data on the daily basis through Social Media and various other Platforms.
The companies are interested in collecting, storing, and using this data for their business growth.
Now, here comes the role of a Data Analyst, who Analyzes this huge amount of data for the faster growth of the company.
Data Analytics is a practice of refining, transforming, and using the data for the increase in sales and business profit.
So, To perform the analysis, one should have a good understanding of Data Analytics Tools.
In the market, there are several different tools available for analyzing the data. However, we are going to take a look at some of the best tools out of them.
Here are the Top 8 Data Analytics Tools you should go with:
Table of Contents
Official Website: Tableau
Tableau is one of the best and most popular tools available for Data Analytics.
With this tool, Data Analytics becomes so easy that even a person with a non-technical background can use it for analyzing the data.
It takes a huge amount of data in real-time and performs the Data Visualization techniques in it.
So, Tableau converts the huge amount of raw data into a meaningful graph, map, or in any other format.
It is majorly used in Business Intelligence Industries.
Top Companies using Tableau: Adobe, Walmart, Amazon, TerraCycle, LinkedIn, and many more.
Official Website: SAS
SAS stands for Statistical Data Analysis. It is written in C Language back in 1960.
It is a software suite used for performing Data Management, Predictive Analysis, Statistical Analysis, Visualization, and much more.
Unlike other Data Analytics Tools, In SAS, you can perform the analysis using Graphical Interface as well as with the help of SAS Programming Language.
SAS is used for combining the different Data Sources and manipulating and analyzing that data for creating Graphs, Stats, Maps, and different other readable formats.
Top Companies Using SAS: Google, Dell Advanced Analytics, SBI, Netflix, HDFC, and many more.
Official Website: RapidMiner
RapidMiner is an open-source Data Analytics Software used by Data Scientists and Data Analysts.
This tool allows you to perform advanced Predictive Analysis and Descriptive Statistics, implementing a Machine Learning and Deep Learning Model with the help of a simple GUI.
However, it provides the facility of performing the analysis using Python and R Programming Language as well.
Top Companies using RapidMiner: PayPal, Tech Data Corporation, Corning Inc, Daimler, Liat, and many more.
Official Website: QlikView
QlikView is a fast and easy to use Data Analytics Tool which works on data association to build a relationship between different Data available and visualize it with the help of colors.
One can easily create a graph, map, dashboard, or report of its data using QlikView on the mobile phone as well.
It uses different Machine Learning and Deep Learning Algorithms for Data Mining, Data Analysis, and Business Intelligence tasks.
Top Companies using QlikView: TCS, Cognizant, Accenture, Citibank, Mercedes-Benz, and many more.
Official Excel: Excel
If you are a beginner and just starting into Data Analytics, then this is one of the best tools you can go with.
Excel is the most basic and traditional tool available for analyzing the data.
It is a part of Microsoft Office Software Suite and is used by Small Businesses and Individuals only.
So, if you have fewer data to work on, then Excel should be your choice.
Besides Graphical Interface, you can also use the Programming Language called “Visual Basic” with Excel to create the Form, Spreadsheet, PivotTable, etc.
Top Companies using Excel: Securely, WanderlustAI, OutSystems, Direct Market, Team Magento 2, and many more.
Official Website: R Programming Language
R is an open-source environment and programming language for Data Analysis and Statistical Computing.
It is the favorite programming language of Data Scientist and Machine Learning Engineers as well.
R is also considered much better and faster as compared to other Data Analytics Tools as it gives more control and flexibility to the user.
It is heavily used for Data Visualization and Statistical Analysis.
To use R for Data Analysis, one should know Programming. So, without programming experience, you can’t use this tool.
The community support of R is great. So, you will not find any lack of resources in case you get stuck in any problem or error.
Top Companies using R: IBM, Uber, Facebook, Twitter, ANZ Bank, and many more.
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7. Microsoft Power BI
Official Website: Microsoft Power BI
Microsoft Power BI is a software suite for Data Analytics and Business Intelligence.
It has an amazing User Interface and fast enough to process large company size data very easily.
It supports different platforms, like – Desktop, Mobile, and Cloud.
This is a software which is used by lots of large size companies as well as by non-technical individuals.
Microsoft Power BI supports many top Programming Languages, like – Python, R, SQL, DAX, and M.
So, if you are also looking forward to the good Data Visualization and Analytics Tool, then you surely go with it.
It will give you the report and real-time dashboard in the most presentable manner than any other tool could.
Top Companies using Microsoft Power BI: Apple Inc, Walmart, Aircare, Bing Ads, Coca-Cola, and many more.
Official Website: Python
If you are a programmer, then this can be your favorite tool for Data Analytics.
Python is an open-source Object-Oriented Programming Language, which is heavily used for Scripting, Web Development, Data Science, Machine Learning, and Deep Learning.
It has lots of libraries available for Data Visualization, like – Matplotlib, Plotly, Seaborn, Altair, and many more.
Python is the Easiest Programming Language to learn and fast enough to process a huge amount of Data, which makes this the best choice for programmers who want to pursue their career in Data Analytics.
Top Companies using Python: Dropbox, Instagram, Reddit, Quora, Twilio, and many more.
Hope you like the article on Best Data Analytics Tools. We also have an article on Artificial Intelligence 101, which you must take a look at.