Top 5 Data Analytics Tools of 2020

Identified as the sexiest job of 2020 round the globe, the inflow of Data Analysts has surged in recent years. Because the demand-supply ratio of quality analysts is highly distorted—not in an industry-favorable way—even the entry-level Data Analytics professionals get..Read More

  • Updated on 28th May, 20
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This Data Analytics tools blog will help you gain insight into the following topics:

 

What are Data Analytics tools used for?

These days business decisions made are not at all based on the intuitions of people in charge; rather, it is driven by data. Analyzing data gives insights into the exact problems faced and helps clear the roadblocks toward increased revenue and customer experience. A variety of Data Analytics tools and techniques help in achieving this. The most important ones are listed here in this blog.

For more insights on the topic of Data Analysis and modern Data Analytics tools, check out this video by Intellipaat:

 

Tools for Data Analysis

 

R Programming

R is one of the best Data Analytics tools used for data mining, modeling, and heavy statistical computing. This free, open-source software is not just employed in analyzing data, but also creates software and applications that can perform statistical analysis seamlessly. 

Additionally, R has a graphical interface, making it a viable choice while carrying out a wide range of analytical modeling, such as time series analysis, linear/non-linear modeling, and data clustering, among others. Most statisticians use this because of the ready-to-publish nature of its plots, graphs, equations, and formulae. 

Certification in Bigdata Analytics

The language can come off as slightly complex compared to other Data Analytics tools, but it still remains as one of the most favored.

 

Python

This high-level language supports both object-oriented programming and structured programming. Despite being a high-level language, it is the easiest to learn in the IT world. 

Python is a good facilitator in data analysis. Let us see how:

  • It provides libraries like NumPy and Panda, making it easier to process data with parallel processing.
  • Not always you get data in readily usable formats. Python uses libraries like BeautifulSoup and Scrapy to extract it from the Internet.
  • Visualizing datasets makes analysis much easier than trying to figure out patterns by just staring at them. Matplotlib and Seaborn are some libraries that help you represent data in the form of pie charts, bar graphs, etc.
  • Its capacity to compute incredibly complex mathematical problems makes it the go-to tool for Machine Learning enthusiasts. Scikit-Learn helps them do such complex operations.
  • Python also processes images using the OpenCV library.
 

SAS

Statistical Analysis System (SAS) finds its application increasingly in Machine Learning, Data Science, and Business Intelligence (BI) problems. It is among the most popular BI tools that comes with various features, including data visualization, data exploration, creating easy and insightful analysis, interactive dashboards, and ad-hoc reporting. 

SAS is one of the most frequently used statistical tools for data analysis. Some important features of SAS are as follows:

  • The iOS and Android-friendly BI companion app helps you easily monitor business anywhere, at any time. 
  • SAS BI can be easily accessed through Microsoft Office, where analysts can create and distribute reports and charts as per convenience.
  • SAS can predict behaviors and manage and optimize their communications.
 

Apache Spark

This University of California developed tool is a large-scale data processing engine, which executes applications in Hadoop. It is known for its speed in processing, where the applications are executed 100x faster in memory and 10x faster on disk than Hadoop. 

Because Apache Spark can work with Big Data efficiently, a lot of big companies with large amounts of unstructured data are adopting it into their stack.

  • It can support basic data loading to handling SQL queries and Machine Learning. 
  • It has a unified nature that allows it to combine different libraries and processing types. 
  • It loads data from storage systems and performs computation operations on them. 
  • Spark is associated with a wide range of functionality. It includes libraries for structured data and SQL, stream processing, Machine Learning, and graph analytics. 

Now, let us read about Excel BI tools which is also one of the top Data Analytics tools used in industries today.

 

Microsoft Excel

Analyzing data has become the most powerful skill these days. MS Excel might be the most primitive tools used among Data Analysts, but it makes the go-to option of any beginner in the field.

  • Even if you are an expert in all of the above tools, you might still need to use Excel. It is the most basic and popular tool used in the industry.
  • It makes use of pivot tables to filter complex data and draws insights from them as per client requirements. 
  • It has advanced analytics options that help in modeling capabilities.

Become a Big Data Architect

The world of Data Science has become much simplified because of the Data Science tools used in this field. The above-mentioned Data Analyst tools are just an iceberg in the sea of options we have. Visit our website to enter the world of Data Science effortlessly!

Course Schedule

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