Important tools for Data Analytics

Artificial intelligence is making waves all across the globe. It is changing the way organizations work and is enhancing the overall productivity of the firms. The biggest application of machine learning in the organizations is data analytics. It refers to the process of digging out useful data and using it to get insights about the businesses, make predictions and to prepare strategies. Efficacious analysis of data can garner immense benefits for the firm. Data is thus treated as an asset for every organization and is, in fact, the fuel that makes organizations run properly. However, the organizational data is of no use without data analytics. That is why companies seek data analysts and experts who can analyze the data and provide useful information to the companies.

The big data analytics market is huge and is expected to escalate even further thereby providing opportunities to a plethora of dynamic candidates. In this scenario, the intensive know-how of data analysis with working knowledge of data analytics tools can give you a competitive edge over others.  MIT Skills offers a job-oriented course in data analytics that will broaden your knowledge, sharpen your analytics skills and accustom you to vital data analytics tools. Here are some vital data analysis tools that are used in the present scenario:

1.     Excel

It is the most elementary and widely used tool for data analysis. It is a great tool for the beginners and helps in performing the basic grunt work. The fact that it has a massive user community makes it popular. Excel can be used to summarize data, basic data manipulation and for data visualization. It is also used by the analytics team to explain the observations to the business team.

2.     SAS

SAS is the most widely used data analytics tool by virtue of being versatile, robust and easy to learn. Two major apps are enterprise miner and visual analytics. Several new features and modules have been added to this tool. The new additions include anti-money laundering, SAS analytics for SME’s, and SAS for IoT.

3.     Python

Python is one of the most popular languages in the present era as it is fast and easy to learn. Features of analytics have been added to the language to make it a powerful data analysis tool. The tool provides statistical and mathematical analysis. Apart from analytics, it can also include machine learning.

4.     R

The R language is being used for the past 20 years. The language was designed to perform analytics. It contains several packages including big data support, Microsoft support, data mapping, data visualization and statistical analysis. The only constraint of the tool is that it is single threaded.

5.     Tableau

Tableau is a tool that facilitates data visualization. It has a drag and drop interface that helps in data mining and performing different kind of data analysis. Its machine learning algorithms decipher the type of data and the best method to analyze it.

6.     Spark

Spark is a big data analytics tool that runs in real time. The traditional analysis tools were disk-based and had batch processing during the night. However, this tool runs in memory thereby leading to a faster analysis of data. The biggest advantage of the tool is that it is an open source tool and free of cost.

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