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TOP 10 ESSENTIAL DATA ANALYTICS TOOLS FOR DATA ANALYSTS

To perform data analysis at the best possible level, analysts and data professionals need<br>effective software. Although there are different solutions in the market, data analysts<br>must choose wisely to benefit their analytical efforts.

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TOP 10 ESSENTIAL DATA ANALYTICS TOOLS FOR DATA ANALYSTS

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  1. TOP 10 ESSENTIAL DATA ANALYTICS TOOLS FOR DATA ANALYSTS INTRODUCTION To perform data analysis at the best possible level, analysts and data professionals need effective software. Although there are different solutions in the market, data analysts must choose wisely to benefit their analytical efforts. To get a better understanding of these tools, Data Analytics Online Training in Dubai can be helpful in determining the correct tool for your data analysis. To help you choose the best software for your analysis, we have compiled a list of the best data analyst tool. Essential Data Analytics Tool 1.R Programming R is the leading analytics tool in the industry and is widely in use for statistics and data modeling. It can easily manipulate data and present it in multiple ways. It has more functionalities than SAS in many ways including the capacity of data, performance, and

  2. outcome. R also provides tools to automatically install all packages as per user needs and requirements, which can also help you with big data. 1.Tableau Public Tableau Public is a free tool that connects any data from all sources. Be it corporate Data Warehouse, Microsoft Excel, or even web-based data. It also creates data visualizations, maps, dashboards, and more with real-time updates presented on the web. You can also share this on social media or even with your client and also allows you to download the files in various formats. Additionally, Tableau’s Big Data capabilities make it extremely important. 1.Python Python is an object-oriented scripting language that is easy to read, write, and even maintain. Additionally, it is a free and open-source tool. It supports both functional and structural programming methods. Python is however easy to learn as it is very similar to JavaScript, Ruby, and PHP. 1.SAS SAS is a programming environment and also language for data manipulation and is even a leader in the world of analytics. SAS is easily accessible, and manageable and moreover can analyze data from any source. Today, SAS is widely in use for profiling customers and prospects. It can help in predicting their behaviors, manage, and optimizing communications. 1.Apache Spark Apache Spark is a fast large-scale data processing engine that executes applications in Hadoop clusters faster in memory and on disk. Spark is basically built on data science. Spark is popular for data pipelines and also machine learning model development. 1.Excel Excel is a basic, popular, and widely in-use analytical tool in almost every industry. Whether you are an expert in Sas, R, or even Tableau, you will still need to use Excel. It analyzes the complex task that summarizes the data with a preview of pivot tables that

  3. further helps in filtering the data as per client needs. Excel has an advanced business analytics option that further helps in modeling capabilities. 1.RapidMiner RapidMiner is a powerful data science platform. It comes from the same company that performs predictive analysis and other advanced analytics like data mining, text analytics, machine learning, and visual analytics without any programming. This tool is very powerful and can generate analytics based on real-life data transformation settings so that you can control the formats and data sets for predictive analysis. 1.KNIME KNIME is a leading open-source, reporting, and integrated analytics tool that enables you to analyze and model data through visual programming. It integrates different components for data mining and machine learning through its modular data pipelining concept. 1.QlikView QlikView offers many unique features such as patented technology and in-memory data processing. This executes the result rapidly to the end-users and stores the data in the report itself. Data association in QlikView is automatically maintained and can further be compressed to almost 10% of its original size. 10. Splunk: Splunk is a tool that analyzes and searches for machine-generated data. It pulls text- based log data and offers a simple way to search through it. A user can pull in all varieties of data, perform all sorts of statistical analyses, and further present it in multiple formats CONCLUSION By going through these data analytics tools, you must have got a good understanding of data analytics tools. However, you should initially start by understanding your

  4. company’s data needs and then proceed to the correct analytics tool. With Data Analytics Online Certification in Qatar, you can grow in your career in data analytics and add a certification to your credibility. Moreover, an add-on certification will make you stand out from the crowd and add more visibility to your skills.

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