Data analytics involves utilizing a variety of techniques to make sense of large and complex data sets. This could include collecting data from multiple sources, cleaning and preparing the data, using analytical tools such as Excel or R to uncover insights, and then communicating those findings to others through visualizations and reports. The goal of data analytics is to find useful information and use it to inform decisions and conclusions. People working in data analytics are commonly found in sectors like healthcare, retail, finance, and technology.
The field of data analytics will expand more in the future as more organizations are recognizing the benefits of data-driven decision making. With the increasing amount of data being generated by businesses, governments and individuals, the demand for individuals with data analytics skills is also increasing. With advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) becoming more prevalent, data analysts will have the ability to process bigger and more complex datasets and make more accurate predictions. This will lead to faster and more accurate decisions, and better results in areas like finance, technology,retail, healthcare etc as mentioned above . The rise of IoT will also create more data, which will lead to more opportunities to extract insights and make data-driven decisions.
So in case you want to have a growing career, data analyst is that job. And knowing these skills will keep you at the top in the list of recruiters if you want to be a Data Analyst:
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<h4>SQL(Structured Query Language):</h4>
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To perform any operation on data we need to have the data stored somewhere and as we know, SQL is a very powerful tool for easily accessing, manipulating, analyzing and storing data in relational databases. In other words, to perform any sort of operation on data, we need to know SQL.
SQL is also a widely used and standardized language, making it easy for data analysts to work with data from different sources. This makes it an essential tool for data analysts who need to work with data from different systems and platforms. Additionally, SQL is supported by a wide range of analytical and business intelligence tools, which makes it easy to integrate with other tools and technologies used in data analytics.&nbsp;So you should have a good command of SQL.
<h4 dir="ltr">Programming Language:&nbsp;</h4>
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There are several programming languages which are helpful for data ana;lysis depending upon the tasks and needs of the organization. Most commonly used languages like java,scala spark,python,R any of these can be used for data analysis in fact the data analysts use a combination of languages and tools to complete their works.
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For recommendation, You can learn python as it is a very simple language, easy to understand and it is very powerful at the same time as it has extensive libraries for data analysis and visualization such as matplotlib, pandas, numpy&nbsp; etc.
<h4 dir="ltr">Excel:&nbsp;</h4>
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Excel provides a wide range of features&nbsp; like sorting, filtering etc for cleaning and transforming&nbsp; the data. It also provides a wide range of features for visualizing data, such as charts, graphs, and tables, which makes it easy to communicate data insights to others. Also, it is easy to automate repetitive tasks for excel using its VBOs and macros, along with that it is easy to be integrated with other tools to work with like R, SQL, Python etc.
So basic excel operations are recommended for data analysis. 
<h4 dir="ltr">Statistics:&nbsp;</h4>
Statistics plays a crucial role in data analysis as it allows data analysts to extract meaningful insights from large and complex data sets. It provides a variety of techniques and methodologies that allow analysts to understand patterns, relationships and trends in the data. With the help of statistical methods, analysts can make predictions and draw conclusions that can help in decision-making. Additionally, statistics also helps in validating the results and inferences drawn from the data, giving analysts a higher level of confidence in their findings. In summary, statistics acts as a key tool for data analysts to understand and interpret data, making it a very important part of the data analysis process.
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<h4 dir="ltr">Data visualization Tool:&nbsp;</h4>
Tools like Looker or Tableau are helpful. You can learn any data visualization tool. Some of these are:
Tableau: Tableau is a widely used data visualization tool that allows users to create interactive visualizations, dashboards, and reports. It has a user-friendly interface and a wide range of built-in chart types and visualizations.
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Power BI: Power BI is a data visualization tool from Microsoft that allows users to create interactive dashboards and visualizations. It has a wide range of data connectors and integration options.
Looker: Looker is a data visualization tool that allows users to create interactive dashboards and reports. It has a wide range of data connectors and integration options.
<img src="media/blogs/looker.png" alt="" width="400" height="200">
<h4 dir="ltr">Communication:</h4>
Communication skill is very important for any profession, hence it goes for data analysis as well. To make people understand about your data, you need to have great communication skills. 
So conquer these skills and tools and you&rsquo;ll always be on the top in the list of recruiters.
Happy learning!!!
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Data analytics involves utilizing a variety of techniques to make sense of large and complex data sets. This could include collecting data from multiple sources, cleaning and preparing the data, using analytical tools such as Excel or R to uncover insights, and then communicating those findings to others through visualizations and reports. The goal of data analytics is to find useful information and use it to inform decisions and conclusions. People working in data analytics are commonly found in sectors like healthcare, retail, finance, and technology.

Crucial skills for data analytics

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