{"id":174262,"date":"2024-05-06T08:00:21","date_gmt":"2024-05-06T12:00:21","guid":{"rendered":"https:\/\/www.kdnuggets.com\/?p=174262"},"modified":"2024-05-05T19:57:00","modified_gmt":"2024-05-05T23:57:00","slug":"a-comprehensive-guide-to-essential-tools-for-data-analysts","status":"publish","type":"post","link":"https:\/\/www.kdnuggets.com\/a-comprehensive-guide-to-essential-tools-for-data-analysts","title":{"rendered":"A Comprehensive Guide to Essential Tools for Data Analysts"},"content":{"rendered":"

\"Tools<\/p>\n

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When you think of data analysis, what are the four main tasks you always have to do? Forget about those fancy infographics showing the data analysis cycle; let\u2019s keep it very simple: you get the data, you manipulate it, you analyze it, and you visualize it.<\/span><\/p>

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Hopefully, you won\u2019t want to do that by using the abacus and shifting through the papyrus scrolls. Nothing against being retro, but let\u2019s at least embrace the electricity. Possibly also some other nice tools that all those tech guys and gals created to earn money. Sorry, <\/span>help us in our data analysis journey<\/span><\/i>.<\/span><\/p>\n

My sarcasm aside, there are some really useful tools for data analysts that allow for data to be used and analyzed very elegantly.<\/span><\/p>\n

I have already written about some of them when I covered the most useful <\/span>tools for data scientists<\/span><\/a>. Now, it\u2019s time to do the same for data analyst tools.<\/span><\/p>\n

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Data Analyst Tools Overview<\/strong><\/h2>\n

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Most tools I\u2019ll discuss can do everything data analysts do, from fetching and manipulating data, to analyzing and visualizing it.<\/span><\/p>\n

Of course, they\u2019re not equally good at all those tasks. So, I tried to rank their use in the overview below. This should help you understand when to use what tool.<\/span><\/p>\n

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\"Tools<\/p>\n

In the broadest sense, the data analyst tools can be categorized into programming languages and spreadsheets\/BI tools.<\/span><\/p>\n

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Programming Languages<\/strong><\/h2>\n

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1. SQL<\/strong><\/h3>\n

Use:<\/b> Fetching, manipulating, analyzing data<\/span><\/p>\n

Description:<\/b> SQL<\/span><\/a> is the ultimate master in querying data saved in relational databases. It\u2019s specifically designed for extracting and manipulating data and making changes to data (such as inserting, updating, or deleting) directly in the database. It\u2019s designed for precisely that purpose, and it fulfills it brilliantly!<\/span><\/p>\n

It\u2019s also quite good at analyzing data. However, it can show its limitations compared to the programming languages below.<\/span><\/p>\n

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2. Python<\/strong><\/h3>\n

Use:<\/b> Fetching, manipulating, analyzing, visualizing data<\/span><\/p>\n

Description:<\/b> Python<\/span><\/a> is a general-purpose language, a darling of data scientists and data analysts. It\u2019s relatively easy to learn and has plenty of specific-purpose libraries for data analysis tasks.<\/span><\/p>\n

Data analysts typically write Python code in <\/span>Jupyter Notebook<\/span><\/a> directly or through the services such as <\/span>Google Colab<\/span><\/a> or <\/span>Anaconda<\/span><\/a>. There are also some other similar tools, such as <\/span>Sage Maker<\/span><\/a>, which is nothing but Amazon\u2019s version of Jupyter Notebook.<\/span><\/p>\n

Using notebooks means you can code and view your code\u2019s output step-by-step. This is much easier than the traditional coding in IDEs and code editors.<\/span><\/p>\n

What makes Python so flexible is a wide range of libraries for different purposes.<\/span><\/p>\n

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\"Tools<\/p>\n

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With Python, you can <\/span>connect to a database and fetch the data<\/b> via various toolkits:<\/span><\/p>\n