Cons: Memory intensive-doesn’t execute as fast as some other languages.Ī programming language with a wide range of uses, Python is a must-have for any data analyst.

Pros: Easy to learn, highly versatile, widely-used.

Used for: Everything from data scraping to analysis and reporting.

Availability: Open-source, with thousands of free libraries.

Get started with these ten Excel formulas that all data analysts should know. Nevertheless, it’s an important and powerful data analysis tool, and with many plug-ins available, you can easily bypass Excel’s shortcomings. For instance, it runs very slowly with big datasets and tends to approximate large numbers, leading to inaccuracies.

SUMIF lets you create value totals based on variable criteria, and Excel’s search function makes it easy to isolate specific data. For instance, the CONCATENATE function allows you to combine text, numbers, and dates into a single cell.

It also has a variety of other functions that streamline data manipulation. Its invaluable built-in features include pivot tables (for sorting or totaling data) and form creation tools. Whatever your specialism, and no matter what other software you might need, Excel is a staple in the field.

What’s more, it features calculations and graphing functions that are ideal for data analysis. Cons: Cost, calculation errors, poor at handling big data.Įxcel: the world’s best-known spreadsheet software.Pros: Widely-used, with lots of useful functions and plug-ins.Mostly used for: Data wrangling and reporting.Here are the data analysis tools we’ll cover: Whether you’re preparing for an interview, or are deciding which tool to learn next, by the end of this post you’ll have an idea how to progress. Then we’ll move onto some of the more popular tools and platforms used by organizations large and small. We’ll start our list with the must-haves-the data analysis tools you can’t do without. What’s even better, a good few of those on this list contain AI data analytics tools, so you’re at the forefront of the field in 2023. From open-source tools to commercial software, you’ll get a quick overview of each, including its applications, pros, and cons. In this post, we’ll highlight some of the key data analytics tools you need to know and why. If you’re considering a career in the field, you’ll want to know: Which data analysis tools do I need to learn? As the field of data analytics evolves, the range of available data analysis tools grows with it.

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