I’ve always admired the immense power of Excel. This software is not only capable of doing basic data computations, but you can also perform data analysis using it. It is widely used for many purposes including the likes of financial modeling and business planning. It can become a good stepping stone for people who are new to the world of data analysis.
Even before learning R or Python, it is advisable to have knowledge of Excel. It does no harm to add excel in your skill sets. Excel, with its wide range of functions, visualization, arrays empowers you to quickly generate insights from data which would be hard to see otherwise.
It has a few drawbacks as well. It can’t handle large data sets very efficiently. I’ve personally faced this issue. Try doing computations of data ~ 200,000 entries and you’ll notice that excel starts struggling. There are ways to work around and handle this data to some extent, but Excel is not a big data tool. In such cases, R or Python are the best bets.
I feel fortunate that my journey started with Excel. Over the years, I’ve learnt many tricks to work to deal with data faster than ever. Excel has numerous functions. It becomes confusing at times to choose the best one. In this article, I’ll provide you some tips and tricks to work on Excel and save you time. This article is best suited to people keen to upgrade their data analysis skills.
Note: If you think you are a master coder in data science, you won’t find this article useful. For others, I’d recommend you to practice these tricks to develop a concrete understanding of them.