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#EMBA 27 Learnings Part 1: Data Analytics

Malobi Ogbechie Written by Malobi Ogbechie · 1 min read >

 Today’s blog will be covering a subject that was relatively new to me before commencing the Executive MBA at Lagos Business School. Not long ago, towards the end of 2021, a Ghanaian friend told me he graduated in Data Analytics but I never bothered to ask him what it was about. It was only till the first session with our lecturer that I got a taste of what I got myself into.

 If you google data analytics, you will probably get a definition that explains the subject as follows: Data analytics ‘is the systematic computational analysis of data or statistics; but what does that really mean?

 From what I now understand it is just the understanding, interpretation, and presentation of data to help in decision making. Data can come from different sources but data itself is just quantified facts or statistics. So if you have used excel to analyze information and present it at school or a company meeting, you have done data analytics! Crazy right? Granted, you couldn’t necessarily call yourself a data analytics expert, but still, you have practiced it to a certain degree.

 Please bear in mind that the data analytics subject goes way deeper than a simple excel spreadsheet. You also have tools like Python that are used for machine learning and Big Data. Python is a programming language that many prospective students are learning in their spare time in order to be more employable to some of the biggest tech companies around the world. It is also the programming language that many people use to create machine learning software tools and algorithms.

 Related to machine learning is artificial intelligence. In fact, machine learning, AI, and deep learning are all connected. Guess what unites all three? Data! So you can see that data analytics is not only breaking down excel equations and reporting, it is actually integral to a lot of the technology that is coming in the future. This is because, in order to have artificial intelligence, a machine has to learn. In order for that machine to learn, it needs to analyze data.

One question you could be asking yourself now is why not use excel for machine learning and AI. The simple answer is that excel can only handle a very limited amount of data. Excel can only handle just over a million rows. This is nothing compared to python and other tools.

 To conclude, I would say that data analytics is an interesting subject that has very relevant applications which are currently being used by some of the largest tech companies in the world. Companies like Google, Facebook, and Amazon are constantly hiring students that have a deep understanding of this subject. There are some more theoretical applications of data analytics that I chose not to cover in this blog post but maybe we can cover that later. Other parts of it just seem unnecessarily complicated. Either way, it is worth looking into if you see yourself being employed by Google in the future.

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