Data Science vs Data Analytics

YOU HAVE NOW ENTERED THE DATA DIMENSION. WE HOPE YOU ENJOY YOUR STAY.

Data Science vs Data Analytics

So, data science and data analytics are terms that are constantly confused with each other. Who can blame anyone? They both have the word data in them and they both seemingly are about similar things. There are even terms like “big data” and “data mining” - even some experts conflate these terms sometimes! Being someone who is exploring the field of data science, I wanted to get these terms down and get to the bottom of what differentiates them. I mean, I have to know what I’m getting into here, and if you plan on going in this field too then so will you!

So let’s start data analytics. It’s about examining large data sets to identify patterns and create visual representations of the data to help companies make better business decisions. In a nutshell, it’s essentially what it says - you are analyzing the data and using it to help people. As a career, data analysts do different things in different industries, but essentially, they help businesses identify trends and insights to find a solution to issues they may have. For example, a business would likely ask the question “why did our profitability for a given quarter go up/down?” or “how well did our product do in certain regions?”. Data can identify trends that help understand what happens or why one product is bought more. Let’s use a shoe store for example. If people are only entering the store to buy one specific type of shoe, then the company would put more of that shoe on stock in order to satisfy the majority of its consumers. This can also be affected by outside factors. For example, many of you likely remember when the lockdowns start in March 2020 from the COVID-19 pandemic. Because of this, the popularity of meeting apps such as Zoom and Webex skyrocketed so both businesses and schools could still meet with each other. Around the same time, iPads were also popular because some parents wanted their kids to have a device. When analyzing trends, analysts have to be cautious that certain spikes in profitability are primarily due to outside factors and not necessarily the product itself.


So what is data science then? Data scientists design and create algorithms and prototypes with their main question being “what if?”. While data analysts analyze historical data, data scientists use that historical data to predict what will happen in the future, so they build predictive models to help. Some companies can use this to their advantage. If they can see a pattern of when items spiked in popularity in previous years, it can ultimately benefit them. For example, if, say, 1,000 copies of Mario Kart sold within the first 30 minutes of being launched, you can use that to predict the kinds of games that you’d want to promote to people so they can also play those similar games. Hopefully, I’ve differentiated between these two well enough.


Let’s end this post with this quote which I admittedly laughed out loud at: “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” - Dan Ariely


YOU ARE NOW EXITING THE DATA DIMENSION. SEE YOU SOON!

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