It’s not Data Science without Statistics
And why it's a pre-requisite for all aspiring Data Scientists.
I was in high school when I first had my statistics subject, and up to this day, I can’t forget how much I dreaded those years. I can still remember how tedious I was counting the number of occurrences per data point, memorizing so many formulas, and the worst part is getting an “F” after a whole day of exam because I overlooked the class size. I just thought back then that this too will be over and I won’t need to deal with it ever again.
Ironically, here I am now writing a blog on why I can’t live without statistics because I can never be a data scientist without it.
But before answering that, let’s first define Data Science and Statistics.
According to datarobot.com, Data Science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.
While Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data.
To sum that up, Statistics is one of the foundations of Data Science that quantifies our data to makes measurable insights into accurate decisions.
It is through Statistics we can make sense of our data and measure uncertainty in it. And to know and to be able to measure uncertainty is very important in Data Science cause most things in the real world are never exact.
Concepts such as descriptive statistics, probability, and sampling methods are some basic foundations crucial to becoming a Data Scientist. If you want to learn more about them you may want to check it out here: https://towardsdatascience.com/the-5-basic-statistics-concepts-data-scientists-need-to-know-2c96740377ae
But before applying all these high-level concepts, and I think the most important above them all is Data Cleaning. Having a clean data and structuring them to be ready for analysis will make the next steps flow smoothly. And thanks to the advancements in technology, all the tools we need to make clean and structured massive data are just within our fingertips (and no tedious manual counting will ever be needed).
To conclude, this story is just a reminder that you should never hate anything for life cause you never know, after years it might be everything you’ll ever need.
Just like how a Data Scientist needs Statistics.
