Statistics can seem like a daunting and dry subject for real !, but fear not, Gen Z! In this article, we’ll take a fun and engaging approach to explore statistics and how they can be applied in data science.

Hey there Top-gs! Welcome to the first part of Statistically Speaking! In this article, we’re gonna dive into some super basic Qs about stats like what the heck it even is and why we need it for data science. Prepare to have your mind blown, fam!

Let’s be real on this topic, we’ll have this doubt, right? “Why Statistics and Why is it used in Data Science”.

WHY STATISTICS?

Statistics is like the superhero sidekick to data science. It helps us understand and make sense of all the data we collect. Without statistics, we’d be drowning in a sea of numbers and not know what to do with them.

In a nutshell, statistics is all about analyzing data to find patterns, make predictions, and draw conclusions. We use it in data science to help us uncover insights, make decisions, and build models that can help us solve problems.

Here’s an example to make the above paragraphs make sense:

Let’s say you’re a Rizzler who wants to know if you got that Rizz or not. So, you gather data by asking 100 girls if they think you got that Rizz or not. You find that 60 girls say “yes! you’re a Rizzler stop Rizzing me up “, and 40 say “No your Rizz is dead bro”.

Using statistics, you can analyze this data and conclude that the majority of girls think you’re a Rizzler. You can also calculate a “confidence interval” to determine how accurate your conclusion is based on the size of your sample (in this case, 100 girls).

So, in goofy terms, statistics help you decide whether you’re a Rizzler or not based on the other girls think about you. And as a Rizzler, you always want to Rizz 'em up!

Ok, now you get to know why statistics but

why do we use Statistics in data science?

In data science, statistics are used for a variety of purposes such as data cleaning, exploratory data analysis, hypothesis testing, regression analysis, and machine learning. It allows us to extract valuable information from large datasets, identify relationships between variables, and make predictions about future outcomes.

Statistics is like the magic wand of data science. It helps us wave away all the nonsense and find the gold nuggets of insight hidden in that big ol’ pile of data.

Using statistics, we can make predictions, find patterns, and draw conclusions that help us make smart decisions and avoid making big, fat mistakes. Whether we’re predicting the stock market, analyzing customer behavior, or trying to figure out why Karen keeps stealing all the staplers, statistics is the trusty sidekick that always has our back.

So, if you wanna be a data science superhero, you gotta embrace the power of statistics and use it to save the day!

Is there any math other than statistics for data science?

Oh yeah, there’s definitely more math than just stats in data science. We’ve got things like matrix math, fancy calculus, and some wild probability stuff. It’s like a math playground, and we get to play with all the coolest toys. But don’t forget about stats, it’s like the foundation that all these other math things are built. So basically, stats is like the OG math and the rest are like its cool math friends.

So, if you’re thinking about diving into the world of data science, then let me tell you that statistics is a must-have tool in your arsenal. But wait, don’t be scared! I’m here to help you out. I’m on a mission to share my knowledge with all of you so that you can become the coolest and most successful data scientists out there. So, let’s learn statistics together and rule the data world, Top-Gs!

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Yo, Top-g! Get ready to level up your data science game with the upcoming parts of Statistically Speaking! Join me on this journey by hitting that follow button and enabling notifications. And if you have any questions, slide into my DMs on Instagram or Twitter. See you there!