Statistics is one of the most important concepts you need to know as a data scientist because without any knowledge of statistics you cannot understand data. If you are learning data science without any knowledge of statistics, this article is for you. In this article, I’ll take you through why statistics is important for data science.
Why Statistics is Important for Data Science?
The ultimate goal of a data scientist is to make sense of the data. In data science, we have to use a programming language and many other tools to work with data. When working with data, the most important part is understanding it so that you can think about the next steps you need to take as a Data Scientist. If you don’t know statistics, how will you analyze a dataset, because statistics is the most important concept when analyzing a dataset.
It doesn’t matter that you get a bachelor’s, master’s, or certification based on statistics. What is more important is learning the concepts of statistics so that you can analyze a dataset and answer questions based on how the business is performing according to the dataset.
For example, think of a dataset based on a company’s performance over the past 5 years. If you have a good grasp of Python fundamentals for data science, you can easily create many visualizations and easily train a machine learning model on your dataset. But if someone asks you what your model tells you about the relationship between independent variables and dependent variable, then how will you answer them?
Just forget about the machine learning model, if someone asks you about data distribution what are you going to say if you have no idea? This is why statistics is very important for data science. It is much more than just calculating the mean, the median and the mode.
If you are a data scientist and don’t know statistics, you are just a professional who can work with data using data science tools. To become a data scientist, you have to think like a statistician while analyzing a dataset. Hope you now understand why statistics is important for data science. Please feel free to ask your valuable questions in the comments section below.