**Takeaways from this article **

• In this article, we understand why data is important, and talk about the importance of statistics in data analysis and data science.
• We also understand some basic statistics concepts and terminologies.
• We see how statistics and machine learning work in sync to give deep insights into data.
• We understand the fundamentals behind Bayesian thinking and how Bayesian theorem works.

**Introduction **

Data plays a huge role in today’s tech world. All technologies are data-driven, and humongous amounts of data are produced on a daily basis. A data scientist is a professional who is able to analyse data sources, clean and process the data, understand why and how such data has been generated, take insights from it, and make changes such that they profit the organization. These days, everything revolves around data.

• Data Cleaning: It deals with gathering the data and structuring it so that it becomes easy to pass this data as input to any machine learning algorithm. This way, redundant, irrelevant data and noise can also be eliminated.
• Data Analysis: This deals with understanding more about the data, why the data has yielded certain results, and what can be done to improve it. It also helps calculate certain numerical values like mean, variance, the distributions, and the probability of a certain prediction.

#data-science