# What statistics should I know to do data science?

Although there are predefined functions & commands in programming languages such as Python, R, SAS etc. which performs the statistical modelling in the background, students must have a basic know-how of the algorithm or hypothesis used by these statistical methods. A good starting point could be to understand the metrics of variation & central tendency; followed by different hypothesis testing, and finally progressing towards more complicated classification & logistic algorithms.

Statistics is a powerful tool that is used to perform Data Science. It is the use of mathematics in order to perform the technical analysis of the data. The use of statistics helps in retrieving high-level information and also in operating with data in an informative and targeted manner. Statistics also helps in gaining insights into how the data is structured based on which one can apply different accurate data science techniques to get the right decision.

following are the topics which I consider, a data scientist should know :

1. Probability (Bayes rule, Conditional Probability)
2. Linear Algebra (Gaussian Elimination, Rank determination and Augmented Matrices)
3. Discrete Mathematics (Permutation and Combination, Set theory, Graph theory and Mathematical Induction)
4. Mathematical Modeling and Logic

Advanced machine learning algorithms in data science use statistics to find and turn data patterns into useful evidence.

Statistics are used by data scientists to collect, assess, analyze, and derive conclusions from data and apply quantified mathematical models to applicable variables. Data scientists operate in a variety of capacities, including programmers, academics, and corporate executives. However, there is one thing that all of these fields have in common: a statistical foundation. As a result, statistics are just as important as programming languages in data science.

Data scientists give businesses information-driven, targeted data beyond basic data visualization. This procedure is tightened by advanced statistics mathematics, which cultivates concrete conclusions.

Some of the statistical skills you need to know as a data scientist are:

• Data manipulation: Data scientists can clean and organize enormous data sets using Excel, R, SAS, Stata, and other applications.

• Attention to detail and critical thinking: Using linear regression, data scientists identify and model links between dependent and independent variables. Data scientists select methods that include built-in assumptions that are taken into account during implementation. Results will be skewed if assumptions are violated or chosen incorrectly.

• Problem-solving and innovation: Data scientists utilize applied statistics to connect abstract discoveries to real-world problems, in addition to pure computations and fundamental data analysis.