1. Linear Algebra: Linear algebra is an important area of mathematics for data scientists. Linear algebra provides tools for working with matrices and vectors, which are often used to represent and manipulate data in machine learning algorithms. For example, many machine learning algorithms involve finding the best set of parameters for a model, which is often done using linear algebra techniques such as matrix multiplication and eigenvalue decomposition.

2. Calculus:  Calculus is another area of mathematics that is frequently used by data scientists. Calculus provides the tools for understanding the behaviour of functions, which is important for many machine learning techniques such as gradient descent. Gradient descent is an optimization algorithm that is used to find the optimal set of parameters for a model by iteratively adjusting the parameters to minimize the error of the model. Calculus is also used for understanding the properties of a function, such as its local minima and maxima.

3. Probability and statistics: Probability and statistics is another important area of mathematics for data scientists. Probability and statistics provide the tools for understanding and modeling data, including techniques such as Bayesian inference and hypothesis testing. For example, a data scientist might use Bayesian inference to update their belief about a model’s parameters based on new data. Hypothesis testing is used to determine whether a set of data is consistent with a given hypothesis.

4. Discrete mathematics:  Discrete mathematics is another area of mathematics that is frequently used by data scientists. Discrete mathematics provides the tools for working with discrete data, such as integers, and for understanding algorithms and complexity. For example, discrete mathematics is used to understand the time and space complexity of algorithms, which is important for understanding how an algorithm will scale as the size of the input data increases.

In conclusion, data science is a broad field and the specific mathematics required can vary depending on the specific task or problem at hand. However, the areas of mathematics discussed in this blog post are commonly used by data scientists and form a solid foundation for understanding and working with data. While it’s not always required for data scientists to have a deep understanding of mathematics, it’s important for data scientists to have a general understanding of these areas to be able to communicate with other data scientists and to have a better understanding of the tools and techniques they’re using.