How much Mathematics is required for Data Science and how do I learn?

Mathematics is an integral component of Data Science. Building a strong foundation in topics like Probability, Statistics, Linear Algebra, Differential Calculus, Optimisation etc. will hold you in good stead in your data science career.

Having said that, it is important to note that the required level of understanding of the mathematical underpinnings of machine learning methods varies depending on the type of data science role, company, and the business domain.

For example, if you are a product data scientist at big e-commerce company, you may not need to dive deep into the underlying math to excel at your job. On the other hand, if you are a research/applied scientist in an R&D division or a financial trading firm, you do need to have strong fundamentals in mathematics to better understand existing algorithms and develop novel techniques.

Following is a list of recommended resources to get you started in your journey towards learning the mathematics of machine learning:

Resources:

• Coursera: Mathematics for Machine Learning Specialization [course]
• Mathematics for Machine learning [book]
• MIT: Mathematics of Machine Learning [course]
• Probabilistic Machine Learning [book]
• Matrix calculus for Deep Learning [paper]
• Algebra, Topology, Calculus, Optimisation theory for ML [book]