Build a Foundation for your Data Science Skills. Master a wide range of math underlying Data Science and learn how to apply it in practice. Each course of the specialisation ends with a project that gives an opportunity to see how the material of the course is used in Data Science. Each project is directed at solving practical problem in Data Science. In particular, in your projects you will analyse social graphs, predict estate prices and uncover hidden relations in the data. A Coursera Specialization is a series of courses that helps you master a skill.
Mathematical statistics is the application of probability theory , a branch of mathematics , to statistics , as opposed to techniques for collecting statistical data. Specific mathematical techniques which are used for this include mathematical analysis , linear algebra , stochastic analysis , differential equations , and measure theory. Statistical data collection is concerned with the planning of studies, especially with the design of randomized experiments and with the planning of surveys using random sampling. The initial analysis of the data often follows the study protocol specified prior to the study being conducted.
As Josh Wills once said,. In fact, Mathematics is behind everything around us, from shapes, patterns, and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives. In this blog post, you will understand the importance of Math and Statistics for Data Science and how they can be used to build Machine Learning models. To become a successful Data Scientist you must know your basics.
We use cookies to give you a better experience. Data analysis is inextricably linked with maths. While statistics are the most important mathematical element, it also requires a good understanding of different formulas and mathematical inference.