Math for Data Science

‘Math for Data Science’ is the third course in a series. To obtain the best results, consider taking the first two courses: ‘Data Science Masterclass for Beginners’ and ‘Python for Data Science: From the Basics to Advanced’. This specific course begins with an introduction to linear algebra by answering questions such as, ‘What makes equations linear?’. The priority is for students to understand systems of linear equations. Then, the focus shifts to approaches to solving matrix equations. After that, you discover an essential mathematical object - the vector space.

In the two remaining topics for linear algebra, you explore properties of vector spaces and see how to apply the previous knowledge via least-squares approximation. The next module introduces you to probability. You first grapple with the concept of a probability model and its axioms before exploring simple counting. You then learn to think about and solve simple discrete probability problems, including conditional Bayes’ theorem problems.

Afterward, you study random variables, probability mass function, expectation, and joint probability mass functions. You then advance to working with continuous variables and calculating probabilities for more than one variable at a time. Finally, you comprehend how to apply statistical inference to obtain insights. Data science is one of the most exciting fields of the 21st century. Since the start of the century, organizations have been keeping larger volumes of data, updating it more frequently, and using a wider variety of it (not just numbers but texts, tweets, audio, video, image, and more). The world needs more data scientists and business analysts to take the massive amount of raw facts and work with them to produce actionable insights.