Applied Math for Data Science

With the availability of data, there is a growing demand for talent who can analyze and make sense of it. This makes practical math all the more important because it helps infer insights from data. However, mathematics comprises many topics, and it is hard to identify which ones are applicable and relevant for a data science career. Knowing these essential math topics is key to integrating knowledge across data science, statistics, and machine learning.

In this course, learners will delve into a carefully curated list of mathematical topics to jumpstart proficiency in areas of mathematics that they will be able to apply immediately. They will grasp the fundamentals of probability, statistics, hypothesis testing, linear algebra, linear regression, classification models, and practical calculus. Along the way they will integrate this knowledge into practical applications for real-world problems.

• Gain a fundamental grasp of calculus, linear algebra, probability, statistics, and supervised machine learning.
• Apply mathematical fundamental principles in Python using standard mathematical libraries like NumPy and SymPy.
• Integrate multiple applied mathematical disciplines like linear algebra and calculus to perform tasks like gradient descent.

• You're a budding data science professional who wants to build foundational knowledge in essential math concepts and how they apply to probability, statistics, and machine learning.
• You're a programmer using data science and machine learning libraries and want to understand the math and probability principles behind them.
• You're managing a data science team and want to have a fundamental understanding of techniques used on the field.

Prerequisites:

• Beginner knowledge of Python (if-then conditionals, for loops, lists and other collections)