Applied Math for Data Science

Applied Math for Data Science
MP4 | Video: h264, yuv420p, 1920×1080 | Audio: aac, 44100 Hz | Duration: 5h 41m | 1.45 GB

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.

What you’ll learn and how you can apply it
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.
This course is for you because.
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.

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