Description
Applied Mathematics for Data Science course. As data becomes more available, there is a growing need for talent that can analyze it and make sense of it. This makes applied mathematics that helps derive insights from data all the more important. However, mathematics encompasses many subjects and it can be difficult to know which ones are applicable and relevant to a career in data science. Knowing these essential mathematical topics is key to integrating knowledge in data science, statistics, and machine learning. In this course, learners carefully review a list of mathematics topics to learn areas of mathematics that they can apply immediately. They understand the principles of probability, statistics, hypothesis testing, linear algebra, linear regression, classification models, and practical computing. Along the way, they will integrate this knowledge into applications to real-world problems.
What you will learn in the Applied Mathematics for Data Science course
- Gain a fundamental understanding of calculus, linear algebra, probability, statistics, and supervised machine learning.
- Apply the fundamentals of mathematics in Python by using standard math libraries such as NumPy and SymPy.
- Integrate multiple applied mathematics disciplines such as linear algebra and analysis to perform tasks such as gradient descent.
This course is suitable for people who
- You are an aspiring data scientist who wants to gain a foundational knowledge of fundamental mathematical concepts and their application to probability, statistics, and machine learning.
- You are a programmer who uses data science and machine learning libraries and wants to understand the mathematical principles and probabilities behind them.
- You lead a data science team and want to gain a basic understanding of the techniques used in this field.
Specifications of the Applied Mathematics for Data Science course.
- Editor: Oreilly
- Lecturer: Thomas Nield
- Training level: beginner to advanced
- Training duration: 5 hours and 41 minutes
Course headings
Prerequisites for the course “Applied Mathematics for Data Science”.
- Beginner knowledge of Python (if-then conditions, for loops, lists and other collections)
Course pictures
Sample video of the course
installation Guide
After extracting, you can watch it with your favorite player.
English subtitles
Quality: 1080p
Download link
free download software
Size
1.3GB