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Discrete Math and Analyzing Social Graphs
by Владимир Подольский , Ilya V. Schurov , Stepan Kuznetsov- 4.4
Approx. 20 hours to complete
The main goal of this course is to introduce topics in Discrete Mathematics relevant to Data Analysis. We will start with a brief introduction to combinatorics, the branch of mathematics that studies how to count. Basics of this topic are critical for anyone working in Data Analysis or Computer Science. As prerequisites we assume only basic math (e....
Matrix Methods
by Daniel Boley- 4.1
Approx. 7 hours to complete
Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction....
Precalculus: Relations and Functions
by Joseph W. Cutrone, PhDTop Instructor- 4.6
Approx. 11 hours to complete
This course helps to build the foundational material to use mathematics as a tool to model, understand, and interpret the world around us. This is done through studying functions, their properties, and applications to data analysis. This course is designed for all students, not just those interested in further mathematics courses....
Probability Theory, Statistics and Exploratory Data Analysis
by Ilya V. Schurov- 4.7
Approx. 22 hours to complete
Exploration of Data Science requires certain background in probability and statistics. This course introduces you to the necessary sections of probability theory and statistics, guiding you from the very basics all way up to the level required for jump starting your ascent in Data Science. The core concept of the course is random variable — i....
Дискретная математика: подсчеты, графы, случайные блуждания
by Владимир Подольский- 0.0
Approx. 37 hours to complete
Основная цель курса — дать введение в разделы дискретной математики, важные для анализа данных. Мы начнем с краткого введения в комбинаторику, раздел математики, изучающий подсчеты. Основы комбинаторики критически важны для всех, кто работает в анализе данных или в Computer Science. После этого мы используем наши знания в комбинаторике в изучении дискретной вероятности....
Calculus and Optimization for Machine Learning
by Anton Savostianov- 4
Approx. 32 hours to complete
Hi! Our course aims to provide necessary background in Calculus sufficient for up-following Data Science courses. Course starts with a basic introduction to concepts concerning functional mappings. To provide an understanding of the practical skills set being taught, the course introduces the final programming project considering the usage of optimisation routine in machine learning....
First Steps in Linear Algebra for Machine Learning
by Dmitri Piontkovski , Vsevolod L. Chernyshev- 4.1
Approx. 21 hours to complete
The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. This Course is part of HSE University Master of Data Science degree program....
Precalculus: Mathematical Modeling
by Joseph W. Cutrone, PhDTop Instructor- 4.5
Approx. 10 hours to complete
This course helps to build the foundational material to use mathematics as a tool to model, understand, and interpret the world around us. This is done through studying functions, their properties, and applications to data analysis. This course is designed for all students, not just those interested in further mathematics courses....
Precalculus: Periodic Functions
by Joseph W. Cutrone, PhDTop Instructor- 4.7
Approx. 9 hours to complete
This course helps to build the foundational material to use mathematics as a tool to model, understand, and interpret the world around us. This is done through studying functions, their properties, and applications to data analysis. This course is designed for all students, not just those interested in further mathematics courses....
Information Theory
by Prof. Raymond W. Yeung- 4.7
Approx. 33 hours to complete
The lectures of this course are based on the first 11 chapters of Prof. Raymond Yeung’s textbook entitled Information Theory and Network Coding (Springer 2008). At the completion of this course, the student should be able to: 1) Demonstrate knowledge and understanding of the fundamentals of information theory. 3) Develop deeper understanding of communication systems....