Tag: probability

Carnegie Mellon University – Master of Science in Machine Learning

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From Carnegie Mellon: “The Machine Learning Department is made up of a multi-disciplinary team of faculty and students across several academic departments. Machine learning is dedicated to furthering the scientific understanding of automated learning, and to producing the next generation of tools for data analysis and decision making based on that understanding…. The MS in Machine Learning is ideal for students considering a career in industry or as preparation for … Continue Reading

Columbia University – Master of Science in Data Science

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From Columbia: “The Master of Science in Data Science allows students to apply data science techniques to their field of interest, building on four foundational courses offered in our Certification of Professional Achievement in Data Sciences program. our students have the opportunity to conduct original research, included in a capstone project, and interact with our industry partners and faculty. Students may also choose an elective track focused on entrepreneurship or … Continue Reading

Columbia University – Certification of Professional Achievement in Data Science

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From Columbia: “The Certification of Professional Achievement in Data Sciences prepares students to expand their career prospects or change career paths by developing foundational data science skills. Join us from anywhere in the world as this program is now also offered online.” Required/Core Courses: Algorithms for Data Science: Methods for organizing data, e.g. hashing, trees, queues, lists, priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. … Continue Reading

Probability: Theory and Examples

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From Amazon: “This book is an introduction to probability theory covering laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems.”

Probability and Random Processes

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From Amazon: “This book gives an introduction to probability and its many practical applications by providing a thorough, entertaining account of basic probability and important random processes, covering a range of important topics. Emphasis is on modelling rather than abstraction and there are new sections on sampling and Markov chain Monte Carlo, renewal-reward, queuing networks, stochastic calculus, and option pricing in the Black-Scholes model for financial markets. In addition, there … Continue Reading

Statistical Inference

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From Amazon: “This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be … Continue Reading

Data Science Essentials

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from edX: “Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from Duke University and Microsoft. In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Microsoft Azure Machine Learning platform, or … Continue Reading

Statistics and Probability

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A list of courses from the Khan Academy covering the following topics: Introduction to statistics Analyzing categorical data Displaying and comparing quantitative data Summarizing quantitative data Modeling data distributions Exploring bivariate numerical data Study design Probability Counting, permutations, and combinations Random variables Sampling distributions One-sample confidence intervals One-sample z and t significance tests Two-sample inference for the difference between groups Inference for categorical data (chi-square tests) Advanced regression (inference and … Continue Reading

MIT Statistics Cheat Sheet

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A long list of definitions, equations, and examples for common statistical terms and tests, including: Variance Standard Deviation & Error T-tests Chi-Square Tests Probability Distributions

mathematicalmonk

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From Youtube: “Videos about math, at the graduate level or upper-level undergraduate”, these videos cover topics in mathematics and statistics that are less than 15 minutes long, with narration over written text and equations. Topics include: Machine Learning Probability Primer Information Theory