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Advanced Probability Theory | Codefinity
Advanced Probability Theory

Advanced Probability Theory

Statistics and probability theory are fundamental tools in data analysis, decision-making, and scientific research. They provide a systematic and quantitative way to understand and interpret data, make predictions, and draw conclusions based on evidence. Now we will consider all additional topics necessary for Data Science and Data Analytics.

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Gain insight into a topic and learn the fundamentals

5.0

(4)

Advanced
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27 chapters
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Learning outcomes
Modules
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Outcomes
Learning outcomes
Modules
Testimonials
Recommendations
Codefinity advantages
FAQs
What you will learn
  • Gain a comprehensive understanding of additional probability theory topics pertinent to data science and analytics.
  • Develop proficiency with both absolutely continuous and discrete random variables.
  • Grasp the principles of cumulative distribution functions and probability density functions.
  • Explore and characterize the features of random variables and random vectors.
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There are 6 modules in this course

Statistics and probability theory are fundamental tools in data analysis, decision-making, and scientific research. They provide a systematic and quantitative way to understand and interpret data, make predictions, and draw conclusions based on evidence. Now we will consider all additional topics necessary for Data Science and Data Analytics.
Now we will understand some fundamental theoretical concepts which are used in solving real live tasks: absolutely continuous and discrete random variables, probability density function, cumulative distribution function, the characteristics of a random variable, etc.
The limit theorems of probability theory are fundamental laws of probability theory that are often used in practice in a wide variety of areas, such as: building confidence intervals, estimating distribution parameters, providing A/B testings, creating ensembles of ML models, etc. Now we will consider two of the most commonly used: the Law of Large Numbers and the Central Limit Theorem.
We have already learned how to estimate the parameters of the population. But to estimate the parameter, we make an assumption about the population distribution. Can we say that our assumption is correct? How do we prove that the estimated parameters are the real parameters of the population? Can we show that two sets of samples are independent? To answer these questions, it is necessary to consider the concept of hypothesis testing.

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Learner reviews

5.0

4 reviews

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Showing 3 of 4 reviews

Author

Lex Xai

5

Reviewed on Jun 23, 2024

Nice

Author

Yurii Kuchirka

5

Reviewed on Jan 5, 2024

It is very good course for this complicated topic. Anyway it would be good to provide more practical examples from real life usage of such material

Author

Subhransu Mohanty

5

Reviewed on Dec 16, 2023

Very well explained

Recommended if you're interested in learning Python

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