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.Course
Gain insight into a topic and learn the fundamentalsnew
- 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.- Course OverviewPreview
- Absolutely Continuous and Discrete Random VariablesPreview
- Cumulative Distribution Functions and Probability Density FunctionsPreview
- Characteristics of Random VariablesPreview
- Random VectorsPreview
- Useful Properties of the Gaussian DistributionPreview
- Challenge: Detecting Outliers Using 3-Sigma RulePreview
- General population. Samples. Population parameters.Preview
- Momentum estimation. Maximum Likelihood EstimationPreview
- Challenge: Estimate Parameters of Chi-square DistributionPreview
- Unbiased EstimationPreview
- Challenge: Checking Bias of An Estimation Using SimulationPreview
- Consistent EstimationPreview
- Efficient EstimationPreview
- Confidence Intervals for Population ParametersPreview
- Challenge: Confidence Interval for Exponential Distribution ParameterPreview
- What is Statistic Hypothesis? Type 1 and Type 2 ErrorsPreview
- What is P-value?Preview
- Comparing Means of Two Different DatasetsPreview
- Challenge: Using CLT to Compare Mean Values of Non-Gaussian DatasetsPreview
- Challenge: Resampling Approach to Compare Mean Values of the DatasetsPreview
- Testing the Hypothesis of Independence of Two Random VariablesPreview
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