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Master Data Science step-by-step
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Module 1
Strengthening Statistical Foundations
1
Parameters vs. Statistics
Distinguish between population parameters and sample statistics.
2
Confidence Intervals: Precision and Sample Size
Interpret confidence intervals and explain their relationship to sample size.
3
T-Tests in Python: Comparing Group Means
Perform and interpret a t-test to compare group means using Python.
Updating Beliefs with Bayes' Theorem
Apply Bayes' theorem to update the probability of a hypothesis given new evidence.
5
Parametric vs. Non-Parametric Tests: When to Use Which
Identify when to use parametric vs non-parametric statistical tests.
6
Correlation vs. Causation: Unpacking Relationships
Calculate and interpret correlation coefficients, distinguishing correlation from causation.
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Exploratory Data Analysis in Practice