Learn the statistics & probability for data science and business analysis
InstructorNikolai SchulerThe curriculum organizes learning into distinct stages, moving from descriptive statistics and probability theory through to hypothesis testing and regression analysis.
Instructional content includes explanations of mathematical formulas using plain language to aid understanding.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This educational slide defines standardized statistical moments including mean, variance, skewness, and kurtosis alongside their mathematical formulas and visual distribution graphs.
This diagram illustrates a normal distribution curve, highlighting that approximately 84.1% of data falls within one standard deviation above the mean.
This slide introduces ensemble methods by contrasting bagging and boosting, illustrating how bagging uses random data sampling with replacement to train multiple decision trees in parallel.
The curriculum focuses on surface-level coverage of regression and machine learning algorithms, lacking the depth required for advanced data analytics. This signal predates the displayed update; while the update label may have addressed it, it does not prove a correction.
The teaching style often skips theoretical and mathematical derivations, which may hinder an intuitive understanding for some learners. This signal predates the displayed update; while the update label may have addressed it, it does not prove a correction.
Learners have noted a lack of downloadable datasets for practice and some difficulty reading instructor notes written via light-pen. This signal predates the displayed update; while the update label may have addressed it, it does not prove a correction.
The content aligns well with the stated beginner target audience, though difficulty levels may vary depending on existing mathematical backgrounds.