Applied Statistics Real World Problem Solving
InstructorAkhil VydyulaThe curriculum spans fundamental measures of central tendency and dispersion to more than complex inferential topics like ANOVA and the Central Limit Theorem.
Editorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
This document outlines the Data Analytics course curriculum, covering key statistical concepts like descriptive statistics, probability distributions, hypothesis testing, and data cleaning techniques.
This instructional slide introduces the classification of data in statistics, highlighting nominal, ordinal, discrete, and continuous types alongside a hierarchical diagram distinguishing categorical from numerical data.
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Instruction includes techniques for data cleaning, preprocessing, and visual analysis using histograms, box plots, and heat maps.
Practical application is delivered via video lectures rather than interactive coding environments or hands-on software labs.
The curriculum aligns with the stated goals of business analysts and data scientists by covering essential statistical tools.
