Dive into a comprehensive 8-hour university-level course on data science basics, focusing on essential statistical methods. Master the art of collecting, organizing, summarizing, interpreting, and drawing conclusions from data. Explore real-life examples to understand how statistical methods serve as analytical tools for developing generalizations and meaningful conclusions across various fields of study. Cover key topics including sampling techniques, experimental design, data visualization methods, measures of central tendency and variation, probability distributions, correlation analysis, and the central limit theorem. Gain practical skills in creating and interpreting frequency histograms, time series plots, box-and-whisker plots, and scatter diagrams. Learn to apply concepts such as z-scores, normal distribution, and sampling distributions to real-world scenarios. Taught by Monika Wahi, this course offers a solid foundation in statistics, emphasizing its practical applications beyond mere mathematical calculations.
Overview
Syllabus
) What is statistics.
) Sampling.
) Experimental design.
) Randomization.
) Frequency histogram and distribution.
) Time series, bar and pie graphs.
) Frequency table and stem-and-leaf.
) Measures of central tendency.
) Measure of variation.
) Percentile and box-and-whisker plots.
) Scatter diagrams and linear correlation.
) Normal distribution and empirical rule.
) Z-score and probabilities.
) Sampling distributions and the central limit theorem.
Taught by
freeCodeCamp.org
