Lectures related to Zipf's law

Explores heavy-tailed distributions, the Hill estimator, convergence to Gaussian, and distribution comparison.

Descriptive Statistics: Hypothesis Testing

Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.

Node Degree and Strength

Explores brain node connectivity, node degree, strength, random networks, power law distributions, and the complexity of real networks.

Big Data Challenges: Scaling to Massive Data

Explores challenges of handling massive data in the era of big data, discussing solutions like MapReduce and Spark.

Describing Data: Statistics & Uncertainty

Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.

Scale-Free Networks: Power Laws and Preferential Attachment

Explores scale-free networks, power laws, preferential attachment, and network assortativity.