CONTENT

1. Introduction [General Lecture Description PDF]
a. Data centric sciences: principles and common aspects [PDF]
b. Digital data collections: characteristics and properties [PDF]
c. Data science: big data, data analytics algorithms & tools [PDF]

2. Designing experiments [PDF]
a. Data Science Pipeline
b. Exploring and preparing data collections for building corpora

3. Data Analytics Methods

3.1 Descriptive statistics

a. Preparing data sets
b. Explanatory data analysis
c. Estimation

3.2 Statistical inference [PDF]
a. The frequentist approach
b. Measuring the variability in estimates
c. Testing hypothesis

3.3 Unsupervised learning: clustering

3.4 Network Science: dealing with graphs [PDF]