CONTENT

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

2. Designing experiments [PDF]
a. Data collections and their life cycle
b. Exploring and preparing data collections for building corpora
c. Architectural settings: from in house to large scale experiments

  • i. Parallel execution platforms & environments
  • ii. Multi-core programming
  • iii. Externalizing computation

3. Descriptive statistics [PDF]
a. Preparing data sets
b. Explanatory data analysis
c. Estimation

4. Statistical inference
a. The frequentist approach
b. Measuring the variability in estimates
c. Testing hypothesis

5. Supervised & unsupervised learning
a. What is learning? : problem statement & first steps

b. Unsupervised learning: Clustering [PDF]

c. Supervised learning