MSc:AdvancedStatistics old

Advanced Statistics

• Course name: Advanced Statistics
• Course number: DS-03
• Area of instruction: Math

Administrative details

• Faculty: Computer Science and Engineering
• Year of instruction: 1st year of MSc
• Semester of instruction: 2nd semester
• No. of Credits: 5 ECTS
• Total workload on average: 180 hours overall
• Frontal lecture hours: 2 hours per week.
• Frontal tutorial hours: 0 hours per week.
• Lab hours: 2 hours per week.
• Individual lab hours: 2 hours per week.
• Frequency: weekly throughout the semester.
• Grading mode: letters: A, B, C, D.

Course outline

The course covers substantially two topics: most of the course (about 80%) is about nonparametric statistics, especially with reference to non parametric tests and bootstrap; then the courses discusses Gamma analysis and sequence analysis (20%). For the labs, Python is used together with its statistical packages.

Expected learning outcomes

• To understand the problems related to analyse statistically data not distributed normally
• To know the more recent computationally-intensive techniques that can help to describe samples and to infer properties of populations in absence of normality
• To identify situations when the data is on nominal scales so alternative techniques should be use, and act accordingly.
• To be able to run experiment to evaluate hypotheses for situation of scarce data, distributed non normally, on different kinds of scales.

Programming related learning outcomes

• None.

Required background knowledge

Fundamental knowledge of statistics, including parametric tests, (multilinear) regression, logistic regression, and inference.

Prerequisite courses

It is recommended that the students have passed all the courses of a BS in Computer Engineering, Electrical Engineering, Computer Science, or Applied Mathematics, and then have taken specific courses in Empirical Methods.

Detailed topics covered in the course

• Review of basic concepts (Bernoulli and binomial distributions, convergence, statistical hypothesis testing)
• Estimation of the CDF
• Jackknife
• Bootstrap. Bootstrap Confidence Intervals.
• Smoothing: General Concepts
• Nonparametric Regression
• Nonparametric Classification
• Kernels
• Gamma Analysis
• Sequence Analysis

Required computer resources

Students should have laptops. A Mac or Window’s PC capable of running a scientific python development environment.

Evaluation

• Weekly Assignments (10%)
• Mid-term exam (20%)
• Final written exam (30%)
• Final oral exam (35%)
• Participation (5%)