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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

Textbook

Reference material

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%)

MSc:AdvancedStatistics old

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