10.90.136.11: Created page with "= Advanced Statistics = * '''Course name:''' Advanced Statistics * '''Course number:''' DS-03 * '''Area of instruction:''' Math == Adm..."

2021-07-30T11:06:52Z

Created page with "= Advanced Statistics = * '''Course name:''' Advanced Statistics * '''Course number:''' DS-03 * '''Area of instruction:''' Math == Adm..."

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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%)

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10.90.136.11: Created page with "= Advanced Statistics = * '''Course name:''' Advanced Statistics * '''Course number:''' DS-03 * '''Area of instruction:''' Math == Adm..."