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= Metrics and Empirical Methods for Software Engineers and Data Scientists_old =

* <span>'''Course name:'''</span> Metrics and Empirical Methods for Software Engineers and Data Scientists
* <span>'''Course number:'''</span> SE-06
* <span>'''Area of instruction:'''</span> Computer Science and Engineering

== Administrative details ==

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

== Course outline ==

The course aims at presenting the fundamentals of metrics and empirical methods to the future software engineers and data scientists, on one side providing the scientific fundamentals of the disciplines, and on the other anchoring the theoretical concepts on practices coming from the world of software development and engineering. As a side product, the course also refreshes the fundamentals of statistics, providing the basis for more advanced statistical courses in the following semester(s) of study.

== Expected learning outcomes ==

* To master the fundamentals of statistics and empirical sciences
* To understand how to perform an experiment using measures of different kinds and on different scales
* To know the fundamental measures in software development
* To appreciate the value of different kinds of measures, also coming from biometrics sensors
* To be able to develop a comprehensive metrics plan for software organizations linking business goals to actual practices

== Programming related learning outcomes ==

* None.

== Required background knowledge ==

Fundamentals of statistics and of software development.

== Prerequisite courses ==

None

== Detailed topics covered in the course ==

* Review of the fundamentals of descriptive and hypothesis testing
* Inferential statistics, convolution, correlation, linear and logistic regression
* Experimentation and quasi experimentation
* Suitable parametric and non parametric statistical tests
* Fundamental of measurements, measurement scales, kinds of measurements, the representational theory of measurement
* Fundamental of software measures, axiomatic approaches, GQM(+)
* Object oriented metrics and other advanced metrics
* Biometrics in software development
* Non invasive measurements
* Statistical meta-analysis

== Textbook ==

* Handouts supplied by the instructor
* 
* 
* 

== Reference material ==

* 
* 

== Required computer resources ==

A laptop.

== Evaluation ==

* Weekly quizzes (20%)
* Project (40%)
* Final oral (40%)