ZUT - Krajowe Ramy Kwalifikacji / Rok 2018/2019 / Administracja Centralna Uczelni / Wymiana międzynarodowa (S2) / Sylabus przedmiotu - Statistics and Data Handling for Industry and Research

Administracja Centralna Uczelni - Wymiana międzynarodowa (S2)

Sylabus przedmiotu Statistics and Data Handling for Industry and Research:

Informacje podstawowe

Kierunek studiów	Wymiana międzynarodowa
Forma studiów	studia stacjonarne	Poziom	drugiego stopnia
Tytuł zawodowy absolwenta
Obszary studiów	—
Profil
Moduł	—
Przedmiot	Statistics and Data Handling for Industry and Research
Specjalność	przedmiot wspólny
Jednostka prowadząca	Katedra Konstrukcji, Mechaniki i Technologii Okrętów
Nauczyciel odpowiedzialny	Zbigniew Sekulski <Zbigniew.Sekulski@zut.edu.pl>
Inni nauczyciele
ECTS (planowane)	6,0	ECTS (formy)	6,0
Forma zaliczenia	zaliczenie	Język	angielski
Blok obieralny	—	Grupa obieralna	—

Formy dydaktyczne

Forma dydaktyczna	KOD	Semestr	Godziny	ECTS	Waga	Zaliczenie
wykłady	W	1	30	3,0	0,50	zaliczenie
ćwiczenia audytoryjne	A	1	30	3,0	0,50	zaliczenie

Wymagania wstępne

KOD	Wymaganie wstępne
W-1	Students should be comfortable with math at the level of elementary algebra (e.g., equation of a straight line, plotting points, taking powers & roots, percentages).

Cele przedmiotu

KOD	Cel modułu/przedmiotu
C-1	Through hands-on experience with real data from a wide variety of applications, students will learn basic methods required for statistical data analysis and interpretation. The emphasis will be on formulating questions, choosing appropriate statistical techniques for a given problem, verifying whether the assumptions behind the techniques are met by the dataset, drawing appropriate conclusions from the analysis, and communicating the results.

KOD

Cel modułu/przedmiotu

C-1

Through hands-on experience with real data from a wide variety of applications, students will learn basic methods required for statistical data analysis and interpretation. The emphasis will be on formulating questions, choosing appropriate statistical techniques for a given problem, verifying whether the assumptions behind the techniques are met by the dataset, drawing appropriate conclusions from the analysis, and communicating the results.

Treści programowe z podziałem na formy zajęć

KOD	Treść programowa	Godziny
ćwiczenia audytoryjne
T-A-1	Histograms; expected values; variance; skewness.	2
T-A-2	Random number generators.	2
T-A-3	Illustrating the Central Limit Theorem.	4
T-A-4	2-D random variable; covariance and correlation sample and population estimators.	4
T-A-5	Interval estimation.	4
T-A-6	Statistical hypotheses – mean, variance, standard deviation, correlation.	4
T-A-7	Linear regression.	4
T-A-8	Analysis of variance (ANOVA).	4
T-A-9	Evaluation of skills.	2
		30
wykłady
T-W-1	Preliminaries: Statistics and Data Analysis, introduction to combinatorics.	2
T-W-2	Probabilities – an introduction: chance event (definition, examples), Venn diagrams, axiomatic definition of probability, conditional probability, Bayes theorem.	2
T-W-3	Random variable and its characteristics: distribution function (probability (density) function; cumulative distribution function), random variable (RV) of the discrete type (examples), random variable of the continuous (examples), basic parameters of a random variable (expected value – the basic idea and formulae moments and central moments, expected value of RV – E(X), variance as the expected value of the square of deviation of the RV from E(X) higher moments: skewness, kurtosis of the RV), descriptive statistics (quantiles, “box-with-whiskers”), two-dimensional random variable, n-dimensional random variable, covariance and correlation; covariance matrix.	6
T-W-4	Some most popular distributions of random variables (RVs) of the discrete type: one-point (single-valued) RVs, two-point (two-valued) RVs, Bernoulli (binomial) distribution, multinomial distribution, Poisson distribution.	2
T-W-5	Some most popular distributions of random variables of the continuous type: the holy grail of statistics – the Normal distribution, the exponential distribution (its connection with the Poisson distribution), the 2-d Normal distribution, central limit theorem (examples), Chebyshev inequality and the law of large numbers (practical applications), other important distributions (Student’s t distribution; chi-square distribution; Snedecor’s f distribution; gamma distribution; Weibull distribution), putting statistics into work, statistical sample and statistical population, estimator – the general idea and properties, point estimators – fundamental formulae, the sample arithmetic average (arithmetic mean), the sample mean square deviation from the mean, the sample correlation coefficient (for a 2-d RVs), three sigma rule.	6
T-W-6	Interval estimation and confidence intervals: confidence intervals for the mean, confidence intervals for the variance, question of the adequate sample size.	2
T-W-7	Parametric statistical tests – basic ideas: tests concerning the expected value of RV, testing the equality of expected values of 2 RV’s, testing the equality of variances.	2
T-W-8	Non-parametric statistical tests: type of distribution tests, non-parametric tests – test of independence using the Pearson’s test.	2
T-W-9	Linear regression (and correlation): method of least squares, the simple regression model, the multiple linear regression – an outline, testing the correation coefficient.	2
T-W-10	Analysis of variance (ANOVA): the single-factor ANOVA model, confidence intervals for the treatment means, contrasts problems, random effects model.	2
T-W-11	Knowledge evaluation.	2
		30

Obciążenie pracą studenta - formy aktywności

KOD	Forma aktywności	Godziny
ćwiczenia audytoryjne
A-A-1	Participation in classes.	30
A-A-2	Preparation of homeworks.	60
		90
wykłady
A-W-1	Participation in the classess.	30
A-W-2	Own study of the literature.	50
A-W-3	Preparation to the knowledge evaluation.	10
		90

Metody nauczania / narzędzia dydaktyczne

dla tego przedmiotu nie są określone metody nauczania ani narzędzia dydaktyczne

Sposoby oceny

dla tego przedmiotu nie są określone sposoby oceny

Zamierzone efekty kształcenia - wiedza

Zamierzone efekty kształcenia	Odniesienie do efektów kształcenia dla kierunku studiów	Odniesienie do efektów zdefiniowanych dla obszaru kształcenia	Cel przedmiotu	Treści programowe	Metody nauczania	Sposób oceny
WM-WTMiT_2-_??_W01 The course gives students fundamentals to master statistical analysis.	—	—	—	—	—	—

Zamierzone efekty kształcenia - umiejętności

Zamierzone efekty kształcenia	Odniesienie do efektów kształcenia dla kierunku studiów	Odniesienie do efektów zdefiniowanych dla obszaru kształcenia	Cel przedmiotu	Treści programowe	Metody nauczania	Sposób oceny
WM-WTMiT_2-_??_U01 The ability to use the acquired knowledge to solve practical problems.	—	—	—	—	—	—

Zamierzone efekty kształcenia - inne kompetencje społeczne i personalne

Zamierzone efekty kształcenia	Odniesienie do efektów kształcenia dla kierunku studiów	Odniesienie do efektów zdefiniowanych dla obszaru kształcenia	Cel przedmiotu	Treści programowe	Metody nauczania	Sposób oceny
WM-WTMiT_2-_??_K01 Improvement of social and personal competencies including self-awareness, self-management, social awareness, relationship skills, responsible decision-making and others. Encouraging dialogue and mutual respect between peoples of different nations, cultures and faiths.	—	—	—	—	—	—

Treści programowe - ćwiczenia audytoryjne

KOD	Treść programowa	Godziny
T-A-1	Histograms; expected values; variance; skewness.	2
T-A-2	Random number generators.	2
T-A-3	Illustrating the Central Limit Theorem.	4
T-A-4	2-D random variable; covariance and correlation sample and population estimators.	4
T-A-5	Interval estimation.	4
T-A-6	Statistical hypotheses – mean, variance, standard deviation, correlation.	4
T-A-7	Linear regression.	4
T-A-8	Analysis of variance (ANOVA).	4
T-A-9	Evaluation of skills.	2
		30

Treści programowe - wykłady

KOD	Treść programowa	Godziny
T-W-1	Preliminaries: Statistics and Data Analysis, introduction to combinatorics.	2
T-W-2	Probabilities – an introduction: chance event (definition, examples), Venn diagrams, axiomatic definition of probability, conditional probability, Bayes theorem.	2
T-W-3	Random variable and its characteristics: distribution function (probability (density) function; cumulative distribution function), random variable (RV) of the discrete type (examples), random variable of the continuous (examples), basic parameters of a random variable (expected value – the basic idea and formulae moments and central moments, expected value of RV – E(X), variance as the expected value of the square of deviation of the RV from E(X) higher moments: skewness, kurtosis of the RV), descriptive statistics (quantiles, “box-with-whiskers”), two-dimensional random variable, n-dimensional random variable, covariance and correlation; covariance matrix.	6
T-W-4	Some most popular distributions of random variables (RVs) of the discrete type: one-point (single-valued) RVs, two-point (two-valued) RVs, Bernoulli (binomial) distribution, multinomial distribution, Poisson distribution.	2
T-W-5	Some most popular distributions of random variables of the continuous type: the holy grail of statistics – the Normal distribution, the exponential distribution (its connection with the Poisson distribution), the 2-d Normal distribution, central limit theorem (examples), Chebyshev inequality and the law of large numbers (practical applications), other important distributions (Student’s t distribution; chi-square distribution; Snedecor’s f distribution; gamma distribution; Weibull distribution), putting statistics into work, statistical sample and statistical population, estimator – the general idea and properties, point estimators – fundamental formulae, the sample arithmetic average (arithmetic mean), the sample mean square deviation from the mean, the sample correlation coefficient (for a 2-d RVs), three sigma rule.	6
T-W-6	Interval estimation and confidence intervals: confidence intervals for the mean, confidence intervals for the variance, question of the adequate sample size.	2
T-W-7	Parametric statistical tests – basic ideas: tests concerning the expected value of RV, testing the equality of expected values of 2 RV’s, testing the equality of variances.	2
T-W-8	Non-parametric statistical tests: type of distribution tests, non-parametric tests – test of independence using the Pearson’s test.	2
T-W-9	Linear regression (and correlation): method of least squares, the simple regression model, the multiple linear regression – an outline, testing the correation coefficient.	2
T-W-10	Analysis of variance (ANOVA): the single-factor ANOVA model, confidence intervals for the treatment means, contrasts problems, random effects model.	2
T-W-11	Knowledge evaluation.	2
		30

Formy aktywności - ćwiczenia audytoryjne

KOD	Forma aktywności	Godziny
A-A-1	Participation in classes.	30
A-A-2	Preparation of homeworks.	60
		90

(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KOD	Forma aktywności	Godziny
A-W-1	Participation in the classess.	30
A-W-2	Own study of the literature.	50
A-W-3	Preparation to the knowledge evaluation.	10
		90

(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Pole	KOD	Znaczenie kodu
Zamierzone efekty kształcenia	WM-WTMiT_2-_??_W01	The course gives students fundamentals to master statistical analysis.

Pole	KOD	Znaczenie kodu
Zamierzone efekty kształcenia	WM-WTMiT_2-_??_U01	The ability to use the acquired knowledge to solve practical problems.

Pole	KOD	Znaczenie kodu
Zamierzone efekty kształcenia	WM-WTMiT_2-_??_K01	Improvement of social and personal competencies including self-awareness, self-management, social awareness, relationship skills, responsible decision-making and others. Encouraging dialogue and mutual respect between peoples of different nations, cultures and faiths.