SylabUZ
Nazwa przedmiotu | Fundamentals of business analytics |
Kod przedmiotu | 04.2-WE-BizElP-PodAnalBiz-Er |
Wydział | Wydział Informatyki, Elektrotechniki i Automatyki |
Kierunek | Biznes elektroniczny |
Profil | praktyczny |
Rodzaj studiów | Program Erasmus pierwszego stopnia |
Semestr rozpoczęcia | semestr zimowy 2019/2020 |
Semestr | 2 |
Liczba punktów ECTS do zdobycia | 5 |
Typ przedmiotu | obowiązkowy |
Język nauczania | angielski |
Sylabus opracował |
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Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
Wykład | 30 | 2 | - | - | Egzamin |
Laboratorium | 30 | 2 | - | - | Zaliczenie na ocenę |
Data analysis fundamentals
Statistical software for business analytics. Elements of using JMP software: data tables; graphs; reports; scripts; formula editor; simulation techniques; descriptive statistics and statistical inference. Elements of using the SAS system: elements of the language; data step; data processing; proc step; global expressions; graphics; basic statistical procedures; debugging. Using Enterprise Guide. Fundamentals of using the R system: elements of R language, programming, data processing and visualization.
Analysis of dependence between quantitative variables. Linear regression model. Properties of least-squares estimators. Regression model diagnostics. Outliers, leverage points and influential observations. Transformations to achieve linearity. Logistic regression.
Analysis of variance. One-way analysis. F-test for ANOVA. Relations to regression analysis. Multiple comparisons. Two-way analysis.
Analysis for qualitative variables. Testing hypotheses for one variable. Testing uniformity. Testing independence for two random variables.
Random sampling from a finite population. Representative method. Estimators of population parameters for various sampling schemes.
Monte Carlo method. Generation of pseudo-random numbers. Estimation of distribution parameters using the Monte Carlo method. Permutation tests. Bootstrap method.
Rank methods. Comparison of feature distribitions in two populations. Tests for pairwise comparisons. Rank tests for independence. Comparison of feature distributions in many populations. Rank methods for linear regression.
Dimensionality reduction methods. Principal component analysis. Factor analysis. Components defined by the user.
Time series analysis and forecasting. Aggregation and interpolation of time series. Exponential smoothing without seasonality. Confidence intervals for the forecasts. Exponential smoothing in forecasting for time series with seasonality. Exponential smoothing vs, parametric models AR, MA, ARMA, ARIMA. Models with hidden components.
Lecture - conventional lecture.
Labs - laboratory exercises using SAS software.
Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
Zmodyfikowane przez prof. dr hab. inż. Dariusz Uciński (ostatnia modyfikacja: 11-12-2019 22:34)