IT / Databases / Oracle / Oracle 19c / Statistics using SQL
| ID | 2757811 |
|---|---|
| Classroom | 3 days 9:00-16:30 |
| Webinar | 5 days 9:00-12:30 |
| Method | Lecture with examples and exercises. |
| Prequisite | Oracle SQL, PL / SQL |
| Audience | Business Intelligence Developer |
Oracle developers, and marketing/controlling professionals who have direct access to the Oracle database using SQL or PL/SQL can perform statistical analysis for descriptive statistics and inferential statistics using SQL queries and PL/SQL procedures and PL/SQL functions. This course presents you the numerous functions that are available directly in the Oracle database by making heavy use of scripting examples. The statistical concepts of central tendency, dispersion, correlation and regression, and statistical testing for distribution tests, contingency analysis and the analysis of variance (ANOVA) are also a part of this training.
Central tendency: Frequencies using COUNT, mode using STATS_MODE, mean values ??using AVG, MEDIAN - quantiles using PERCENTILE_CONT and PERCENTILE_DISC - Measures of dispersion: range using MIN and MAX, standard deviation using STDDEV, STDDEV_POP and STDDEV_SAMP, variance using VAR_POP, VAR_SAMP and VARIANCE - Rank and distribution using CUME_DIST, DENSE_RANK, RANK, and PERCENT_RANK
Covariance using COVAR_POP and COVAR_SAMP - correlation using CORR (Bravais-Pearson) - rank correlation using CORR_S (Spearman's rho) and CORR_K (Kendall's tau)
Linear regression and the least squares method - linear equation derived using REGR_SLOPE and REGR_INTERCEPT - coefficient of determination using REGR_R2 - averages using REGR_AVGX and REGR_AVGY - model check using REGR_COUNT, REGR_SXX, REGR_SYY and REGR_SXY - prediction and residual analysis
contingency and categorical variables - Chi-Square test using CHISQ_OBS and CHISQ_DF - significance using CHISQ_SIG - Contingency: Phi Coefficient using PHI_COEFFICIENT, Cramer's V using CRAMERS_V, Contingency Coefficient using CONT_COEFFICIENT and Cohen's Kappa using COHENS_K
Overview of probability theory - introduction to test theory - t-test using STATS_T_TEST_ONE (one sample), STATS_T_TEST_PAIRED (two samples), STATS_T_TEST_INDEP (two independent samples) and STATS_T_TEST_INDEPU (two independent samples with unequal variance) - variance comparison using STATS_F_TEST - distribution tests using STATS_BINOMIAL_TEST - Mann-Whitney test using STATS_MW_TEST - Kolmogorov-Smirnov function using STATS_KS_TEST - Wilcoxon signed ranks using STATS_WSR_TEST
Analysis of Variance - ANOVA performed using STATS_ONE_WAY_ANOVA: Sum of Squares using SUM_SQUARES_BETEEN and SUM_SQUARES_WITHIN, mean squares using MEAN_SQUARES_BETWEEN and MEAN_SQUARES_WITHIN, F-value using F_RATIO and significance using SIG
Fundamentals of time series analysis: Components, stationarity, autocorrelation, autocovariance, periodicity - Smoothing: moving average, exponential smoothing - Trend calculations using linear regression - seasonal decomposition and residual analysis
Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.
He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining. He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.
He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.
