Data Science

Quantitative methods of data processing

  • Class 15
  • Practice 30
  • Independent work 120
Total 165

Course title

Quantitative methods of data processing

Lecture type


Course code






Lecturers and associates

Course objectives

The objective of the course is to enable students to independently apply quantitative analysis and modeling. Students will acquire theoretical and practical knowledge of quantitative methods, recognize certain types of problems and choose the appropriate method of analysis and modeling. It is especially important through examples and exercises to develop the skill of modeling realistic problems.


Introduction to quantitative methods. Descriptive statistics. Correlation coefficients. Linear regression. Probability distributions. Estimating parameters of distribution. Confidence intervals. Statistical hypothesis testing. Contingency table analysis.

Required reading

Course handbook prepared and printed by Algebra University College

Minimal learning outcomes

  • Choose, interpret and determine the basic statistical measures: measures of central tendency and dispersion in terms of applicability, interpretation and usefulness for different business problems
  • Choose and interpret the basic aspects of correlation and regression analysis.
  • Choose, interpret and determine confidence intervals, and choose, interpret, and apply parametric tests.
  • Choose and interpret the basic aspects of the analysis of contingency tables.

Preferred learning outcomes

  • Choose, apply and critically interpret appropriate measures of descriptive statistics, graphical methods, and estimate parameters of distribution.
  • Select, measure and critically interpret aspects of correlation and regression analysis: parameter estimation, standardized coefficients, significance, predictiveness and residual analysis.
  • Choose, critically interpret and determine confidence intervals, and choose, interpret and apply parametric statistical tests and post-hoc tests.
  • Choose and interpret the aspects of contingency table analysis using graphical methods and statistical tests.
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