Course Information Package
|Course Unit Title||QUANTITATIVE METHODS|
|Course Unit Code||ACSC124|
|Course Unit Details||BSc Computer Science (Required Courses) -|
|Number of ECTS credits allocated||5|
|Learning Outcomes of the course unit||By the end of the course, the students should be able to:|
|Mode of Delivery||Face-to-face|
|Recommended optional program components||NONE|
Tabular and graphical methods
Statistics in practice. Kinds of data (discrete and continuous, ordinal and nominal). Different kinds of variables. Frequency tables, cumulative distribution tables and graphs (histograms, bar charts, pie charts). Shape of various data distributions (skewed, and symmetric).
Descriptive statistics: Numerical methods
Summarizing quantitative data. Measures of location (mean, mode, and median) and measures of dispersion (variance, standard deviation, range) for group data and raw data. Difference between measures of location and measures of dispersion and their significance. Extreme values, outliers and their importance.
Introduction to Probability
The idea of probability. Experiments, events, outcomes and sample space. Relative frequencies. Calculation of probabilities and basic relationships of probability (union of events, complement of event, intersection of events). Mutually excusive, mutually exhaustive and independent events. Conditional probability and multiplication law.
Discrete Probability Distributions
Probability distribution tables. Theory and their applications in Business problems concerning the discrete probability distributions: Binomial, Poisson. Expected values and variance.
Continuous Probability Distributions
Theory and their applications in Business problems concerning the continuous probability distributions: Normal distribution. Standard normal distribution and table of the standard normal distribution. Applications in Business problems. Discrete versus Continuous distributions.
|Recommended and/or required reading:|
|Planned learning activities and teaching methods|
The course is delivered to the students by means of lectures, and tutorials. Lecture notes are available through the e-learning platform of the University, and the instructor’s webpage. Students are encouraged for class work, problem solving and discussion. Students are also introduced in data analysis using IBM SPSS but under a different course (ARRW101).
|Assessment methods and criteria|
|Language of instruction||English|