Applied Statistics

This course introduces students to statistical concepts and methods used to analyze data and make informed decisions in various fields, particularly in computing and information systems. The course covers key topics such as descriptive statistics, probability theory, hypothesis testing, regression analysis, and statistical inference. Students will learn how to apply statistical tools to real-world data sets, interpret the results, and use data-driven insights for problem-solving. Emphasis is placed on the practical application of statistical software to manage and analyze data.

Upon successful completion of this course, students will be able to:

• Understanding Descriptive and Inferential Statistics: Students will gain a solid foundation in both descriptive statistics (mean, median, mode, variance, standard deviation) and inferential statistics, learning how to summarize and interpret data sets effectively.
• Applying Probability Theory to Data Analysis: Learn how to use probability theory to assess risks and make predictions, including understanding probability distributions and calculating probabilities for different types of data.
• Conducting Hypothesis Testing and Regression Analysis: Gain proficiency in conducting hypothesis tests to determine relationships between variables and performing regression analysis to predict outcomes based on data trends.
• Using Statistical Software for Data Analysis: Develop hands-on skills in using statistical software (such as Excel, R, or SPSS) to input, analyze, and visualize data, applying statistical methods to solve practical problems.

These outcomes aim to equip students with the statistical knowledge and analytical skills necessary for data-driven decision-making in fields such as computing, business, and information systems. The course prepares students for roles that require statistical analysis, data management, and quantitative problem-solving.