Statistics
General
Code: BSC_IT11
Language: English
Delivery: In person
Prerequisites:
No formal prerequisites. Basic knowledge of mathematics is recommended.
Familiarity with Discrete Mathematics is recommended.
Workload
- Lectures: 39.0 hours
- Lab: 13.0 hours
- Study: 55.0 hours
- Project: 43.0 hours
Course Content
Probability theory (events, conditional probability, independence)
Random variables (discrete and continuous)
Expectation and variance
Common distributions (Binomial, Normal, Poisson)
Sampling and Central Limit Theorem
Statistical inference (estimation, confidence intervals)
Hypothesis testing
Regression and correlation
Introduction to data analysis using computational tools (e.g., R and./or Python)
Learning Outcomes
Upon successful completion of the course, students will be able to:
Apply probability theory to computational problems
Analyze discrete and continuous random variables
Identify and use common probability distributions
Apply statistical inference methods
Conduct hypothesis testing and interpret results
Develop and evaluate basic regression models
Analyze datasets using statistical and computational tools
Skills
Analytical and critical thinking
Quantitative reasoning and problem-solving
Data analysis and interpretation skills
Ability to handle uncertainty and variability
Decision-making based on data and statistical evidence
Computational and statistical thinking
Ability to work with real-world datasets
Use of digital tools for data analysis
Independent and collaborative learning skills
Communication of quantitative results