The course will focus on probability distributions, emphasizing classical discrete and continuous distributions, measures of central tendency (mean, median, and mode), variance, and the concept of expected value. Visual tools for data description and distribution visualization will also be introduced.

• Provide a comprehensive understanding of classical probability distributions, both discrete (e.g., binomial, Poisson) and continuous (e.g., normal, exponential).
• Explore fundamental concepts such as expected value, variance, and their interpretation in probabilistic contexts.
• Familiarize participants with visual tools for data description and distribution visualization, including histograms, box plots, and density plots.

• Demonstrate proficiency in identifying and interpreting classical discrete and continuous probability distributions.
• Calculate and interpret measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation).
• Understand the concept of expected value and its importance in probability and data analysis.
• Utilize visual tools to describe and analyze data distributions effectively.
• Apply the concepts learned to practical datasets to gain insights for real-world applications.

The course follows a traditional classroom format with slide presentations, fostering interactive engagement and active participation.