Course Description: Unlock the power of data analysis and decision-making with our intensive “Workshop in Probability and Statistics.” Designed for individuals seeking to strengthen their understanding of statistical concepts and techniques, this workshop provides a hands-on approach to probability theory, descriptive statistics, inferential statistics, and more. Whether you’re a student, researcher, or professional looking to enhance your analytical skills, this workshop will equip you with the knowledge and tools needed to analyze data with confidence.

Course Curriculum:

1. Introduction to Probability
   • Basic concepts of probability theory
   • Probability distributions and their properties
   • Calculating probabilities of events
2. Descriptive Statistics
   • Measures of central tendency: mean, median, mode
   • Measures of dispersion: variance, standard deviation, range
   • Graphical representation of data: histograms, box plots, scatter plots
3. Probability Distributions
   • Discrete probability distributions: binomial, Poisson
   • Continuous probability distributions: normal, exponential
   • Applications of probability distributions in real-world scenarios
4. Sampling and Sampling Distributions
   • Random sampling methods
   • Central Limit Theorem and its implications
   • Sampling distributions of sample statistics
5. Statistical Inference
   • Estimation: point estimation, interval estimation
   • Hypothesis testing: null and alternative hypotheses, significance level, p-value
   • Confidence intervals and their interpretation
6. Correlation and Regression Analysis
   • Understanding the relationship between two variables
   • Pearson correlation coefficient and its properties
   • Simple linear regression analysis: fitting a regression line, interpreting regression coefficients
7. Experimental Design
   • Principles of experimental design
   • Randomized controlled trials vs observational studies
   • Avoiding common pitfalls in experimental design
8. Multivariate Analysis
   • Analysis of variance (ANOVA)
   • Multivariate regression analysis
   • Factor analysis and principal component analysis (PCA)
9. Statistical Software Tools
   • Introduction to statistical software packages (e.g., R, Python, SPSS)
   • Data manipulation, visualization, and analysis using statistical software
   • Hands-on exercises and case studies using real-world datasets
10. Practical Applications and Case Studies
    • Applying statistical techniques to solve real-world problems
    • Case studies from various industries: healthcare, finance, marketing, etc.
    • Collaborative projects and group discussions

Prerequisites: Basic knowledge of mathematics and algebra is recommended. No prior experience with statistics is required.

Duration: This workshop is intensive and may vary in duration depending on the format (e.g., weekend workshop, week-long bootcamp). Please refer to the course schedule for specific timings.

Certification: Upon successful completion of the workshop, you’ll receive a certificate of participation from PMH Edu.