CSPB 3022 - Introduction to Data Science with Probability and Statistics

• Recognize the importance of data collection, identify limitations in data collection methods and other sources of statistical bias, and determine their implications and how they affect the scope of inference. 
• Use statistical software to summarize data numerically and visually, and to perform data analysis. 
• Have a conceptual understanding of the unified nature of statistical inference. 
• Apply estimation and testing methods to analyze single variables or the relationship between two variables in order to understand natural phenomena and make data-based decisions. 
• Model numerical response variables using a single explanatory variable or multiple explanatory variables in order to investigate relationships between variables. 
• Interpret results correctly, effectively, and in context without relying on statistical jargon. 
• Critique data-based claims and evaluate data-based decisions. 

• Data Exploration and Probability
• Conditional probability and Bayes rule
• Discrete/continuous random variables and computing with distributions
• Joint distributions, covariance, correlation and sums of random variables
• Using Jupyter python environment
• Python tools for data science – NumPy and Pandas
• Basic statistical estimation, random samples, bootstrap and resampling techniques, unbiased estimators and confidence intervals for measure data
• T-Test
• Linear Regression and classification
• Maximum likelihood estimation and analysis of variance

Counting theory, Probabilities, Integration