Expected Learning Outcomes

 At the end of  this course, the  students should  be able to:

• appreciate the  importance of  data analysis in  computer science and Information Technology
• Create different kinds of graphs and interpret differences in data distributions via visual display
• evaluate and  compute the  different descriptive measures like central location, variability and other measures of location, and skewness and kurtosis for data in computer science and information technology

• define basic  properties and rules of probability and 
• calculate probability of simple and composite events
• Introduce combinatorial probability, conditional probability, multiplication rule, and Bayes' Theorem
• Define random variables for a given random experiment.
• Describe the  difference between discrete and continuous random variables
• Recall properties of the different discrete probability distributions like Binomial, Hypergeometric, and Poisson distributions
• Calculate descriptive statistics like the mean and standard deviation and probabilities of events using Binomial, Geometric, Negative, Hypergeometric, and Poisson Distributions
• Recall the  properties of the different discrete continuous probability distributions like uniform, normal, and exponential distributions.
• Probabilities of events using uniform, normal, and exponential distributions

• Define the properties of the distribution of the sample mean and sample proportion
• Appreciate the  application of  central limit  theorem
• Estimate population means, proportions for both one-sample using confidence intervals
• Know when to  use and how to  perform the different statistical tools for hypothesis testing for population means and proportions
• Estimate the  linear regression equation and use the linear regression equation to predict values of dependent variables
• Recognize patterns of correlation between variables
• Understand and do multiple regression analysis for a real set of data

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