Chapter 1: Swift Primer
- Introduction to Swift and its pros when working with large data sets
- Provided data sets and how to load them using the Decodable protocol- Higher-Order Functions (map, filter, reduce, apply)
Chapter 2: Introduction to Probability and Random Variables
- What is a random variable?
- Sample spaces
- Laws and axioms of probability
- Variable Independence
- Conditional probability
Chapter 3: Distributions and Random Numbers
- Mass and density functions
- Discrete distributions
- Discrete uniform distribution
- Bernoulli trials
- Binomial distribution- Poisson distribution
- Continuous distributions
- Continuous uniform distribution
- Exponential distribution
- Normal distribution
- Implement a random number generator that samples from a given distribution
Chapter 4: Predicting House Sale Prices with Linear Regression
- Central tendency measures
- Variance measures- Association measures
- Stratification of data
- Linear regression
Chapter 5: Hypothesis Testing
- T Testing- Null and Alternative Hypotheses
- P-value
- Determining sample sizes
Chapter 6: Data Compression Using Statistical Methods
- Measurement scales
- Calculate the distribution of example data
- Compute a Huffman Tree
- Encode the original data in a smaller package
- &nb 
