Why?
Statistics is at the core of data science. It is the logic, rules, & techniques. It is the lens we see and interpret data through it. It provides methods to collect, analyze, interpret, and present data effectively. Understanding statistical concepts is crucial for making data-driven decisions, identifying patterns, and drawing reliable conclusions from data - all essential skills for a data scientist.
What?
This introductory course covers the fundamental concepts of statistics, from basic descriptive methods to probability theory then some inferential techniques. Students will learn how to summarize data, understand probability distributions, estimate parameters, and conduct hypothesis tests, building a strong foundation for more advanced statistical analysis.
Curriculum:
What is Statistics
Why statistics matters, types of statistics, levels of measurement and variables.
Describing Data: Frequency Tables and Graphs
Frequency tables, frequency distributions and graphic presentation of data.
Describing Data: Numerical Measures
Mean, median, mode, dispersion, and measures of position.
Describing Data: Displaying and Exploring Data
Dot plots, quartiles, box plots, skewness and describing relationships between variables.
A Survey of Probability Concepts
Probability definitions, rules of addition and multiplication, contingency tables, Bayes' theorem and counting principles.
Notes
Some of the concepts will seem useless practically -and that may be right- however they still play a key role in understanding more complex concepts and proofs in more advanced topics in statistics and machine learning. For example, understanding distributions will help you understand Linear Regression (in Stat 3), and afterwards several Machine Learning models that are based on maximum likelihood estimation.