Statistics Introduction
Descriptive Statistics
Graphing Distributions
Summarizing Distributions
The Binomial Distribution
The Normal Distribution
Sampling Distributions and the Central Limit Theorem
Confidence Intervals
Hypothesis Tests
The t-Distribution
Correlation and Regression

Learner Outcomes
Learn and understand statistical logic, critical thinking, and language skills needed for successful data analytics using data science in decision making
Apply the language of statistics while learning how to calculate types of data to correctly use statistical measurements and conclude certain assumptions that relate to decision making
Calculate the basic concepts of probability to increase the likelihood of sound decision making, including descriptive versus inferential statistics
Describe samples, as a subset of a statistical population, to improve probability equality and results
Communicate how to manage variables and to plot different types of data, such as a frequency distribution
Comprehend different types of distributions to data analytics and management roles, including an assessment of data quality
Synthesize Data-Driven Decision Making (DDDM) concepts using facts, metrics, and data to guide strategic business decisions that align with leadership and analyst goals, objectives and initiatives