Data Analysis and Decision Making

In this module, we will discuss probability, which is fundamentally important in all aspects of business. You may think that you are new to the concept of probability, but you actually make decisions based on probability every day. Any time you think to yourself, "What are the odds..." you are considering the probability that something will occur. In this module, we're going to talk about how to calculate probability more precisely so you can make better decisions based on those calculations.

This module will equip you to:

• Identify the sources and role of randomness in samples.
• Calculate expected value and variance of random variables and understand their significance in the context of business problems.
• Use Excel to make probability and percentile calculations.

In this module, we will discuss the concept of descriptive statistics and what they are used for. We will also talk about data visualizations and how they can be used to communicate the meaning behind data.

This module will equip you to:

• Generate and interpret key statistics and graphs to make sense of business data.
• Create cross-tabulations (pivot charts) in Excel.
• Add trend lines to scatter plots to estimate relationships between variables.
• Generate a variety of graphs using the Tableau tool.

In this module, we will discuss the concept of sampling distribution and construct confidence interval estimation for the means and proportions. Then we will examine how to use hypothesis testing to make statistical inferences on a population parameter.

This module will equip you to:

• Apply the concept of sampling distribution to understand confidence interval.
• Generate and interpret confidence intervals.
• Apply confidence intervals to generate business insights.
• Apply the Central Limit Theorem.
• Formulate hypothesis tests and evaluate p-values.
• Perform statistical tests on means and proportions.
• Perform statistical tests on correlations. 

In this module, you will explore linear regression, one of the most widely used prediction methods.

This module will equip you to:

• Identify both simple and multiple linear regression models.
• Fit linear regression models to given data sets and determine which regression line best fits the data.
• Build and evaluate complex linear regression models using both numerical and categorical variables.  
• Interpret the output of regression models and to use them for prediction.

In this module, we take a deeper dive into linear regression. We will expand the regression analysis we started in the previous module by looking at the purpose of our regression modeling. Is it an explanation or prediction and how that may affect our modeling decisions? We will discuss multiple other topics, including p-values, outliers, residual plots, multicollinearity, and lurking variables.  

This module will equip you to:

• Identify how the goal of analysis will influence modeling decisions and how models are evaluated.
• Analyze large datasets with an awareness of error measures, p-values, and multicollinearity.
• Identify outliers and lurking variables.
• Apply variable selections to complex linear regression models.