• Understand the sources and types of uncertainty in decision-making
• Apply data visualization and descriptive statistics to summarize and explore data
• Use probability theory and distributions to model and measure uncertainty
• Ensure data integrity and validity using sampling and hypothesis testing
• Apply decision theory and criteria to compare and select among alternatives
• Identify causal relationships and confounding factors using regression and experimentation
• Forecast future trends and scenarios using time series analysis
• Communicate and persuade your audience using data storytelling

This course is designed for professionals who want to learn how to use data and statistics to make better decisions under uncertainty. It is suitable for professionals from any industry, function, or region who are involved in or responsible for decision-making in their organization or personally.

Day One:

Data Visualization and Descriptive Statistics

• What is data visualization and why is it important for decision-making?
• How can you use charts, graphs, tables, maps, dashboards, and other visual elements to display data effectively?
• What are descriptive statistics and how can they help you summarize and explore data?
• How can you use measures of central tendency, variability, shape, correlation, and association to describe data?
• Case study: How Airbnb used data visualization to understand its market

Day Two:

Quantifying Risk through Probability

• What is probability and why is it useful for decision-making under uncertainty?
• How can you use basic rules of probability, such as addition, multiplication, conditional, Bayes’, and independence to calculate probabilities?
• How can you use probability distributions, such as binomial, normal, Poisson, exponential, uniform, etc., to model uncertain events and outcomes?
• How can you use expected value, variance, standard deviation, coefficient of variation, etc., to measure risk and uncertainty?
• Case study: How Netflix used probability to recommend movies

Day Three:

Data Integrity and Statistical Inference

• What is data integrity and why is it essential for decision-making?
• How can you ensure data quality, accuracy, reliability, completeness, consistency, timeliness, etc., using data cleaning, validation, verification, etc.?
• What is statistical inference and how can it help you make generalizations from data?
• How can you use sampling methods, such as random, stratified, cluster, systematic, etc., to collect representative data from a population?
• How can you use hypothesis testing methods, such as t-test, ANOVA, chi-square test, etc., to test claims or assumptions about a population parameter?
• Case study: How Google used statistical inference to optimize its search engine

Day Four:

Evidence-Based Decisions

• What is evidence-based decision making and why is it important for reducing uncertainty?
• How can you use decision theory and criteria, such as maximin, maximax, minimax regret, expected value, expected utility, etc., to compare and select among alternatives?
• How can you use decision trees, sensitivity analysis, and scenario analysis to visualize and evaluate complex decisions?
• How can you use Bayesian analysis and updating to incorporate new information and revise your beliefs?
• Case study: How Amazon used evidence-based decisions to launch its Prime service

Day Five:

Understanding the Causes of Things

• What is causality and why does it matter for decision-making?
• How can you use regression analysis, such as linear, logistic, multiple, etc., to model and estimate the relationship between variables?
• How can you use experimentation methods, such as randomized controlled trials (RCTs), A/B testing, factorial design, etc., to test and measure the causal effect of an intervention?
• How can you deal with confounding factors, such as selection bias, omitted variable bias, reverse causality, etc., that may affect your causal inference?
• Case study: How Facebook used causality and experimentation to improve its news feed

To enhance learning and practical application of concepts, the training course will use a combination of interactive lectures, case studies, group discussions, practical exercises, and real-world examples. Participants will also get the chance to collaborate on group projects and create action plans adapted to the needs of their respective organizations.