People Analytics Analyst — Statistical Analysis Fundamentals

Description: This day focuses on advanced regression modeling techniques. Topics include polynomial regression, interaction terms, model diagnostics (multicollinearity, heteroscedasticity, influential points), and regularization methods (Lasso, Ridge, Elastic Net). Specific resources or activities: Read chapters from "Applied Linear Statistical Models" by Kutner, Nachtsheim, Neter, and Li. Build and evaluate regression models in R or Python using lm, glm, statsmodels, or scikit-learn. Analyze real-world datasets and interpret the results, paying particular attention to model assumptions and diagnostics. Expected outcomes: Confidently build, interpret, and diagnose complex regression models, including the use of regularization techniques. Identify and address violations of model assumptions.

Description: Learn the principles of time series analysis and its relevance to people analytics. Focus on stationarity, autocorrelation, ARIMA models, and forecasting employee-related metrics such as attrition, performance, and absenteeism. Specific resources or activities: Study "Time Series Analysis: Forecasting and Control" by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. Use R or Python libraries like forecast or statsmodels.tsa.arima.model to build and evaluate time series models on sample people analytics datasets. Experiment with different forecasting methods and evaluate their performance. Expected outcomes: Comprehend the basics of time series analysis and its application to people analytics data. Build and interpret ARIMA models and generate forecasts.

Description: Explore the fundamentals of Bayesian statistics and its application to people analytics. Learn about prior distributions, posterior distributions, Bayesian inference, and Markov Chain Monte Carlo (MCMC) methods. Specific resources or activities: Read chapters from "Doing Bayesian Data Analysis" by John K. Kruschke. Implement Bayesian models in R using packages like rstan, brms, or Python using libraries such as PyMC3 or bambi. Work on case studies involving Bayesian hypothesis testing and model comparison. Expected outcomes: Understand the principles of Bayesian inference and implement Bayesian models for people analytics problems. Be able to interpret posterior distributions and make probabilistic inferences.

Description: This day covers causal inference methods to understand the causal relationships between variables. Explore concepts like potential outcomes, counterfactual analysis, causal diagrams (directed acyclic graphs, DAGs), and methods like propensity score matching, inverse probability of treatment weighting (IPTW), and instrumental variables. Specific resources or activities: Study "Causal Inference in Statistics: A Primer" by Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell. Practice implementing causal inference techniques using R or Python with libraries like CausalML, dowhy, or causalimpact. Analyze real-world datasets and interpret causal effects. Expected outcomes: Understand the key concepts of causal inference and their importance in people analytics. Apply causal inference techniques to identify causal relationships and evaluate the impact of interventions.

Description: Focus on creating advanced and compelling data visualizations to communicate statistical insights effectively. Explore different chart types, interactive dashboards, and storytelling techniques. Specific resources or activities: Read "Storytelling with Data" by Cole Nussbaumer Knaflic. Use data visualization tools like Tableau, Power BI, or Python's matplotlib and seaborn libraries to create interactive dashboards and compelling visualizations. Practice communicating statistical findings through visual storytelling. Experiment with different chart types and design principles. Expected outcomes: Effectively communicate statistical findings through advanced data visualization techniques. Create interactive dashboards and tell compelling stories with data.

Description: The final day is dedicated to a hands-on project applying the statistical techniques learned throughout the week. Choose a real-world people analytics problem, gather data (or use a publicly available dataset), formulate a research question, analyze the data using the learned techniques, and present the findings. Specific resources or activities: Work independently or in a small group. Use any of the resources or techniques covered during the week. Document the entire process, including data cleaning, analysis, and visualization. Create a presentation or report summarizing the findings and conclusions. Expected outcomes: Demonstrate the ability to apply advanced statistical techniques to a real-world people analytics problem. Communicate findings effectively and draw actionable insights. Solidify understanding through practical application.