Process Improvement Using Data

Visualization, Statistics, DOE & Monitoring

What you’ll learn
Analyze data using visualization, statistics, and regression to diagnose process issues and identify evidence‑based improvement opportunities.
Apply control charts and capability measures to evaluate stability, track improvements, and support ongoing data‑driven process decisions.
Create effective time series, bar, box, and scatter plots to communicate insights.
Understand univariate statistics, including normal distributions, outliers, and robust methods.
Build and interpret confidence intervals and paired tests for practical decision-making.
Apply least squares regression, ANOVA, and assumption checks to real-world problems.
Use multiple linear regression (including categorical variables) to model complex relationships.
Design, run, and analyze experiments (2-factor, multi-factor, fractional factorials).
Understand aliasing, blocking, randomization, and covariates in experimental setups.
Optimize outcomes with Response Surface Methods (RSM) and contour plots.
Monitor processes using control charts and interpret Type I & II errors.
Calculate and apply process capability (Cp/Cpk) for performance benchmarking.
Plan data-driven improvements that maximize impact and minimize risk.
Translate rigorous statistical methods into actionable insights for engineering, operations, and programs.

Requirements
Basic comfort with spreadsheets (Excel/Google Sheets) or statistical software.
High school level algebra and statistics (mean, variance, distributions).
Curiosity and willingness to test ideas and iterate.

Description
Process Improvement Using Data is a complete, hands-on journey through the methods that professionals use to diagnose issues, improve systems, and measure results. Whether you’re in engineering, manufacturing, public health, or NGO/program operations, you’ll learn practical techniques to turn messy data into clear decisions.You’ll start by mastering data visualization and univariate analysis, then progress into least squares regression and ANOVA to model the drivers behind performance. The course dives deep into Design of Experiments (DOE)—from two-factor designs to fractional factorials, aliasing, blocking, and randomization—so you can plan efficient experiments that reveal what truly matters. Finally, you’ll learn process monitoring and capability analysis, ensuring your improvements stick and your systems remain stable.This is practical statistics for improvement, not theory for theory’s sake. Every concept is connected to real analysis workflows and decision-making.This course includes9.5 hours on-demand video64 lectures Downloadable resources associated with lecturesPractical case studies and exercisesLifetime accessCertificate of completionOutcomes & Career ImpactBy the end of the course, you’ll be able to:Diagnose problems quickly using the right analysis and visuals.Quantify uncertainty and make confident decisions with statistical rigor.Plan efficient experiments that reduce costs and reveal true drivers.Monitor performance and ensure sustained improvement with SPC.Present clear, compelling insights to stakeholders and leadership.

Who this course is for
Process Engineers & Operations Managers seeking structured improvement methods.
Data Analysts & BI Practitioners wanting to connect analysis to process change.
Quality & Six Sigma professionals applying DOE and SPC in production.
Program Managers (NGO/Public Sector) improving service delivery using data.
Researchers & Lab Teams optimizing experiments efficiently.
Students & Early-career professionals building a strong quantitative toolkit.

https://www.udemy.com/course/process-improvement-using-data/