Quality Analytics with Python Training Course

Course Overview

Learning Objectives

• Explain the role of Python-based analytics in quality management and process improvement.
• Prepare, clean, validate, and organize quality datasets using Python.
• Use pandas and NumPy to automate data processing and statistical calculations.
• Calculate descriptive statistics and identify patterns in quality performance.
• Create visualizations that reveal defects, variation, and process trends.
• Apply statistical tests to investigate differences in quality performance.
• Calculate and interpret process capability indices, including Cp and Cpk, where applicable.
• Develop control charts and assess process stability using appropriate methods.
• Identify recurring defects, unusual observations, and potential sources of variation.
• Produce reproducible analytical reports that support evidence-based quality decisions.

Who Should Attend

Learning Outcomes

• Import quality datasets from common file formats and organize them for analysis.
• Clean datasets by addressing missing values, duplicate records, inconsistent entries, and formatting issues.
• Use pandas and NumPy to perform calculations and manipulate quality data efficiently.
• Summarize quality performance using descriptive statistics and grouped analysis.
• Create charts to identify trends, outliers, defect patterns, and variation.
• Apply suitable statistical tests to compare quality results across groups or time periods.
• Calculate and interpret Cp and Cpk when the required statistical assumptions are satisfied.
• Generate and interpret control charts using appropriate process data and methods.
• Automate recurring quality calculations and analytical tasks using reusable Python scripts.
• Present findings through clear visualizations, analytical summaries, and actionable recommendations.

Course Outline