This course is designed to provide participants with a detailed and up-to-date overview of Data Analysis. It covers the role of data in operations and production; the data collection techniques, central tendency and variability; visualizing data using histograms, boxplots and scatterplots; the importance of data cleaning, handling missing values and detecting and managing outliers; the right tool for the task using Excel, R, Python and other analytical tools; the relationship of correlation and causation between variables; and the simple linear regression and multiple regression.
During this interactive course, participants will learn the time series analysis, quality control and statistical process control (SPC) and experimental designing and analysis of variance (ANOVA); the non-parametric statistics and inventory management analysis and supply chain analytics; the demand forecasting, optimization techniques and Lean and Six Sigma analytics; the KPIs for operational excellence and operational metrics for decision making; the machine learning basics and predictive maintenance, production scheduling with data, risk management and scenario analysis; the operational and production reporting and data driven decision making; the advanced topics like Big Data and IoT in production; and the future trends in data analysis for production.