ENGINEERING DATA ANALYSIS - Intro Part 2 (Data Analysis for Solving Engineering Problems)

Part 2 focuses on applying statistical thinking to practical, data-intensive engineering problems. It begins with data acquisition, management, and preprocessing, highlighting the importance of data quality, structure, and visualization. Learners gain experience transforming raw data into reliable inputs for analysis, reflecting real-world engineering and industrial contexts. M5: Data Acquisition, Management, and Analysis This module covers the complete data workflow from acquisition to analysis in engineering contexts. Learners explore data representation, basic database concepts, and the overall analysis process. Emphasis is placed on data cleaning, preprocessing, and visualization as essential steps for producing reliable and meaningful insights. M6: Descriptive and Inferential Statistics in Excel This module applies statistical concepts using Excel as a practical analysis tool. Learners perform descriptive analysis, construct distributions, and conduct common inferential tests such as z-tests, t-tests, ANOVA, and chi-square tests. Correlation and regression analysis are also used to model and interpret engineering data effectively. M7: Structured, Data-Driven Problem Solving This module integrates analytics with systematic problem-solving approaches used in engineering and industry. Learners are introduced to Six Sigma fundamentals, analytical tools across problem-solving phases, and the integration of design thinking with data analysis. The focus is on translating data insights into justified, actionable engineering decisions. M8: Introduction to Data Analytics using Python This module introduces modern data analytics concepts using Python as a primary tool. Learners explore big data fundamentals, descriptive, predictive, and prescriptive analytics, and the role of artificial intelligence. Emphasis is placed on preparing data and applying analytics techniques that support machine learning and intelligent engineering systems.