Advanced Engineering Analytics

2 Days | 1.4 CEUs


Advanced Engineering Analytics is our latest class that builds upon our Introduction to Engineering Analytics offering. In this course, engineers, managers and analysts will explore how to improve upon their existing data-driven decision making skills, which can lead to better business results. More organizations are turning to analytics to improve their operations by identifying factors and levels that improve quality, predict and reduce equipment failures, enhance machine utilization, lowering production costs and improve customer service delivery.   

Advanced statistical methods and new data science concepts are introduced. Participants will gain hands-on experience by exploring larger datasets, using case studies and example problem demonstrations. 

Participants should bring a laptop running MS Windows with Excel and Access installed. SigmaXL or comparable statistical software is advised for data analysis. Open source MS Excel Add-ins, Analysis ToolPak and XLMiner Analysis ToolPak, will be installed in class and will address most of the course content. Note: Each participant is required to have administrator installation rights.


  • State the strategic implications of analytics from Big Data
  • Describe new data science concepts
  • Apply advanced techniques for data visualization, descriptive and predictive analytics
  • Effectively interpret and communicate the results of data analysis
  • Make better predictions



  • Business Impact of Big Data (Set Context, Big Data 1.0, Big Data 2.0)
  • Fast Track Review: Introduction of Engineering Analytics (High-level Concepts; No Exam)
  • The Data Mining Process (CRISP-DM)
  • Managing Your Data (Introduction to SQL, Merging Datasets)
  • Preparing Data for Analysis (Cleaning, Verification, Checking Assumptions, Data Types)
  • Exploratory Data Analysis and Visualization Techniques (Graphical & Quantitative)  
  • Descriptive Analytics (Identify missing and inaccurate data, summarization)
  • Analysis of Variance (ANOVA) (Three Assumptions-Normality, Case Independence, etc.)
  • Nonparametric Analysis: Introduction (Distribution-free Advantages, Drawbacks)
  • Design of Experiments: Introduction (2Factor-4Run Full Factorial, 2-Level Screening)
  • Predictive Analytics: Linear Regression (Verifying Assumptions, Prediction)
  • Evaluating a Linear Regression Model (Training/Holdout Data)
  • Predictive Analytics: Introduction to Logistic Regression (How to, Model Effectiveness)
  • Evaluating a Logistic Regression Model (Accuracy, Precision, True Positives/Negatives)
  • Case Study Illustration 



IISE reserves the right to cancel a class up to 15 business days prior to the scheduled start date

Registration Fee

Member: $795 Non-Member: $1,145

Course Schedule

Course ID: 2701 Nov 6 - 7, 2019 Norcross, GA Register