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Advanced Analytics with Engineering Applications 2023FS is a Course

Advanced Analytics with Engineering Applications 2023FS

Aug 21, 2023 - Dec 15, 2023
4.5 CEUs

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Full course description

Course Overview:
In today’s digital age, all industries depend on data-driven analytical models to analyze historical trends/patterns, improve processes, predict future outcomes, optimize strategies, and uncover business intelligence. Business analytics (BA), a set of methods, tools, and approaches companies use, is one of the most in-demand skills in today’s workforce. It allows a company to gain a competitive advantage, minimize operational costs, and improve customer satisfaction. The demand for professionals with data analytics skillset remained strong even during the economic disruptions and workforce downsizing caused by the COVID-19 global pandemic. Besides, the BA-related job opportunities are expected to flourish, as the US Bureau of Labor Statistics estimates over 30% growth, one of the highest, during the next 10 years. Currently, there exists a shortage in the supply of professionals with the necessary analytics skills. This course introduces the core principles, methods, and tools associated with data analytics and provides hands-on training in using popular analytical tools (Python and R). The course covers advanced tools/techniques for data summarization, visualization, predictive modeling, association mining, clustering, and natural language processing. It is organized around the two foundational pillars of data analytics – descriptive analytics and predictive analytics. 
Fundamental knowledge of linear algebra, probability, and statistics.
100% online, Asynchronous format. The course is delivered through the Canvas LMS.  

Course Materials:
Due to a wide range of topics covered in this course, the materials have either been developed by the instructor or taken from multiple sources. Therefore, the students are not expected to purchase/follow one particular textbook. The materials provided by the instructor, namely, lecture notes, reading materials, PowerPoint slides, in-class exercises, homework, and case study, is more than sufficient to learn the topics and prepare for the assignments/exams. Students interested in learning more about the topics taught in class can read one of the following references (optional):
  • Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (Vol. 810). New York: Springer.
  • Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, pp. 241-249). New York: Springer series in statistics

Learning Outcomes:
Upon completing this course, students will be able to:
  • Understand the fundamentals of data analytics and its engineering applications 
  • Gain quantitative problem-solving skills applicable to any industry (e.g., healthcare, manufacturing, transportation/logistics, etc.)
  • Develop/deploy state-of-the-art analytical tools for optimizing operational costs, business process efficiency, and service quality
  • Derive data-driven business intelligence 

Overview of Data Analytics
  • Data types
  • Measurement Scales
  • Big Data Analytics
  • Analytics in decision making
  • Types of analytics
  • Tools for data analytics
Introduction to Python Programming
  • Install Python
  • Install Anaconda
  • Introduction to R
  • Install R Studio
Exploratory Analysis and Visualizations
  • Descriptive analytics
  • Best practices in analytics
  • Pivot tables and chart using Excel
  • Descriptive statistics
  • Visualizations using Excel
  • Important functions in Excel
  • Exploratory Analysis and Visualizations in R
  • Exploratory Analysis and Visualizations in Python
Predictive Analytics 
  • Introduction to machine learning
  • Evaluating ML algorithms
  • Steps in ML
Machine Learning Algorithms in Python
  • Naïve Bayes Classifier
  • Tree-based Classifiers
Machine Learning Algorithms in R
  • Logistic Regression
  • Random Forest
  • ANN
Market Basket Analysis
  • Association rule mining (ARM)
  • Real-life applications
  • Use cases
  • Terminology
  • Apriori principle
  • Implementation in R
Customer Segmentation using Clustering
  • Clustering overview
  • K-means clustering
  • Example Problems
  • Clustering using R
  • Dealing with Unstructured data
  • Natural Language Processing
  • Sentiment Analysis

Sharan Srinivas, Ph.D.

16 weeks

Department of Industrial and Systems Engineering

Non-credit | 4.5 Continuing Education Units or 45 Continuing Education Hours

Adult Learners

University of Missouri Extension complies with the Americans with Disabilities Act of 1990. If you have a disability and need accommodations in connection with participation in an educational program or you need materials in an alternate format, please notify your instructor as soon as possible so that necessary arrangements can be made.
Please direct payment questions and refund requests to MU Extension Customer Support. Access MU Extension’s Course Cancellation and Refund Policy for details.