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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), covered in this course, is 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.
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.
PRE-REQUISITES
Fundamental knowledge of linear algebra, probability, and statistics.
DELIVERY AND ORGANIZATION
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
Topics:
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
Instructor:
Sharan Srinivas, Ph.D.
Length:
16 weeks
Department:
Department of Industrial and Systems Engineering
Credit:
Non-credit | 4.5 Continuing Education Units or 45 Continuing Education Hours
Audience:
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.
