Business Analytics Lessons from an Undergraduate Course in Presbyterian College

Business analytics involves the methodical exploration of an organization's data through statistical analysis for data-driven decision-making. Teaching considerations include statistics, probability, algorithms, data visualization, machine learning, and more. The curriculum covers a range of topics from statistics to programming, emphasizing theory versus practice and includes hands-on projects and independent studies in data science.

• Business Analytics
• Data Analysis
• Statistics
• Machine Learning
• Data Science

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1. BUSINESS ANALYTICS L E S S O N S F R O M A N U N D E R G R A D U AT E I N T R O D U C T O R Y C O U R S E TOBIN TURNER PRESBYTERIAN COLLEGE

3. BUSINESS ANALYTICS BUSINESS ANALYTICS Business analytics is the practice of iterative, methodical exploration of an organization's data with emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision making.

4. BUSINESS ANALYTICS BUSINESS ANALYTICS Business analytics is the practice of iterative, methodical exploration of an organization's data with emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision making.

5. TEACHING CONSIDERATIONS Statistics, Probability, Math Statistics Algorithms Probability and Stochastic Process Principles of Database Systems Data Visualization Statistical Methods and Data Analysis Introduction to Optimization Statistical Models and Regression Computational Statistics Machine Learning Data Mining Game Theory

6. TEACHING CONSIDERATIONS Statistics, Probability, Math Statistics Algorithms Probability and Stochastic Process Principles of Database Systems Data Visualization Statistical Methods and Data Analysis Introduction to Optimization Statistical Models and Regression Computational Statistics Machine Learning Data Mining Game Theory Programming Depth vs Breadth Theory Vs. Practice r, python, - the good and the bad JMP The 0.32344*** Problem

7. TEACHING CONSIDERATIONS Statistics, Probability, Math Statistics Algorithms Probability and Stochastic Process Principles of Database Systems Data Visualization Statistical Methods and Data Analysis Introduction to Optimization Statistical Models and Regression Computational Statistics Machine Learning Data Mining Game Theory Programming Depth vs Breadth Theory Vs. Practice r, python, - the good and the bad JMP The 0.32344*** Problem Communication, Interpretation & Practice Capstone Project in Data Science Independent Study in Data Science Independent Study in Data Science

8. https://ep.jhu.edu/programs-and-courses/programs/data-sciencehttps://ep.jhu.edu/programs-and-courses/programs/data-science

9. TEACHING CONSIDERATIONS Statistics, Probability, Math Statistics Algorithms Probability and Stochastic Process Principles of Database Systems Data Visualization Statistical Methods and Data Analysis Introduction to Optimization Statistical Models and Regression Computational Statistics Machine Learning Data Mining Game Theory Programming Depth vs Breadth Theory Vs. Practice r, python, - the good and the bad JMP The 0.32344*** Problem Communication, Interpretation & Practice Capstone Project in Data Science Independent Study in Data Science Independent Study in Data Science

10. TEACHING CONSIDERATIONS Statistics, Probability, Math Statistics Algorithms Probability and Stochastic Process Principles of Database Systems Data Visualization Statistical Methods and Data Analysis Introduction to Optimization Statistical Models and Regression Computational Statistics Machine Learning Data Mining Game Theory Programming Depth vs Breadth Theory Vs. Practice r, python, - the good and the bad JMP The 0.32344*** Problem Communication, Interpretation & Practice Capstone Project in Data Science Independent Study in Data Science Independent Study in Data Science